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DEVELOPMENT OF A DECISION SUPPORT SYSTEM FOR MUNICIPAL SOLID WASTE MANAGEMENT SYSTEMS PLANNING
Waste Management & Research (1996) 14, 71–86
DEVELOPMENT OF A DECISION SUPPORT SYSTEM FOR MUNICIPAL SOLID WASTE MANAGEMENT SYSTEMS PLANNING K. D. Barlishen and B. W. Baetz Department of Civil Engineering, McMaster University, Hamilton, Ontario, Canada L85 4L7 (Received 24 January 1994, accepted in revised form 21 March 1995) A review of the municipal solid waste (MSW) management and planning literature reveals the growing number and complexity of the available mathematical models. A technical survey of practising waste management professionals indicated a general interest in, but lack of practical applications of, mathematical modelling techniques. The creation of knowledge-based systems to interface with individual MSW management and planning models, or assist with model selection and integration is proposed. As a means of demonstrating the validity of the suggested decision support approach, a prototype decision support system was developed to assist with the preliminary planning of MSW management systems. This planning tool combines knowledge-based system components with spreadsheet, optimization and stimulation models to assist with: waste forecasting; technology evaluation; recycling and composting programme design; facility sizing; location and investment timing; waste allocation; and MSW management system analysis using stimulation. A case study application of the planning tool is described. 1996 ISWA Key Words—Municipal solid waste, waste management, decision support systems.
1. Introduction In the past, communities have relied almost exclusively on landfills to dispose of the generated municipal solid waste (MSW) materials. Transfer stations were used if long distance hauling of waste was required. Due to the potential for environmental damage from landfill sites, the scarcity of land near urban centres and growing public opposition, there is a trend towards creating integrated MSW management systems, which rely on a combination of waste management approaches to minimize the dependence on landfills. Additional facilities may be considered: energy-from-waste facilities, centralized composting facilities, materials recovery facilities and mixed MSW processing facilities (which separate out and process a mixed MSW stream). The increasing number of options makes it more challenging for a waste management engineer or planner to decide on the combination of collection, processing and disposal systems that will best serve the present and future needs of a particular community. Systems analysis techniques may assist in developing long-range MSW management plans, which specify the type, size, location and investment timing for required facilities over a planning horizon of 20–30 years. Existing MSW management systems planning models range from simple, cost-estimation models to more complex models which select system technologies, facility capacities and facility locations to meet a set of constraints at minimum cost. 0734–242X/96/010071+16 $12.00/0
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Clark (1973) discusses regional planning models for solid waste management formulated as linear programs and fixed-charge problems modelled as mixed-integer linear programs. Fixed-charge problems include costs, such as site acquisition, that are incurred regardless of the level of activity at a site. Facility capital and operating costs are then represented for modelling purposes as a fixed cost and a variable cost (linearly dependent on facility capacity). Kuhner & Harrington (1975) use mixed-integer linear programming (MILP) techniques to solve a dynamic, multi-period investment model for regional solid waste management. Greenberg et al. (1976) apply linear programming (LP) techniques to an actual waste management systems planning study. Wilson (1977) reviews a comprehensive list of available waste management planning models. Jenkins (1979) investigates MSW management systems planning using an MILP formulation. Hasit & Warner (1981) describe WRAP (Waste Resource Allocation Program), which contains static and dynamic MILP models. Jenkins (1982) utilizes MILP techniques in a planning study for Toronto, Ontario. Chapman & Yakowitz (1984) describe a model which uses LP techniques to size and site facilities, and a cost accounting system to incorporate economies of scale and estimate the effects of decisions. Rushbrook (1987) describes the HARBINGER waste management planning model, developed by the Harwell Laboratory, and Wilson et al. (1984) show its application to waste management planning for Hong Kong. A recent effort relies on simulation to evaluate integrated MSW management systems (Lawver et al. 1990). Light (1990) describes six commercially available software packages for planning integrated solid waste management systems. In order to determine the extent of use of the available mathematical models, approximately 100 technical surveys were sent to local and regional public works departments and environmental consulting firms in North America. The results of the technical survey indicate that the majority of decision-making in the MSW management and planning area occurs without the assistance of available computer modelling tools (Barlishen 1993). This conclusion is consistent with the work of Rogers & Fiering (1986) that demonstrates a distinct lack of practical applications of optimization models by water resources management agencies, despite the proliferation of available models. However, there appears to be a demand for techniques and/or tools that facilitate the forecasting of waste generation and composition, that allow the comparison of waste management technologies, and that model integrated MSW management systems such that the impact of materials recovery programmes may be measured more readily. Knowledge-based system techniques may be used to provide advice and therefore an increased level of support in the MSW management and planning field (Thomas et al. 1990). Several characteristics of the problem domain of MSW management and planning make it suitable for the investigation of knowledge-based system techniques: due to the complexity and uncertainty involved, few individuals exist that would have expertise in all aspects of an integrated waste management system; judgment and expertise is required to make management and planning decisions; and many qualitative issues are involved. Waste management professionals may benefit from the permanent collection of expertise in the knowledge bases, and the provision of a framework for investigating problems. In addition, knowledge-based system components may allow the mathematical models to be more user-friendly and understandable, potentially extending thier usage as an aid to decision-making. To demonstrate the potential applicability of the suggested decision support approach, a microcomputer-based tool has been developed to support the preliminary long-range planning of MSW management systems at the regional or local level. This application
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relies on the integrated use of optimization, simulation and spreadsheet modelling, and knowledge-based system techniques. An attempt was made to collect, organize and encode waste management and mathematical modelling expertise related to the planning activities of waste forecasting, technology selection, composting and recycling programme design, and MSW management system design and analysis. The structure of this paper is as follows. The conceptual modelling approach is described in Section 2, and the specific aspects of the developed decision support system are discussed in Section 3. Issues relating to the verification and validation of the modelling approach are covered in Section 4. Preliminary MSW management systems planning for a case study community is presented in Section 5 as a means of demonstrating the use and capabilities of the planning tool. Concluding remarks are offered in Section 6. The knowledge-based system components of the planning tool were developed using version 2.1 of the VP-Expert expert system shell (Paperback Software 1989). Lotus 1-23 software was used for the development of the spreadsheet models (Lotus Development Corporation 1989). Fuller details of the research, including a description of the decision support system, are available by referring to Barlishen (1993).
2. Development of the conceptual model The long-range planning of MSW management systems was chosen as the prototype application area for its potential to demonstrate the benefits of combining the abilities of mathematical models and waste management expertise in a knowledge-based system framework to support decision-making. This waste management problem requires the integrated use of a series of modelling techniques to solve a full-scale problem, is of sufficient complexity, and is a field currently lacking in decision support. The long-range planning of MSW management systems relies on forecasted data for waste composition and generation over the desired planning horizon. The design of downstream transfer, processing and disposal facilities may be significantly affected by inaccuracies in these estimates. The next phase of strategic MSW management systems planning is to compile and assess information on various technological options for treating, processing and disposing of MSW. It is important at this stage in the planning process to consider, at least qualitatively, potential environmental and social impacts of implementing individual technologies, in addition to goals or policies established by the community. The result of the technology evaluation stage is the generation of several alternative MSW management programme and facility options for further consideration. The MSW management system options generally considered in this analysis of technologies are illustrated in Fig. 1. A decision support tool designed to support regional or local MSW management systems planning should therefore consider waste generation, waste reduction/re-use, recycling and composting programmes, and a range of treatment and disposal options. Once appropriate technologies are selected for consideration as additions to a current waste management system, a more rigorous analysis may be considered. This analysis is based on given information of the cost structures and operational aspects of the technologies as well as social, environmental, technical and political objectives that must be met. The literature offers a multitude of models designed to assist with the selection and location of waste facilities from a set of existing and potential sites based on the optimal allocation of generated wastes. Simulation may also be used to determine facility
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Waste reduction/re-use Solid waste generation
Mixed collection
Source separation
On-site composting
Drop-off depots
Separate collection
Materials recovery facility
Centralized composting facility M
M Mixed MSW processing facility
Transfer station
M Landfill
EFW facility M
Fig. 1. Flow diagram of an integrated MSW management system. Source: adapted from Lawver et al. (1990). Solid line, waste flow; dashed line, flow of processing residuals; dotted line, flow of recovered materials or energy; M, markets or end-uses.
capacities, to evaluate the cost and operation of a given waste management system, or to fine tune the results of an optimization analysis. With the results of an optimization and/or simulation analysis, the relative costs of the various alternative MSW management systems may be compared. Additionally, more accurate estimates of environmental, social and political factors may also be compared based on the locations, sizes and mixture of facilities suggested by the chosen model. This general problem-solving approach is also applicable to the long-range planning of composting systems or recycling systems that involve separate collection systems and processing facilities. The identical planning issues of facility sizing and location, facility type, facility investment timing, and materials allocation may be examined. Generally speaking, models designed for regional solid waste management systems planning have not been adapted for use in composting systems planning or recycling systems planning. A decision support tool, to be truly indicative of the strategic planning of MSW
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management systems, should guide the user through the major planning activities: the estimation of waste generation and composition data; the investigation of available waste management technologies; the design of composting and recycling programmes; and the estimation of operational and cost data for existing and potential waste management technologies in order to determine optimal facility sizing, timing and location, and waste allocation levels. To accomplish these tasks, the decision support tool could combine spreadsheet, optimization, simulation and knowledge-based system modelling techniques. The knowledge-based system environment would be used to provide advice on data development, model execution and output interpretation for the mathematical models. The application of this approach beyond programme and facility planning may potentially benefit other waste management and planning activities: environmental policy making, budgetary planning and funding applications. Potential users of the proposed decision support system would be consulting engineers, waste management engineers, recycling co-ordinators, municipal managers and decisionmakers. The structure of the decision support system should permit the knowledgebased system components to be clearly understood and thus easily modified, due to the constantly expanding expertise and economic, social and technological environments in this field. The majority of the waste management and mathematical modelling knowledge encoded has been taken from existing waste management texts, literature and reports. Thus, the elicitation of knowledge directly from experts was not considered in this research. The modelling approach assumes that information is available on the current waste management system. It is assumed that prior to consultation with the facility location/ waste allocation model, a preliminary site investigation for all proposed MSW management facilities has been completed. In addition, the decision support system assumes that a preliminary market analysis has been done with respect to the sale of recovered energy and materials, and that data are available regarding the number and types of residential dwellings, and industrial/commercial/institutional (ICI) activities in the community or region. However, due to the fact that local estimates may not always be available for specific parameters in the preliminary stages of a planning study, considerable emphasis was placed on compiling and encoding parameter values reported in the waste management literature. 3. Decision support system development The long-range MSW management systems planning procedure has been partitioned into the following major activities for discussion and modelling purposes: (1) (2) (3) (4) (5)
Waste generation and composition forecasting; Technology evaluation; Source separation composting and recycling programme design; Facility cost and operational data estimation; Facility location, sizing and investment timing, and waste allocation investigation; and (6) Simulation of an existing or proposed MSW management system. The general framework of the decision support system modelling components is presented in Fig. 2. Instructions on the use of the software packages and a description of the planning
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Step 1: Waste generation and composition forecasting
Introduction
Step 2: Technology evaluation
Design composting programmes?
Yes
Step 3: Source separation composting programme design
No Step 4: Source separation recycling programme design
Yes
Design recycling programmes? No Step 5: Introduction to modelling tools, facility-related data collection/estimation
Conduct optimization study? No Step 7: Simulation study — simulate operation of a MSW system
Yes
Yes
Step 6: Optimization study — model formulation — output analysis — sensitivity analysis — generation of near— optimal solutions
Conduct simulation study? No End
Fig. 2. Modelling component interaction in the proposed prototype decision support system.
support capabilities of the decision support system are provided by an initial knowledgebased system component. General data are then collected: economic parameters, including the prevailing interest rate; the current year; community characteristics, including population and the number of dwellings; and average waste material densities. The user is then guided through the development of waste generation and composition forecasts over an assumed 20-year planning horizon, using a series of knowledge-based system components and spreadsheet models. Waste data may be developed for the
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residential and industrial/commercial/institutional (ICI) sectors of a community. Waste analyses reported in the literature are supplied to the user to assist with the forecasting process. The user may then access introductory information stored in knowledge-based system components on the various MSW management technologies. Information is supplied on the advantages and disadvantages of alternative technologies which may be used to select technologies for consideration in the planning or expansion of a MSW management system. Assuming source separation composting and/or recycling programmes are to be included, the user may then be assisted in designing programmes through interaction with a number of knowledge bases and spreadsheet models. The user may then be provided with a description of several modelling tools that could be used to explore facility selection and location, facility investment timing, facility sizing, and waste allocation issues with respect to managing recyclables, compostables, processing residuals and/or the entire mixed MSW stream. To investigate facility-related planning issues using either spreadsheet models (which calculate costs for a given MSW management system and assumed waste allocation pattern) or an optimization modelling approach, certain cost and operational parameters are required for each of the technological options being examined or being considered for inclusion in a MSW management system. A set of knowledge bases and spreadsheet models have been developed to assist with the estimation of the required facility-related modelling parameters. For existing facilities, several cost and operational parameters are requested: the current capacity; the current operating cost and tipping fee; the fraction of the input stream that is recovered as materials or energy, and the revenue generated from the sale of the recovered materials or energy; the number of operating days per year; and the fraction of the design capacity utilized for normal operation. For potential facility sites, the user is assisted in estimating the necessary data required to explore facility planning issues using the spreadsheet or optimization modelling tools through the provision of data reported in the waste management literature. After cost and operational parameter estimates have been provided for all of the facilities that are being considered for a particular MSW management system planning problem, the user may then examine the individual spreadsheet models and perform a capital cost curve estimation procedure for any potential facility types. The spreadsheet models may also be used to conduct a cost analysis for a particular site, whether it is an existing or proposed (potential) facility site, based on waste allocation estimates to the site. Alternatively, the user may decide to utilize an optimization approach in the investigation of an existing or proposed MSW management system. The basis for the optimization study is a mixed-integer linear programming (MILP) dynamic, multi-period, model formulation for facility location, timing and sizing, and waste allocation. This allows the user the flexibility of developing an MILP or a linear programming (LP) model formulation for a facility planning problem. The general MILP facility planning model formulation described in the prototype decision support system is based on the previous work of several researchers (Kuhner & Heiler 1973; Kuhner & Harrington 1975; Jenkins 1979). Several extensions to the basic MILP MSW management systems planning model formulations suggested in the literature were required to make the approach more generally applicable to current facility planning, as the previous models focused on the transportation, processing and disposal of mixed MSW only. Knowledge bases were developed to assist in the formulation of the
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optimization model, as well as analysing the model outputs and conducting postoptimality sensitivity tests. The user is also provided with advice on methods available to generate near-optimal solutions for a previously optimized MSW management system planning problem through consultation with a knowledge base based on the previous work of several researchers (Brill 1979; Chang et al. 1982; Harrington & Gidley 1985). The techniques require adjusting the original model formulation to generate solutions that are different with respect to facility development, facility usage and waste allocations patterns, but that have costs within a specified range of the optimal (least-cost) solution. Alternative systems may then be compared on the basis of costs, in addition to the consideration of unmodelled social, political, environmental and technical considerations. The final option in the programme and facility planning decision support system is the examination of the effect of data uncertainty and temporal variation in waste generation on the overall economics and efficiency of a MSW management system, to provide another means of evaluating and comparing alternative. A discrete, deterministic simulation model has been developed in FORTRAN to provide a simulated time history of the costs and waste allocation for a given MSW management system over a given time period. The simulation model can utilize either a forecasted series of waste or source separated material flows, or a synthetic waste series generated from the stochastic variation exhibited by a historical waste series, for each of the waste source areas in the study region. The user is assisted in developing model inputs and analysing the outputs of the simulation model through interaction with a knowledge base and accompanying documentation.
4. Verification and validation of the decision support system Model verification involves ensuring the mathematical equations and the solution procedure are correctly represented, and that the model correctly implements its specifications (O’Keefe et al. 1987). Model validation is concerned with substantiating that a model is sufficiently accurate for the intended use. In addition to its representation of the behaviour of the real system, a mathematical model’s validity must also be judged based on its usefulness, useability and cost (Landry et al. 1983; O’Keefe et al. 1987). The spreadsheet models created to assist with the estimation of facility costs were based on the costing methodology developed by Smith (1989), and the basic model parameter requirements of the MILP optimization model. Hand calculations and comparisons to reported values were the techniques used to verify the mathematical equations. In order to verify the mathematical formulation for the optimization model to assist with facility planning, test case studies were conducted based on the inputs and model outputs for a hypothetical problem and for a case study application to ensure the completeness and corectness of the formulation. The simulation model was verified by comparing model outputs to the optimization output for a test case application. The approach of utilizing linear programming (LP) or mixed-integer linear programming (MILP) techniques to model MSW management systems planning problems has been demonstrated through the previous work of numerous researchers including Kuhner & Harrington (1975), Greenberg et al. (1976), and Jenkins (1979). Sensitivity analysis and simulation have been proposed as additional means by which a planning
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model may be validated through the examination of model parameter uncertainty (Brill 1979; Lund 1990). The validity of optimization approaches may be enhanced by utilizing techniques to generate alternative and near-optimal solutions (Kuhner & Harrington 1975; Rogers & Fiering 1986). The user of the decision support system is assisted in conducting sensitivity analyses to examine the effects of uncertainty. Assistance is also provided on the generation of near-optimal solutions to allow alternative systems with costs near that of the optimal (least-cost) system to be examined and compared. A simulation model has also been developed as an additional means of evaluating alternative MSW management systems. The development of knowledge-based system components provides additional means to ensure that the developed models are sufficiently accurate for their intended uses, as the user is provided with a collection of values reported in the waste management literature. Verification of these components was a fairly straightforward task given the use of a rule-based representation of knowledge, and their limited problem scope and size. The knowledge bases are restricted to dealing with one type of programme, facility or modelling stage, and generally contain approximately 10–50 rules. The logical correctness of the various knowledge bases was verified primarily by exhaustive testing of rule sets. In addition, the system was designed to provide advice as opposed to making decisions, and the knowledge encoded in the system was derived from literature sources as opposed to human experts. The validation methods applied in the development stage should also be feasible for long-term maintenance of the knowledge-based system components of the decision support system, providing the problem scope and size of the rule bases remain relatively small. The validity of a model must also be concerned with the issues of usefulness, useability and cost if it is intended as a means of assisting and improving decision-making for a public sector planning problem (Clapham 1987). The need for a set of modelling tools to assist with the development, evaluation and elaboration of alternative solutions to public sector planning problems has been suggested by Brill (1979). This approach to providing decision support to waste management engineers and planners has also been supported by numerous other researchers and waste management professionals (Clark & Gillean 1981; Wilson 1981; Light 1990). Fiksel & Hayes-Roth (1989) support the application of knowledge-based system techniques to planning activities in general, if they are designed to provide advice as opposed to recommend courses of action. Several knowledgeable waste management professionals were consulted to determine the reasonableness, accuracy and usefulness of the prototype decision support system for long-range MSW management systems planning. The organization of the prototype decision support system was judged to be logical and reasonably accurate, and the input parameter requirements were deemed reasonable. The usefulness and userfriendliness of the mathematical models was enhanced by the addition of the knowledgebased system components. The prototype decision support system was noted for its ability to provide waste managers with the tools to develop and refine estimates of programme and facility costs. The prototype planning tool was also recognized for its benefits in permitting scenario analysis and facilitating comparisons with systems operated by other communities. The projected cost of the system software (approximately U.S. $500, assuming spreadsheet software is available) was viewed as reasonable. The prototype planning tool was considered to be easy to use with its menus and interactive data input. Additional development work is desired before the system is used in actual planning situations.
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The prototype decision support system is designed to illustrate the benefits of combining the capabilities of knowledge-based system techniques and mathematical modelling tools to provide a more flexible and powerful environment in which to conduct preliminary long-range planning studies. Communities conducting preliminary planning of their MSW management systems may capitalize on the experience reported by others and avoid problems with lengthy or costly investigations. The planning tool supports the use of optimization models to investigate alternative MSW management systems as it provides the user with an indication of near-optimal systems for proposed scenarios, and to evaluate systems as it provides the user with advice on the use of sensitivity analysis. The simulation component permits waste managers to examine the impacts of: different waste generation growth patterns; community waste reduction, reuse and recycling impacts or goals; or, fluctuating material and energy markets (Lawver et al. 1990). The modelling approaches allow the examination of the modelling assumptions and data uncertainty. Thus, the developed system is considered to be both useable and useful, and a potentially valid approach to providing support for MSW management systems planning.
5. Application of the decision support system In order to demonstrate the application of the prototype decisions support system to facility planning studies, a case study was conducted based on the current recycling situation in the community of Winnipeg, Manitoba, Canada. A preliminary study of the recyclable materials management system is described. Winnipeg is the capital city of Manitoba, and is located at the junction of the Red and Assiniboine Rivers in southern Manitoba. The 1991 census (Statistics Canada 1992) reported a population of 616,790 for the city of Winnipeg. Although the population of Winnipeg has experienced sharp increases and decreases in the late 1970s and early 1980s, respectively, the population is showing signs of stabilization. Based on the results of a recent study by the Manitoba Bureau of Statistics (1989), a population growth rate of 0.7% per year was assumed over the 20-year planning study time horizon of 1991–2011. Currently, Winnipeg does not operate any incineration facilities or transfer stations. Consistent with many communities in Western Canada, Winnipeg relies almost exclusively on the use of landfills for disposing of its MSW. Following a 1982 landfill siting study, a 40-year landfill site was proposed to replace the existing facility. A dropoff programme for centralized leaf and garden waste composting has recently been initiated. Composting takes place at a landfill site using minimal technology, windrow composting that requires 2–3 years for processing. Recycling activities are primarily privately operated, and include a combination of drop-off depot collection and curbside collection programmes. The City of Winnipeg recently provided funding for the establishment of additional drop-off depots that would be used to collect materials for processing at an existing privately-operated materials recovery facility (MRF). A planning study was conducted to investigate processing facility requirements if the City of Winnipeg were to instigate a widespread, municipally-operated or municipally-funded drop-off recycling programme or curbside recycling programme. Information regarding current waste management practise was obtained from the City of Winnipeg. The waste generation and composition forecasting models of the planning tool were
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TABLE 1 Summary of the proposed drop-off depot recycling programme parameters Source separation recycling
Depot collection to service residential sector
Participation rate Start-up cost Total waste diversion Annual cost Annual cost per tonne of waste diverted Optimal present value system cost over the 20-year planning period
25% $1.5 million 10,000–14,000 tonnes year−1 $500,000–$700,000 year−1 $50 tonne−1 $6.3 million
TABLE 2 Summary of the proposed curbside recycling programme parameters Source separation recycling
Weekly programme to service residential sector (except large apartment complexes)
Participation rate Start-up cost Total waste diversion Annual cost Annual cost per tonne of waste diverted Optimal present value system cost over the 20-year planning period
85% $4.0 million 9000–48,000 tonnes year−1 $1 million–$3 million year−1 $100 (start-up)–$70 (Year 20) tonne−1 $43.2 million
first used to develop estimates for the residential sector over a 20-year planning horizon. In the first year of the case study, the single-family dwellings were forecasted to generate 257,000 tonnes of waste, while the multiple-family dwellings were forecasted to generate 57,000 tonnes of waste. The basic data required with respect to population and dwelling types within the city of Winnipeg were obtained from several sources (Manitoba Bureau of Statistics 1989; Statistics Canada 1992). The source separation recycling programme design components of the decision support system were utilized to design two alternative voluntary recycling programmes to service the residential sectors; one programme was designed based on the use of drop-off depots, and the other programme was designed based on the curbside collection of recyclables. This estimation process was required in order to forecast the total material tonnages that would require processing within a system of MRFs. The programmes were assumed to expand over time to include more materials and to include more residences due to population growth. The curbside collection programme was assumed to start with a pilot-scale programme. A summary of the main programme parameters for the drop-off and curbside collection alternatives are summarized in Tables 1 & 2, respectively. The ranges listed for several of the programme parameters indicate their values in the start-up year and final year (Year 20) of the planning period. The material recovery levels predicted for the drop-off recycling programme were assumed to be managed using three existing processing facilities. Due to the higher forecasted material recovery levels for the curbside collection programme, the development of at least one additional facility would be required, as no expansions were
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assumed possible at the existing MRFs. Three potential facility locations were assumed, with two of the potential sites being located in proximity to the current landfill sites. The city of Winnipeg was subdivided into 12 waste generation source areas based on the 1991 census tracts (Statistics Canada 1992). The total forecasted recyclable material recovery levels were allocated to the waste source areas based on relative populations and expected growth rates. An optimization study was conducted for the forecasted material recovery levels for the drop-off and curbside collection options. The MILP solver package MILP88 (Eastern Software Products 1988) was used for the case study. Waste allocation and facility selection/location issues were investigated in consultation with the facility planning components of the prototype decision support system. As the materials recovered through the drop-off programme over the 20-year planning horizon were assumed to be managed using three existing MRFs, this systems planning problem reduced to an optimization of flow allocation and required only a linear programming model formulation. Material flow values were developed for each of the 12 waste source areas for each year of the 20-year planning horizon. Estimates for the collection cost and the expected revenue from the sale of recovered materials associated with flows of recyclable materials, applicable to all the MRFs, were developed using the results from the source separation programme design stage. Facility operating costs were approximated using information in the facility-related data estimation knowledge base developed for MRFs. Transportation costs were estimated through consultation with a knowledge base; transportation distances between facility sites and waste source areas were estimated from a City of Winnipeg map. The remaining model parameters (the residuals disposal cost and facility capacities) were assumed based partially on information provided by several waste management professionals involved in the operation of the current MSW management system. The model formulation for the optimization study of the drop-off recycling programme processing system was developed with the assistance of the optimization modelling components of the prototype decision support system. The solution package also provided reports summarizing the post-optimality sensitivity analysis information. The optimal present value system cost was determined to be $6.3 million which includes the costs of collecting, transporting, and processing the materials. In the optimal solution, one facility is allocated materials such that it continually operates at full capacity. As material flows gradually exceed the available capacity of this facility, flows are assigned to the other two facilities. These results may be explained due to the difference in operating costs assumed for the facilities and haul transportation distances, as all other cost parameters were identical. The investigation of a processing system for the forecasted flows of recyclable materials from the proposed curbside recycling programme was also conducted. In this case, only one of the existing facilities was included in the system (as the other two facilities have very limited processing capabilities), along with three proposed sites for new MRFs. Potential facility capital costs were estimated using a fixed and a variable cost; capacity development and expansions were limited to discrete units of capacity. Thus, a mixed integer linear programming model formulation was required. The remaining model parameters were estimated in a similar manner to the drop-off programme processing system problem previously described. The 20-year time horizon was modelled in five, 4-year time periods. The optimal present value system cost was determined to be $43.2 million which
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includes the costs of facility construction and expansion, and the costs of collecting, transporting and processing the recyclable materials. In addition to requiring the full use of the capacity of the existing facility, the solution calls for: the development of a facility in Period one at 50 tonnes day−1 and a subsequent expansion of this facility by 50 tonnes day−1 in the second time period; and the development of the other two potential facilities in the second time period at 50 tonnes day−1 each. The available capacities of two of the potential facilities are fully utilized, and the capacity of the other potential facility is gradually utilized over time. Using the methods for generating near-optimal solutions described in several water resources research papers (Brill 1979; Chang et al. 1982; Harrington & Gidley 1985) and summarized in a knowledge base, several alternative solutions were produced with total system costs within 5% of the optimal (least-cost) solution. Three near-optimal solutions were generated which involved varying facility location, sizing and timing of investment patterns, and waste allocations. The existence of near-optimal systems indicated the general flexibility of the assumed system of facilities for managing the recyclables collected through a curbside programme. As maximum capacity limitations were not imposed on the sites due to the availability of land, one near-optimal solution required the development and expansion of only one of the proposed facility sites. The near-optimal systems solutions also provided excess capacity within the system as there was no direct force for the reformulated model to produce minimal cost alternatives. Conducting a simulation study of the optimal system for the same time periods as the optimal system provided a means of verifying the optimization modelling approach. The total present value system cost calculated by the simulation model was within 0.1% of the optimal solution. The simulation model was also used to evaluate the proposed optimal recyclable materials management system for the curbside collection programme under yearly time increments. This allowed the examination of the variability in total material flows and system costs within the optimization time period increments. The total present value system cost based on an annual analysis of the system was calculated as $39.2 million. The difference in the total present value system costs calculated through the two mathematical models may be attributed primarily to the effects of discounting on a yearly basis in the simulation model. The simulation study was conducted using the FORTRAN simulation model with the assistance of the accompanying knowledge base. The case study results were reviewed by a City of Winnipeg waste management engineer, and were found to compare favourably to previously determined estimates for a recyclable materials management system. This case study has demonstrated the extensive and flexible nature of the facility planning components of the prototype decision support system. As well as assisting with the investigation of facility location, sizing, development and expansion issues, the optimization and simulation modelling components may be used to generate alternative solutions to a planning problem and perform sensitivity analyses.
6. Concluding remarks An extensive literature review identified numerous mathematical models developed for MSW management and planning. The emphasis of these previous studies has been on model formulation as opposed to issues such as useability and data requirements. A technical survey indicated that the available mathematical modelling techniques are
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not widely utilized in practise, particularly in municipal or regional waste management agencies. Waste management engineers and planners require additional tools to assist in the development and evaluation of integrated MSW management systems, particularly as the knowledge and technological options in this field continue to expand. There is both a need for, and an interest in, a useful, practical (with respect to data requirements and cost) and reasonably objective form of decision support system for use at the municipal and regional MSW management decision-making levels. Decision support systems based on the use of knowledge-based system techniques could be developed to interface with individual models, assist with model selection, or manage a series of models required to investigate a MSW management or planning activity. These suggested decision support approaches may extend and support the use of mathematical modelling techniques in practise, while allowing end-users to make decisions using their own skill and judgment. This paper reports on research that attempted to address what numerous studies and inquiries have identified as the most significant deterrent to implementing MSW management projects: the perceived complexity of the planning process. A prototype decision support system has been designed to identify the activities and decisions required to conduct preliminary planning studies for MSW management systems. The prototype decision support system contains components related to the major planning activities: waste forecasting; technology evaluation; source separation programme design; facility sizing, location and investment timing; waste allocation; and MSW management systems analysis using simulation. Based on the opinions expressed by several practising waste management professionals, the integration of knowledge-based systems and mathematical models appears to be a suitable method of providing decisionmaking support to waste management professionals. The use of the system also yielded useful and reasonable results for a case study application. Further research is required to identify perceived deficiencies in the existing mathematical models used within waste management and planning agencies. Due to the constantly changing decision-making environment in MSW management systems planning, future decision support systems should be designed to be modular, with rulebased knowledge representation to facilitate end-user understanding and the updating of the expertise encoded in the system. Acknowledgements This paper is based on the Ph.D. dissertation submitted to McMaster University by Kimberly D. Barlishen. This research was supported by the Natural Sciences and Engineering Research Council of Canada and McMaster University. The assistance provided by Dr. Gilles Patry and Dr. Norman Archer is gratefully acknowledged. References Barlishen, K. D. (1993) Decision Support for Municipal Solid Waste Management and Planning. Unpublished Ph.D. Dissertation, Department of Civil Engineering, McMaster University, Hamilton, Ontario, Canada. Brill, E. D., Jr. (1979) The use of optimization models in public-sector planning. Management Science 25, 413–422. Chang, S-Y., Brill, E. D. Jr. & Hopkins, L. D. (1982) Use of mathematical models to generate alternative solutions to water resources planning problems. Water Resources Research 18, 58–64.
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Chapman, R. E., & Yakowitz, H. (1984) Evaluating the risks of solid waste management programs: A suggested approach. Resources and Conservation 11, 77–94. Clapham, W. B., Jr. (1987) RECYCLE: A computerized planning tool to improve municipal solid waste management. Ohio Journal of Science 86, 189–199. Clark, R. M. (1973) Solid waste: management and models. In Models for Environmental Pollution Control (R. A. Deininger, ed.). Ann Arbor, Michigan; U.S.A.: Ann Arbor Science Publishers, Inc., p. 269–305. Clark, R. M. & Gillean, J. I. (1981) Resource Recovery Planning and Management. Ann Arbor, Michigan, U.S.A.: Science Publishers, Inc. Eastern Software Products (1988) Mixed-Integer Linear Programming for the IBM PC. MILP88, Version 7.11: User’s Manual. Alexandria, Virginia; U.S.A.: Eastern Software Products, Inc. Fiksel, J. & Hayes-Roth, F. (1989) Knowledge systems for planning support. IEEE Expert 4, 16–23. Greenberg, M., Caruana, J. & Krugman, B. (1976) Solid-waste management: A test of alternative strategies using optimization techniques. Environment and Planning A8, 587–597. Harrington, J. J. & Gidley, J. S. (1985) The variability of alternative decisions in a water resources planning problem. Water Resources Research 21, 1831–1840. Hasit, Y. & Warner, D. B. (1981) Regional solid waste planning with WRAP. Journal of the Environmental Engineering Division (ASCE) 107, 511–526. Jenkins, A. L. (1979) Optimal Location of Facilities for Recycling Municipal Solid Waste in Southern Ontario. Unpublished Ph.D. Dissertation, University of Toronto, Toronto, Ontario, Canada. Jenkins, L. (1982) Developing a solid waste management model for Toronto. INFOR 20, 237–247. Kuhner, J. & Harrington, J. J. (1975) Mathematical models for developing regional solid waste management policies. Engineering Optimization 1, 237–256. Kuhner, J. & Heiler, B. (1973) Regional planning models for solid waste management. In Models for Environmental Pollution Control (R. A. Deininger, ed.). Ann Arbor, Michigan, U.S.A.: Ann Arbor Science Publishers, Inc., 327–357. Landry, M., Malouin, J.-L. & Oral, M. (1983) Model validation in operations research. European Journal of Operational Research 14, 207–220. Lawver, R., Lund, J. R. & Tchobanoglous, G. (1990) GIGO – A solid waste management model for municipalities. In Proceedings of the Sixth International Conference on Solid Waste Management and Technology. Philadelphia, Pennsylvania, U.S.A., Paper No. 15, 8p. Light, G. L. (1990) Microcomputer software in municipal solid waste management: A review of programs and issues for developing countries. Water and Sanitation Paper Series (DP No.6), UNDP-World Bank Water and Sanitation Program, Washington, D.C., U.S.A. Lotus Development Corporation (1989) Lotus 1-2-3, Release 3. Lotus Development Corporation, Cambridge, Massachusetts, U.S.A.: Lotus Development Corporation. Lund, J. R. (1990) Least-cost scheduling of solid waste recycling. Journal of Environmental Engineering (ASCE) 116, 182–197. Manitoba Bureau of Statistics (MBS) (1989) Manitoba Population Projections: 1988–2011. MBS 88-1. Manitoba, Canada. O’Keefe, R. M., Balci, O. & Smith, E. P. (1987) Validating expert system performance. IEEE Expert 2, 81–89. Paperback Software (1989) VP-Expert: Rule-Based Expert System Development Tool. Berkeley, California, U.S.A.: Paperback Software International. Rogers, P. P. & Fiering, M. B. (1986) Use of systems analysis in water management. Water Resources Research 22, 146S–158S. Rushbrook, P. (1987) The benefits of forward planning and the role of computer assistance. Presented at the HARBINGER Symposium, Llandrindod Wells, U.K., June 2, 1987. Smith, D. G. (1989) A Comprehensive Costing Methodology for the Assessment of Municipal Solid Waste Management Alternatives. Unpublished Master of Engineering Project Report, McMaster University, Hamilton, Ontario, Canada. Statistics Canada (1992) Profile of Census Tracts in Winnipeg – Part A. Ottawa, Canada: Industry, Science and Technology Canada, 1992. 1991 Census of Canada. Catalogue No. 95-360. Thomas, B., Tamblyn, D. & Baetz, B. W. (1990) Expert systems in municipal solid waste management planning. Journal of Urban Planning and Development (ASCE) 116, 150–155. Wilson, D. C. (1977) Strategy evaluation in planning of waste management to land – a critical review of the literature. Applied Mathematical Modelling 1, 205–217.
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Wilson, D. C. (1981) Waste Management: Planning, Evaluation, Technologies. New York, U.S.A.: Oxford University Press. Wilson, D. C., Pugh, M. P., Bradley, V. H. & Hoare, R. W. M. (1984) The Harwell waste management model and its development in Hong Kong. Presented at the International Solid Wastes and Public Cleansing Association Congress, Philadelphia, Pennsylvania, U.S.A., September 15–20, 1984.
DEVELOPMENT OF A DECISION SUPPORT SYSTEM FOR MUNICIPAL SOLID WASTE MANAGEMENT SYSTEMS PLANNING K. D. Barlishen and B. W. Baetz Department of Civil Engineering, McMaster University, Hamilton, Ontario, Canada L85 4L7 (Received 24 January 1994, accepted in revised form 21 March 1995) A review of the municipal solid waste (MSW) management and planning literature reveals the growing number and complexity of the available mathematical models. A technical survey of practising waste management professionals indicated a general interest in, but lack of practical applications of, mathematical modelling techniques. The creation of knowledge-based systems to interface with individual MSW management and planning models, or assist with model selection and integration is proposed. As a means of demonstrating the validity of the suggested decision support approach, a prototype decision support system was developed to assist with the preliminary planning of MSW management systems. This planning tool combines knowledge-based system components with spreadsheet, optimization and stimulation models to assist with: waste forecasting; technology evaluation; recycling and composting programme design; facility sizing; location and investment timing; waste allocation; and MSW management system analysis using stimulation. A case study application of the planning tool is described. 1996 ISWA Key Words—Municipal solid waste, waste management, decision support systems.
1. Introduction In the past, communities have relied almost exclusively on landfills to dispose of the generated municipal solid waste (MSW) materials. Transfer stations were used if long distance hauling of waste was required. Due to the potential for environmental damage from landfill sites, the scarcity of land near urban centres and growing public opposition, there is a trend towards creating integrated MSW management systems, which rely on a combination of waste management approaches to minimize the dependence on landfills. Additional facilities may be considered: energy-from-waste facilities, centralized composting facilities, materials recovery facilities and mixed MSW processing facilities (which separate out and process a mixed MSW stream). The increasing number of options makes it more challenging for a waste management engineer or planner to decide on the combination of collection, processing and disposal systems that will best serve the present and future needs of a particular community. Systems analysis techniques may assist in developing long-range MSW management plans, which specify the type, size, location and investment timing for required facilities over a planning horizon of 20–30 years. Existing MSW management systems planning models range from simple, cost-estimation models to more complex models which select system technologies, facility capacities and facility locations to meet a set of constraints at minimum cost. 0734–242X/96/010071+16 $12.00/0
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Clark (1973) discusses regional planning models for solid waste management formulated as linear programs and fixed-charge problems modelled as mixed-integer linear programs. Fixed-charge problems include costs, such as site acquisition, that are incurred regardless of the level of activity at a site. Facility capital and operating costs are then represented for modelling purposes as a fixed cost and a variable cost (linearly dependent on facility capacity). Kuhner & Harrington (1975) use mixed-integer linear programming (MILP) techniques to solve a dynamic, multi-period investment model for regional solid waste management. Greenberg et al. (1976) apply linear programming (LP) techniques to an actual waste management systems planning study. Wilson (1977) reviews a comprehensive list of available waste management planning models. Jenkins (1979) investigates MSW management systems planning using an MILP formulation. Hasit & Warner (1981) describe WRAP (Waste Resource Allocation Program), which contains static and dynamic MILP models. Jenkins (1982) utilizes MILP techniques in a planning study for Toronto, Ontario. Chapman & Yakowitz (1984) describe a model which uses LP techniques to size and site facilities, and a cost accounting system to incorporate economies of scale and estimate the effects of decisions. Rushbrook (1987) describes the HARBINGER waste management planning model, developed by the Harwell Laboratory, and Wilson et al. (1984) show its application to waste management planning for Hong Kong. A recent effort relies on simulation to evaluate integrated MSW management systems (Lawver et al. 1990). Light (1990) describes six commercially available software packages for planning integrated solid waste management systems. In order to determine the extent of use of the available mathematical models, approximately 100 technical surveys were sent to local and regional public works departments and environmental consulting firms in North America. The results of the technical survey indicate that the majority of decision-making in the MSW management and planning area occurs without the assistance of available computer modelling tools (Barlishen 1993). This conclusion is consistent with the work of Rogers & Fiering (1986) that demonstrates a distinct lack of practical applications of optimization models by water resources management agencies, despite the proliferation of available models. However, there appears to be a demand for techniques and/or tools that facilitate the forecasting of waste generation and composition, that allow the comparison of waste management technologies, and that model integrated MSW management systems such that the impact of materials recovery programmes may be measured more readily. Knowledge-based system techniques may be used to provide advice and therefore an increased level of support in the MSW management and planning field (Thomas et al. 1990). Several characteristics of the problem domain of MSW management and planning make it suitable for the investigation of knowledge-based system techniques: due to the complexity and uncertainty involved, few individuals exist that would have expertise in all aspects of an integrated waste management system; judgment and expertise is required to make management and planning decisions; and many qualitative issues are involved. Waste management professionals may benefit from the permanent collection of expertise in the knowledge bases, and the provision of a framework for investigating problems. In addition, knowledge-based system components may allow the mathematical models to be more user-friendly and understandable, potentially extending thier usage as an aid to decision-making. To demonstrate the potential applicability of the suggested decision support approach, a microcomputer-based tool has been developed to support the preliminary long-range planning of MSW management systems at the regional or local level. This application
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relies on the integrated use of optimization, simulation and spreadsheet modelling, and knowledge-based system techniques. An attempt was made to collect, organize and encode waste management and mathematical modelling expertise related to the planning activities of waste forecasting, technology selection, composting and recycling programme design, and MSW management system design and analysis. The structure of this paper is as follows. The conceptual modelling approach is described in Section 2, and the specific aspects of the developed decision support system are discussed in Section 3. Issues relating to the verification and validation of the modelling approach are covered in Section 4. Preliminary MSW management systems planning for a case study community is presented in Section 5 as a means of demonstrating the use and capabilities of the planning tool. Concluding remarks are offered in Section 6. The knowledge-based system components of the planning tool were developed using version 2.1 of the VP-Expert expert system shell (Paperback Software 1989). Lotus 1-23 software was used for the development of the spreadsheet models (Lotus Development Corporation 1989). Fuller details of the research, including a description of the decision support system, are available by referring to Barlishen (1993).
2. Development of the conceptual model The long-range planning of MSW management systems was chosen as the prototype application area for its potential to demonstrate the benefits of combining the abilities of mathematical models and waste management expertise in a knowledge-based system framework to support decision-making. This waste management problem requires the integrated use of a series of modelling techniques to solve a full-scale problem, is of sufficient complexity, and is a field currently lacking in decision support. The long-range planning of MSW management systems relies on forecasted data for waste composition and generation over the desired planning horizon. The design of downstream transfer, processing and disposal facilities may be significantly affected by inaccuracies in these estimates. The next phase of strategic MSW management systems planning is to compile and assess information on various technological options for treating, processing and disposing of MSW. It is important at this stage in the planning process to consider, at least qualitatively, potential environmental and social impacts of implementing individual technologies, in addition to goals or policies established by the community. The result of the technology evaluation stage is the generation of several alternative MSW management programme and facility options for further consideration. The MSW management system options generally considered in this analysis of technologies are illustrated in Fig. 1. A decision support tool designed to support regional or local MSW management systems planning should therefore consider waste generation, waste reduction/re-use, recycling and composting programmes, and a range of treatment and disposal options. Once appropriate technologies are selected for consideration as additions to a current waste management system, a more rigorous analysis may be considered. This analysis is based on given information of the cost structures and operational aspects of the technologies as well as social, environmental, technical and political objectives that must be met. The literature offers a multitude of models designed to assist with the selection and location of waste facilities from a set of existing and potential sites based on the optimal allocation of generated wastes. Simulation may also be used to determine facility
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Waste reduction/re-use Solid waste generation
Mixed collection
Source separation
On-site composting
Drop-off depots
Separate collection
Materials recovery facility
Centralized composting facility M
M Mixed MSW processing facility
Transfer station
M Landfill
EFW facility M
Fig. 1. Flow diagram of an integrated MSW management system. Source: adapted from Lawver et al. (1990). Solid line, waste flow; dashed line, flow of processing residuals; dotted line, flow of recovered materials or energy; M, markets or end-uses.
capacities, to evaluate the cost and operation of a given waste management system, or to fine tune the results of an optimization analysis. With the results of an optimization and/or simulation analysis, the relative costs of the various alternative MSW management systems may be compared. Additionally, more accurate estimates of environmental, social and political factors may also be compared based on the locations, sizes and mixture of facilities suggested by the chosen model. This general problem-solving approach is also applicable to the long-range planning of composting systems or recycling systems that involve separate collection systems and processing facilities. The identical planning issues of facility sizing and location, facility type, facility investment timing, and materials allocation may be examined. Generally speaking, models designed for regional solid waste management systems planning have not been adapted for use in composting systems planning or recycling systems planning. A decision support tool, to be truly indicative of the strategic planning of MSW
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management systems, should guide the user through the major planning activities: the estimation of waste generation and composition data; the investigation of available waste management technologies; the design of composting and recycling programmes; and the estimation of operational and cost data for existing and potential waste management technologies in order to determine optimal facility sizing, timing and location, and waste allocation levels. To accomplish these tasks, the decision support tool could combine spreadsheet, optimization, simulation and knowledge-based system modelling techniques. The knowledge-based system environment would be used to provide advice on data development, model execution and output interpretation for the mathematical models. The application of this approach beyond programme and facility planning may potentially benefit other waste management and planning activities: environmental policy making, budgetary planning and funding applications. Potential users of the proposed decision support system would be consulting engineers, waste management engineers, recycling co-ordinators, municipal managers and decisionmakers. The structure of the decision support system should permit the knowledgebased system components to be clearly understood and thus easily modified, due to the constantly expanding expertise and economic, social and technological environments in this field. The majority of the waste management and mathematical modelling knowledge encoded has been taken from existing waste management texts, literature and reports. Thus, the elicitation of knowledge directly from experts was not considered in this research. The modelling approach assumes that information is available on the current waste management system. It is assumed that prior to consultation with the facility location/ waste allocation model, a preliminary site investigation for all proposed MSW management facilities has been completed. In addition, the decision support system assumes that a preliminary market analysis has been done with respect to the sale of recovered energy and materials, and that data are available regarding the number and types of residential dwellings, and industrial/commercial/institutional (ICI) activities in the community or region. However, due to the fact that local estimates may not always be available for specific parameters in the preliminary stages of a planning study, considerable emphasis was placed on compiling and encoding parameter values reported in the waste management literature. 3. Decision support system development The long-range MSW management systems planning procedure has been partitioned into the following major activities for discussion and modelling purposes: (1) (2) (3) (4) (5)
Waste generation and composition forecasting; Technology evaluation; Source separation composting and recycling programme design; Facility cost and operational data estimation; Facility location, sizing and investment timing, and waste allocation investigation; and (6) Simulation of an existing or proposed MSW management system. The general framework of the decision support system modelling components is presented in Fig. 2. Instructions on the use of the software packages and a description of the planning
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Step 1: Waste generation and composition forecasting
Introduction
Step 2: Technology evaluation
Design composting programmes?
Yes
Step 3: Source separation composting programme design
No Step 4: Source separation recycling programme design
Yes
Design recycling programmes? No Step 5: Introduction to modelling tools, facility-related data collection/estimation
Conduct optimization study? No Step 7: Simulation study — simulate operation of a MSW system
Yes
Yes
Step 6: Optimization study — model formulation — output analysis — sensitivity analysis — generation of near— optimal solutions
Conduct simulation study? No End
Fig. 2. Modelling component interaction in the proposed prototype decision support system.
support capabilities of the decision support system are provided by an initial knowledgebased system component. General data are then collected: economic parameters, including the prevailing interest rate; the current year; community characteristics, including population and the number of dwellings; and average waste material densities. The user is then guided through the development of waste generation and composition forecasts over an assumed 20-year planning horizon, using a series of knowledge-based system components and spreadsheet models. Waste data may be developed for the
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residential and industrial/commercial/institutional (ICI) sectors of a community. Waste analyses reported in the literature are supplied to the user to assist with the forecasting process. The user may then access introductory information stored in knowledge-based system components on the various MSW management technologies. Information is supplied on the advantages and disadvantages of alternative technologies which may be used to select technologies for consideration in the planning or expansion of a MSW management system. Assuming source separation composting and/or recycling programmes are to be included, the user may then be assisted in designing programmes through interaction with a number of knowledge bases and spreadsheet models. The user may then be provided with a description of several modelling tools that could be used to explore facility selection and location, facility investment timing, facility sizing, and waste allocation issues with respect to managing recyclables, compostables, processing residuals and/or the entire mixed MSW stream. To investigate facility-related planning issues using either spreadsheet models (which calculate costs for a given MSW management system and assumed waste allocation pattern) or an optimization modelling approach, certain cost and operational parameters are required for each of the technological options being examined or being considered for inclusion in a MSW management system. A set of knowledge bases and spreadsheet models have been developed to assist with the estimation of the required facility-related modelling parameters. For existing facilities, several cost and operational parameters are requested: the current capacity; the current operating cost and tipping fee; the fraction of the input stream that is recovered as materials or energy, and the revenue generated from the sale of the recovered materials or energy; the number of operating days per year; and the fraction of the design capacity utilized for normal operation. For potential facility sites, the user is assisted in estimating the necessary data required to explore facility planning issues using the spreadsheet or optimization modelling tools through the provision of data reported in the waste management literature. After cost and operational parameter estimates have been provided for all of the facilities that are being considered for a particular MSW management system planning problem, the user may then examine the individual spreadsheet models and perform a capital cost curve estimation procedure for any potential facility types. The spreadsheet models may also be used to conduct a cost analysis for a particular site, whether it is an existing or proposed (potential) facility site, based on waste allocation estimates to the site. Alternatively, the user may decide to utilize an optimization approach in the investigation of an existing or proposed MSW management system. The basis for the optimization study is a mixed-integer linear programming (MILP) dynamic, multi-period, model formulation for facility location, timing and sizing, and waste allocation. This allows the user the flexibility of developing an MILP or a linear programming (LP) model formulation for a facility planning problem. The general MILP facility planning model formulation described in the prototype decision support system is based on the previous work of several researchers (Kuhner & Heiler 1973; Kuhner & Harrington 1975; Jenkins 1979). Several extensions to the basic MILP MSW management systems planning model formulations suggested in the literature were required to make the approach more generally applicable to current facility planning, as the previous models focused on the transportation, processing and disposal of mixed MSW only. Knowledge bases were developed to assist in the formulation of the
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optimization model, as well as analysing the model outputs and conducting postoptimality sensitivity tests. The user is also provided with advice on methods available to generate near-optimal solutions for a previously optimized MSW management system planning problem through consultation with a knowledge base based on the previous work of several researchers (Brill 1979; Chang et al. 1982; Harrington & Gidley 1985). The techniques require adjusting the original model formulation to generate solutions that are different with respect to facility development, facility usage and waste allocations patterns, but that have costs within a specified range of the optimal (least-cost) solution. Alternative systems may then be compared on the basis of costs, in addition to the consideration of unmodelled social, political, environmental and technical considerations. The final option in the programme and facility planning decision support system is the examination of the effect of data uncertainty and temporal variation in waste generation on the overall economics and efficiency of a MSW management system, to provide another means of evaluating and comparing alternative. A discrete, deterministic simulation model has been developed in FORTRAN to provide a simulated time history of the costs and waste allocation for a given MSW management system over a given time period. The simulation model can utilize either a forecasted series of waste or source separated material flows, or a synthetic waste series generated from the stochastic variation exhibited by a historical waste series, for each of the waste source areas in the study region. The user is assisted in developing model inputs and analysing the outputs of the simulation model through interaction with a knowledge base and accompanying documentation.
4. Verification and validation of the decision support system Model verification involves ensuring the mathematical equations and the solution procedure are correctly represented, and that the model correctly implements its specifications (O’Keefe et al. 1987). Model validation is concerned with substantiating that a model is sufficiently accurate for the intended use. In addition to its representation of the behaviour of the real system, a mathematical model’s validity must also be judged based on its usefulness, useability and cost (Landry et al. 1983; O’Keefe et al. 1987). The spreadsheet models created to assist with the estimation of facility costs were based on the costing methodology developed by Smith (1989), and the basic model parameter requirements of the MILP optimization model. Hand calculations and comparisons to reported values were the techniques used to verify the mathematical equations. In order to verify the mathematical formulation for the optimization model to assist with facility planning, test case studies were conducted based on the inputs and model outputs for a hypothetical problem and for a case study application to ensure the completeness and corectness of the formulation. The simulation model was verified by comparing model outputs to the optimization output for a test case application. The approach of utilizing linear programming (LP) or mixed-integer linear programming (MILP) techniques to model MSW management systems planning problems has been demonstrated through the previous work of numerous researchers including Kuhner & Harrington (1975), Greenberg et al. (1976), and Jenkins (1979). Sensitivity analysis and simulation have been proposed as additional means by which a planning
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model may be validated through the examination of model parameter uncertainty (Brill 1979; Lund 1990). The validity of optimization approaches may be enhanced by utilizing techniques to generate alternative and near-optimal solutions (Kuhner & Harrington 1975; Rogers & Fiering 1986). The user of the decision support system is assisted in conducting sensitivity analyses to examine the effects of uncertainty. Assistance is also provided on the generation of near-optimal solutions to allow alternative systems with costs near that of the optimal (least-cost) system to be examined and compared. A simulation model has also been developed as an additional means of evaluating alternative MSW management systems. The development of knowledge-based system components provides additional means to ensure that the developed models are sufficiently accurate for their intended uses, as the user is provided with a collection of values reported in the waste management literature. Verification of these components was a fairly straightforward task given the use of a rule-based representation of knowledge, and their limited problem scope and size. The knowledge bases are restricted to dealing with one type of programme, facility or modelling stage, and generally contain approximately 10–50 rules. The logical correctness of the various knowledge bases was verified primarily by exhaustive testing of rule sets. In addition, the system was designed to provide advice as opposed to making decisions, and the knowledge encoded in the system was derived from literature sources as opposed to human experts. The validation methods applied in the development stage should also be feasible for long-term maintenance of the knowledge-based system components of the decision support system, providing the problem scope and size of the rule bases remain relatively small. The validity of a model must also be concerned with the issues of usefulness, useability and cost if it is intended as a means of assisting and improving decision-making for a public sector planning problem (Clapham 1987). The need for a set of modelling tools to assist with the development, evaluation and elaboration of alternative solutions to public sector planning problems has been suggested by Brill (1979). This approach to providing decision support to waste management engineers and planners has also been supported by numerous other researchers and waste management professionals (Clark & Gillean 1981; Wilson 1981; Light 1990). Fiksel & Hayes-Roth (1989) support the application of knowledge-based system techniques to planning activities in general, if they are designed to provide advice as opposed to recommend courses of action. Several knowledgeable waste management professionals were consulted to determine the reasonableness, accuracy and usefulness of the prototype decision support system for long-range MSW management systems planning. The organization of the prototype decision support system was judged to be logical and reasonably accurate, and the input parameter requirements were deemed reasonable. The usefulness and userfriendliness of the mathematical models was enhanced by the addition of the knowledgebased system components. The prototype decision support system was noted for its ability to provide waste managers with the tools to develop and refine estimates of programme and facility costs. The prototype planning tool was also recognized for its benefits in permitting scenario analysis and facilitating comparisons with systems operated by other communities. The projected cost of the system software (approximately U.S. $500, assuming spreadsheet software is available) was viewed as reasonable. The prototype planning tool was considered to be easy to use with its menus and interactive data input. Additional development work is desired before the system is used in actual planning situations.
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The prototype decision support system is designed to illustrate the benefits of combining the capabilities of knowledge-based system techniques and mathematical modelling tools to provide a more flexible and powerful environment in which to conduct preliminary long-range planning studies. Communities conducting preliminary planning of their MSW management systems may capitalize on the experience reported by others and avoid problems with lengthy or costly investigations. The planning tool supports the use of optimization models to investigate alternative MSW management systems as it provides the user with an indication of near-optimal systems for proposed scenarios, and to evaluate systems as it provides the user with advice on the use of sensitivity analysis. The simulation component permits waste managers to examine the impacts of: different waste generation growth patterns; community waste reduction, reuse and recycling impacts or goals; or, fluctuating material and energy markets (Lawver et al. 1990). The modelling approaches allow the examination of the modelling assumptions and data uncertainty. Thus, the developed system is considered to be both useable and useful, and a potentially valid approach to providing support for MSW management systems planning.
5. Application of the decision support system In order to demonstrate the application of the prototype decisions support system to facility planning studies, a case study was conducted based on the current recycling situation in the community of Winnipeg, Manitoba, Canada. A preliminary study of the recyclable materials management system is described. Winnipeg is the capital city of Manitoba, and is located at the junction of the Red and Assiniboine Rivers in southern Manitoba. The 1991 census (Statistics Canada 1992) reported a population of 616,790 for the city of Winnipeg. Although the population of Winnipeg has experienced sharp increases and decreases in the late 1970s and early 1980s, respectively, the population is showing signs of stabilization. Based on the results of a recent study by the Manitoba Bureau of Statistics (1989), a population growth rate of 0.7% per year was assumed over the 20-year planning study time horizon of 1991–2011. Currently, Winnipeg does not operate any incineration facilities or transfer stations. Consistent with many communities in Western Canada, Winnipeg relies almost exclusively on the use of landfills for disposing of its MSW. Following a 1982 landfill siting study, a 40-year landfill site was proposed to replace the existing facility. A dropoff programme for centralized leaf and garden waste composting has recently been initiated. Composting takes place at a landfill site using minimal technology, windrow composting that requires 2–3 years for processing. Recycling activities are primarily privately operated, and include a combination of drop-off depot collection and curbside collection programmes. The City of Winnipeg recently provided funding for the establishment of additional drop-off depots that would be used to collect materials for processing at an existing privately-operated materials recovery facility (MRF). A planning study was conducted to investigate processing facility requirements if the City of Winnipeg were to instigate a widespread, municipally-operated or municipally-funded drop-off recycling programme or curbside recycling programme. Information regarding current waste management practise was obtained from the City of Winnipeg. The waste generation and composition forecasting models of the planning tool were
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TABLE 1 Summary of the proposed drop-off depot recycling programme parameters Source separation recycling
Depot collection to service residential sector
Participation rate Start-up cost Total waste diversion Annual cost Annual cost per tonne of waste diverted Optimal present value system cost over the 20-year planning period
25% $1.5 million 10,000–14,000 tonnes year−1 $500,000–$700,000 year−1 $50 tonne−1 $6.3 million
TABLE 2 Summary of the proposed curbside recycling programme parameters Source separation recycling
Weekly programme to service residential sector (except large apartment complexes)
Participation rate Start-up cost Total waste diversion Annual cost Annual cost per tonne of waste diverted Optimal present value system cost over the 20-year planning period
85% $4.0 million 9000–48,000 tonnes year−1 $1 million–$3 million year−1 $100 (start-up)–$70 (Year 20) tonne−1 $43.2 million
first used to develop estimates for the residential sector over a 20-year planning horizon. In the first year of the case study, the single-family dwellings were forecasted to generate 257,000 tonnes of waste, while the multiple-family dwellings were forecasted to generate 57,000 tonnes of waste. The basic data required with respect to population and dwelling types within the city of Winnipeg were obtained from several sources (Manitoba Bureau of Statistics 1989; Statistics Canada 1992). The source separation recycling programme design components of the decision support system were utilized to design two alternative voluntary recycling programmes to service the residential sectors; one programme was designed based on the use of drop-off depots, and the other programme was designed based on the curbside collection of recyclables. This estimation process was required in order to forecast the total material tonnages that would require processing within a system of MRFs. The programmes were assumed to expand over time to include more materials and to include more residences due to population growth. The curbside collection programme was assumed to start with a pilot-scale programme. A summary of the main programme parameters for the drop-off and curbside collection alternatives are summarized in Tables 1 & 2, respectively. The ranges listed for several of the programme parameters indicate their values in the start-up year and final year (Year 20) of the planning period. The material recovery levels predicted for the drop-off recycling programme were assumed to be managed using three existing processing facilities. Due to the higher forecasted material recovery levels for the curbside collection programme, the development of at least one additional facility would be required, as no expansions were
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assumed possible at the existing MRFs. Three potential facility locations were assumed, with two of the potential sites being located in proximity to the current landfill sites. The city of Winnipeg was subdivided into 12 waste generation source areas based on the 1991 census tracts (Statistics Canada 1992). The total forecasted recyclable material recovery levels were allocated to the waste source areas based on relative populations and expected growth rates. An optimization study was conducted for the forecasted material recovery levels for the drop-off and curbside collection options. The MILP solver package MILP88 (Eastern Software Products 1988) was used for the case study. Waste allocation and facility selection/location issues were investigated in consultation with the facility planning components of the prototype decision support system. As the materials recovered through the drop-off programme over the 20-year planning horizon were assumed to be managed using three existing MRFs, this systems planning problem reduced to an optimization of flow allocation and required only a linear programming model formulation. Material flow values were developed for each of the 12 waste source areas for each year of the 20-year planning horizon. Estimates for the collection cost and the expected revenue from the sale of recovered materials associated with flows of recyclable materials, applicable to all the MRFs, were developed using the results from the source separation programme design stage. Facility operating costs were approximated using information in the facility-related data estimation knowledge base developed for MRFs. Transportation costs were estimated through consultation with a knowledge base; transportation distances between facility sites and waste source areas were estimated from a City of Winnipeg map. The remaining model parameters (the residuals disposal cost and facility capacities) were assumed based partially on information provided by several waste management professionals involved in the operation of the current MSW management system. The model formulation for the optimization study of the drop-off recycling programme processing system was developed with the assistance of the optimization modelling components of the prototype decision support system. The solution package also provided reports summarizing the post-optimality sensitivity analysis information. The optimal present value system cost was determined to be $6.3 million which includes the costs of collecting, transporting, and processing the materials. In the optimal solution, one facility is allocated materials such that it continually operates at full capacity. As material flows gradually exceed the available capacity of this facility, flows are assigned to the other two facilities. These results may be explained due to the difference in operating costs assumed for the facilities and haul transportation distances, as all other cost parameters were identical. The investigation of a processing system for the forecasted flows of recyclable materials from the proposed curbside recycling programme was also conducted. In this case, only one of the existing facilities was included in the system (as the other two facilities have very limited processing capabilities), along with three proposed sites for new MRFs. Potential facility capital costs were estimated using a fixed and a variable cost; capacity development and expansions were limited to discrete units of capacity. Thus, a mixed integer linear programming model formulation was required. The remaining model parameters were estimated in a similar manner to the drop-off programme processing system problem previously described. The 20-year time horizon was modelled in five, 4-year time periods. The optimal present value system cost was determined to be $43.2 million which
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includes the costs of facility construction and expansion, and the costs of collecting, transporting and processing the recyclable materials. In addition to requiring the full use of the capacity of the existing facility, the solution calls for: the development of a facility in Period one at 50 tonnes day−1 and a subsequent expansion of this facility by 50 tonnes day−1 in the second time period; and the development of the other two potential facilities in the second time period at 50 tonnes day−1 each. The available capacities of two of the potential facilities are fully utilized, and the capacity of the other potential facility is gradually utilized over time. Using the methods for generating near-optimal solutions described in several water resources research papers (Brill 1979; Chang et al. 1982; Harrington & Gidley 1985) and summarized in a knowledge base, several alternative solutions were produced with total system costs within 5% of the optimal (least-cost) solution. Three near-optimal solutions were generated which involved varying facility location, sizing and timing of investment patterns, and waste allocations. The existence of near-optimal systems indicated the general flexibility of the assumed system of facilities for managing the recyclables collected through a curbside programme. As maximum capacity limitations were not imposed on the sites due to the availability of land, one near-optimal solution required the development and expansion of only one of the proposed facility sites. The near-optimal systems solutions also provided excess capacity within the system as there was no direct force for the reformulated model to produce minimal cost alternatives. Conducting a simulation study of the optimal system for the same time periods as the optimal system provided a means of verifying the optimization modelling approach. The total present value system cost calculated by the simulation model was within 0.1% of the optimal solution. The simulation model was also used to evaluate the proposed optimal recyclable materials management system for the curbside collection programme under yearly time increments. This allowed the examination of the variability in total material flows and system costs within the optimization time period increments. The total present value system cost based on an annual analysis of the system was calculated as $39.2 million. The difference in the total present value system costs calculated through the two mathematical models may be attributed primarily to the effects of discounting on a yearly basis in the simulation model. The simulation study was conducted using the FORTRAN simulation model with the assistance of the accompanying knowledge base. The case study results were reviewed by a City of Winnipeg waste management engineer, and were found to compare favourably to previously determined estimates for a recyclable materials management system. This case study has demonstrated the extensive and flexible nature of the facility planning components of the prototype decision support system. As well as assisting with the investigation of facility location, sizing, development and expansion issues, the optimization and simulation modelling components may be used to generate alternative solutions to a planning problem and perform sensitivity analyses.
6. Concluding remarks An extensive literature review identified numerous mathematical models developed for MSW management and planning. The emphasis of these previous studies has been on model formulation as opposed to issues such as useability and data requirements. A technical survey indicated that the available mathematical modelling techniques are
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not widely utilized in practise, particularly in municipal or regional waste management agencies. Waste management engineers and planners require additional tools to assist in the development and evaluation of integrated MSW management systems, particularly as the knowledge and technological options in this field continue to expand. There is both a need for, and an interest in, a useful, practical (with respect to data requirements and cost) and reasonably objective form of decision support system for use at the municipal and regional MSW management decision-making levels. Decision support systems based on the use of knowledge-based system techniques could be developed to interface with individual models, assist with model selection, or manage a series of models required to investigate a MSW management or planning activity. These suggested decision support approaches may extend and support the use of mathematical modelling techniques in practise, while allowing end-users to make decisions using their own skill and judgment. This paper reports on research that attempted to address what numerous studies and inquiries have identified as the most significant deterrent to implementing MSW management projects: the perceived complexity of the planning process. A prototype decision support system has been designed to identify the activities and decisions required to conduct preliminary planning studies for MSW management systems. The prototype decision support system contains components related to the major planning activities: waste forecasting; technology evaluation; source separation programme design; facility sizing, location and investment timing; waste allocation; and MSW management systems analysis using simulation. Based on the opinions expressed by several practising waste management professionals, the integration of knowledge-based systems and mathematical models appears to be a suitable method of providing decisionmaking support to waste management professionals. The use of the system also yielded useful and reasonable results for a case study application. Further research is required to identify perceived deficiencies in the existing mathematical models used within waste management and planning agencies. Due to the constantly changing decision-making environment in MSW management systems planning, future decision support systems should be designed to be modular, with rulebased knowledge representation to facilitate end-user understanding and the updating of the expertise encoded in the system. Acknowledgements This paper is based on the Ph.D. dissertation submitted to McMaster University by Kimberly D. Barlishen. This research was supported by the Natural Sciences and Engineering Research Council of Canada and McMaster University. The assistance provided by Dr. Gilles Patry and Dr. Norman Archer is gratefully acknowledged. References Barlishen, K. D. (1993) Decision Support for Municipal Solid Waste Management and Planning. Unpublished Ph.D. Dissertation, Department of Civil Engineering, McMaster University, Hamilton, Ontario, Canada. Brill, E. D., Jr. (1979) The use of optimization models in public-sector planning. Management Science 25, 413–422. Chang, S-Y., Brill, E. D. Jr. & Hopkins, L. D. (1982) Use of mathematical models to generate alternative solutions to water resources planning problems. Water Resources Research 18, 58–64.
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