Site selection of construction waste recycling plant

Accepted Manuscript Site Selection of Construction Waste Recycling Plant

Qingwei Shi, Hong Ren, Xianrui Ma, Yanqing Xiao PII:

S0959-6526(19)31343-5

DOI:

10.1016/j.jclepro.2019.04.252

Reference:

JCLP 16608

To appear in:

Journal of Cleaner Production

Received Date:

14 December 2018

Accepted Date:

19 April 2019

Please cite this article as: Qingwei Shi, Hong Ren, Xianrui Ma, Yanqing Xiao, Site Selection of Construction Waste Recycling Plant, Journal of Cleaner Production (2019), doi: 10.1016/j.jclepro. 2019.04.252

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ACCEPTED MANUSCRIPT 1

Site Selection of Construction Waste Recycling Plant

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Qingwei Shi 1, Hong Ren 2, Xianrui Ma3*, Yanqing Xiao 4

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1School

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Email: [email protected]

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2

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Email: [email protected]

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3

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Chongqing, 400045, PR China, Email: [email protected]

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4

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of Construction Management and Real Estate, Chongqing University, Chongqing, 400045, PR China,

School of Construction Management and Real Estate, Chongqing University, Chongqing, 400045, PR China,

Correspondence author: School of Construction Management and Real Estate, Chongqing University,

School of Business Administration, Guangzhou University, Guangzhou, 510006, PR China, Email:

[email protected]

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Site Selection of Construction Waste Recycling Plant 1 / 34

ACCEPTED MANUSCRIPT Qingwei Shi 1, Hong Ren 2, Xianrui Ma3*, Yanqing Xiao 4

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Abstract

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Under the background of the development of construction waste recycling in China, optimizing the

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site of construction waste recycling and disposal plant is important, considering not only the cost of

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construction waste recycling but also the impact on the surrounding environment. This study aims to

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minimize the cost and negative environmental effects. In order to find the best method to solve the problem

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of multiobjective function optimization, we propose a multiobjective location model which combines

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genetic algorithm with probabilistic robust optimization. The model first uses genetic algorithm to get

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preliminary result and then it uses probabilistic robust optimization to find the optimal solution. The

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preliminary results show that 1, 3, 5 of the candidate sites more cost-effective and environmentally friendly

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than other. The fitness value converges at a stable value of 1.55 × 10−5, and the Pareto optimal frontier

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presents considerable clustering characteristics, which prove the rationality and operability of the site

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selection optimization model. Meanwhile, the robust model analysis under the given uncertain

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environment achieves the purpose of further optimization of the site. The research results can provide the

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government with a theoretical basis for the site selection of construction and demolition waste recycling

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plants.

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Highlights

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

A new multiobjective location model is proposed for site selection of waste recycling plant.

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

Quantitatively describe the impact of cost and environmental constraints on site selection.

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

The multiobjective location model using GA and Pro more efficiently and accurately than traditional

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methods. Keywords: Construction waste; Reverse logistics; Location optimization; Genetic algorithm

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1. Introduction

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At present, construction and demolition waste recycling plants (C&DWRP, recycling of construction

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and demolition waste [C&DW] by plants) and are reasonable substitutes to existing unsustainable

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treatment methods, such as landfills and fly tipping. These existing methods are two popular practices of

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poor C&DW disposal in numerous countries, especially developing countries. As a covering ratio, C&DW

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currently accounts for 25%–45% of waste landfills (Townsend et al., 2015). In particular, according to

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National Development and Reform Commission of China, over 90% of C&DW is landfilled in China (NDRC,

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2014). C&DW landfills (i) they consume large amounts of space and (ii) are recognized to produce harmful

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chemical leachate, anaerobic degradation that causes air pollution, landfill gas from organic waste, and

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other contaminants, all of which contribute to acidification and toxic impact on the ground and surface

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water and soil by putrefaction (Del et al., 2010; Lu et al., 2015). In fact, 50%–95% of C&DW generated

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can be recycled depending on its nature (Serdar et al., 2017). In the 1980s, several plants for sorting and

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recycling went into operation in some countries, especially developed ones, due to the growing awareness

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of the pollution and resource potential of C&DW (Gomes et al., 2008). However, the recycling rate of

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C&DW is still low, including Greece, Portugal, Hungary and Spain, the rates are under 15%. (Rodrígueza

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et al., 2015). The main reason is that there are fewer C&DWRPs.

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In terms of environment protection potential, the most environment-friendly treatment is C&DW

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recycling, followed by landfilling and incineration (Ortiz et al., 2010). The advantages of C&DW recycling

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are extensive: conservation/preservation of precious land areas, extension of the lifespan of landfills, cost

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effectiveness of using recycled products, improvement of general environmental status in terms of energy

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and pollution, minimization of the resource consumption, utilization of waste that would otherwise be lost

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to landfill sites, and job creation (Serdar et al., 2017). This fact highlights the necessity of minimizing the 3 / 34

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negative environmental impacts of C&DW and maximizing the social benefits and reclamation of wastes

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by C&DWRP.

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Objective and accurate C&DWRP site selection is the foundation of the sustainable development of

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the construction industry. C&DW recycling is necessary for the growing C&DW flow and the reuse of

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nonrenewable resources. The method for rapidly and effectively achieving C&DW recycling has become

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a research hotspot for scholars. Reverse logistics is effective in solving the problem of resource shortage;

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therefore, it is applied to solve the problem C&DW recycling (Barbosa et al., 2018). In the reverse logistics

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network, the location of the C&DWRP is the core of the entire logistics network (Ghiani et al., 2014). The

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rational planning of the C&DWRP can effectively reduce the occurrence of disorderly discharge on the

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construction site, promote the recycling ratio of C&DW, and advance the standardization of C&DW

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processing. This approach not only maximizes economic benefits and resource recycling but also

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advances the diversification of C&DW recycled products, minimizes negative environmental effects, and

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promotes the green sustainable development of the construction industry (Eriksson et al., 2005).

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1.1 The site selection work situation of C&DWRP

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Site selection work has exhibited great progress in several respects, such as economic evaluation,

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C&DWRP location, and environmental assessment (Ghiani et al., 2014; Serdar et al., 2017). However,

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several shortages remain in the site selection work of C&DWRP. The main reasons are as follows.

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1.1.1. The mono-objective lagging method for site selection

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Most of the traditional research on the site selection of C&DWRP is from a single perspective of

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economic, environmental, and administrative management (Serdar et al., 2017). In particular, economic

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benefits are always the primary consideration, such as transportation (Chong and Hermreck, 2010), waste

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disposal (Yu et al., 2013), and total (Yuan and Wang, 2014) costs. The feasibility of site selection is mainly 4 / 34

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based on qualitative method or a combination of qualitative and quantitative methods, which can achieve

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the goal of minimizing costs but lacks consideration of other C&DWRP aspects, such as societal and

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environment aspects of C&DWRP. These methods cannot provide effective guidance for the sustainable

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development of C&DWRP.

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1.1.2. Differential results from different methods

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Various methods are available and the results of which are remarkably different. Aragonés et al.

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(2010) established an evaluation index system that can comprehensively consider the site selection of

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C&DWRP from the perspectives of economy, technology, society, and environment to improve its

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sustainability. The Analytic Hierarchy Process (AHP) model was used to select a number of candidate

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points in the urban area of Valencia, Spain, and determine the best location for C&DWRP. Caruso et al.

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(1993) presented a single-period problem that considers the possible opening of transfer stations and

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plants and minimizes transportation and facility setup costs through a multiobjective model. The problem

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includes multiple commodities and three different objectives (total cost, waste of recyclable resources,

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and environmental impact). The three objectives are then combined into a parametric single objective

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through a weighting method. A set of approximate Pareto solutions was searched through an add-drop

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heuristic. Wierzbicki (1980) found any point in the objective space can be used instead of weighting

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coefficients to derive scalarizing functions which have minima at Pareto points only. This is used to solve

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the problem of location optimization. Coelho and Brito (2013) assessed the environmental impact of

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C&DWRP. The operation of C&DWRP and the transportation of C&DW exert the greatest impacts on

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residents and the environment. Demirel et al. (2016) adopted a deterministic multiperiod model with

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mixed-integer linear programming to design a network of reverse logistics for managing and recycling

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end-of-life vehicles. A real case was analyzed to find the location of recycling centers. Zhou and Zhou 5 / 34

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(2015) utilized a mixed-integer nonlinear mathematical model to design a network of reverse logistics to

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recycle waste paper. Their model aims to find the optimal number of recycling centers with a real case

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study while minimizing the network costs. However, the model parameters are considered deterministic.

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None of the abovementioned research has presented effective and feasible treatment measures nor

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performed an omnidirectional (economic, technical, social, and environmental) appraisal to C&DWRP in

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the operation stage and proposed secondary optimization of the treatment plant site.

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1.2. Without reliable multiobjective location optimization methods

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Numerous factors, such as technological progress and investigators’ cognitive difference, affect the

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site selection of C&DWRP, but most of them are poorly regulated. A missing possibility along this direction

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is that no traditional method is available for an objective and accurate C&DWRP site selection. Given the

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serious deficiencies in the site selection work of C&DWRP, studies on the accurate calculation of location

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optimization remain seriously inadequate.

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The abovementioned studies indicate that an objective and accurate site selection of C&DWRP is the

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foundation of the sustainable development of the construction industry. The overall innovation and

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contribution of this study are as follows.

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1.2.1. A Genetic Algorithm (GA) method based on multiobjective location model is proposed.

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Two main problems exist in C&DWRP site selection. First, single-objective consideration of cost or

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environmental benefits is adopted. Second, the traditional method is inefficient and subjective and cannot

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optimize the location of C&DWRP to achieve the purposes of facility sustainability (constructing a new

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treatment or disposal facility may take 1–4 years, whereas the operating life of a facility is estimated to be

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approximately 15–30 years) and clean production. In this study, GA is used to establish the multiobjectives

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of minimizing transportation cost (transport environmental impact) and residential environmental impact 6 / 34

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and optimizing locations of facilities. This method can not only overcome the inefficiency of traditional

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mathematical programming methods (e.g., weighting, constrained, and mixed methods) to solve the

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multiobjective problem but also address the nonlinearity, unrepresentativeness, or discontinuity of

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multiobjective and constrained functions (which cannot be solved by traditional methods) and reduce the

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subjectivity of research results (Gong et al., 2009).

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This study has a certain research foundation. Liu et al. (2018) studied the same problem, but their

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research on environmental impact did not provide the appropriate description and correct estimation and

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was not as systematic as cost analysis. The current study establishes a model of environmental estimation

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similar to cost analysis, analyzes the actual treatment capacity of construction waste plant, and performs

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secondary optimization to analyze the environmental impact clearly.

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1.2.2. A new concept is first proposed in this paper.

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In this study, the processing capacity of C&DWRP analysis in uncertain environments is mainly based

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on probabilistic robust optimization (PRO) mathematical model. From strategic, tactical, and operational

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perspectives, Barbosa et al. (2018) analyzed 220 articles on reverse logistics and sustainable supply

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chain. The research on waste reverse logistics network is increasing, whereas the uncertainties of reverse

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logistics network are relatively small. This finding is similar to the results of a literature review by Ghiani

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et al. (2014), who proposed that combining uncertainties (e.g., waste generation) with evolutionary

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algorithms to solve the analytical capacity of treatment plants is best the future research direction.

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The remainder of this paper is organized as follows. Section 2 presents a literature review on the

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mathematical tools of our methodology. Section 3 introduces the multiobjective location model-based GA

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method and explains the model variables. Section 4 indicates the analysis of the processing capacity

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under an uncertain environment and the optimization of the site selection of C&DWRP and provides the 7 / 34

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results of the multiobjective location model and a further discussion based on these results. Section 5

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presents the conclusions and research prospects. In addition, the analysis of environmental impact in this

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paper mainly considers the analysis of residential environmental impact.

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2. Literature review on the mathematical tools of multiobjective location

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The multiobjective location model proposed in our study is essentially a combination of reverse supply

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chain for waste recycling and evolutionary algorithm. A number of current studies have documented the

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development of methods, and the evidence is shown as follows.

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In the sustainable supply chain (reverse supply chain) for waste recycling, the most typical is a three-

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tier reverse (sustainable) logistics network (Rahimi and Ghezavati, 2018); the main elements include

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waste generation, transfer stations, and waste-processing plants, as shown in Figure 1. For the waste

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reverse logistics network, the number of logistics elements in each layer is based on the actual situation.

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Calculation is conducted to solve the problem of regional waste flow (Galante et al., 2010; Rahman and

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Kuby, 1995). Ghiani et al. (2014) summarized the literature on reverse logistics of waste disposal from

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strategic and tactical aspects. The conclusion showed different types of waste reverse logistics network,

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and their characteristics should be considered. Given the particularity of C&DW, 30% of the C&DW input

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may be considered separated, whereas the rest of 70% is the mixed C&DW of which average density is

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1,400 kg/m3 (Serdar et al., 2017).

Waste collection point Collection transport route Transport route

Waste disposal point

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Waste generation point

Fig. 1. Structure of waste recycling reverse network 8 / 34

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Although source separation is mandatory in some EU countries, such as Slovenia, Germany,

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Lithuania, Finland, and Austria (Tojo and Fischer, 2011), off-site or processing-site C&DW sorting is

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always the most preferred selection of contractors. On the basis of China’s environmental policies and

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regulations, C&DW cannot be disposed in densely populated areas. Therefore, a reverse recycling

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network of C&DW with two layers, namely, i) C&DW generation and ii) C&DW candidate processing

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positions, is established in this study by a combination of policy and related literature studies.

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C&DW generation: In this entity, C&DWs are generated. The collected C&DWs are sent to recycling

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centers. This paper aim at recycling a desirable percentage of C&DWs at their point of origin. The C&DW

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generation points include new, demolition, and transformation projects.

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C&DW candidate processing positions (recycling centers): In these processing sites, C&DWs

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gathered from generation points and collection centers are recycled. Recycled wastes are brought to

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project sites and generation points, as well as manufacturers as raw materials. These C&DWs will be

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classified and compressed. The separated C&DWs are unsuitable for recycling and are sent to landfills

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for disposal.

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Traditional mathematical programming methods, such as mixed-integer linear programming (Demirel

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et al., 2016) and mixed-integer nonlinear programming (Zhou and Zhou, 2015), have been widely used in

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the site selection of waste treatment plants, and some achievements have been attained. Although these

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methods can search for the optimal solution or approximate optimal solution, they transform the

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multiobjective optimization into a single-objective problem in a specific way, ignoring the constraints

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among multiple objectives and easily missing the location schemes that achieve the multiobjective

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optimization, but the single-objective is not optimal. With the development of science and technology, the

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application of evolutionary algorithm is the main field of future research for solving multiobjective 9 / 34

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optimization problems. Research is currently lacking in this area. The evolutionary algorithm can not only

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improve the computational efficiency but also extend the individuals in the optimization target Pareto

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frontier to the entire Pareto frontier and spread as uniformly as possible while considering the constraint

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relationship among multiple targets (Gong et al., 2009). Soleimani et al. (2017) utilized GA to solve and

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validate the proposed model in the field closed-loop supply chain, which considered components of end-

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of-life products and raw materials, to design and plan a network of reverse logistics.

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3. Problem description and solution

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3.1 Problem description

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The problem addressed in this section is extensively described. First, general issues in the model are

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presented. Second, the premises of the problem are indicated. Finally, the solution is discussed after a

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presentation of some of the parameters and variables of the problem.

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3.1.1 Multiobjective location model

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This paper proposed a multiobjective model with objective functions of cost and environmental impact

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minimization to realize the sustainability and cleanliness of the construction industry and C&DWRP. The

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proposed model is under the following assumptions and features:

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

The construction of C&DWRP is far from the city center and densely populated residential areas.

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

A portion of generated C&DWs is separated in recycling centers.

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

The model is aimed at finding the optimal number and location of recycling centers among potential

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locations.



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The distance between C&DWRP and densely populated areas is real road distance, which measured by Google Earth without obstacles.

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Various C&DW candidate processing positions have been demonstrated and evaluated by relevant 10 / 34

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environmental laws and regulations, which conform to the relevant local policies and regulations,

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and are optimized under government regulations, without other options.

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

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will remain unchanged for a certain period of time.

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The road conditions of vehicles carrying construction waste are determined, regardless of congestion and vehicle failure.



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The transportation cost of constructing C&DW exhibits a simple linear relationship with transportation distance, without considering management cost.

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Each C&DW unit transportation cost is determined, mainly including transportation vehicle operating and labor costs. The cost is unchanged for a certain period of time.

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The amount of waste generated at each construction waste production point is determined, which

The reverse logistics network of two-tiered C&DW with only treatment station and production point is established, and the capacity of the treatment station is limited.



The proposed model accounts for greenhouse gas (GHG) emissions related to energy consumption

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for constructing and operating recycling centers in terms of CO2 emissions to quantify environmental

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impacts.

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The complete proposed multiobjective location model is presented as follows:

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Sets and indices:

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

𝑚: the subscript of waste production point 𝑚 = 1,2,⋯,𝑀;

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

𝑛: the subscript of the waste disposal station 𝑛 = 1,2,⋯,𝑁；

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𝑃𝑚: production of m waste daily output;

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𝑇𝑛𝑘: the fixed investment amount when the treatment station with grade K is set up at n;

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𝑄𝑘: capacity of K-level processing station; 11 / 34

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𝛼: the cost of transporting each unit of waste;

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𝛽: processing the cost per unit of waste;

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𝜀: carbon dioxide emissions from constructing and disposing various construction materials

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𝜔: carbon dioxide emissions from transport units per unit of waste

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𝑙𝑚𝑛: the measured traffic distance from generated point m to processing station n.

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The definition of decision variables is as follows:

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𝑋𝑛𝑘 =

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𝑌𝑚𝑛 =

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{

1, at the alternative site n, a waste disposal station with a grade of K will be 𝑏𝑢𝑖𝑙𝑑𝑖𝑛𝑔, (3.1) 0, or else

m CDW is recycling by treatment station n {1, waste generation point 0, or else. (3.2)

3.1.2 Objective functions

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The first objective function tries to minimize total costs (𝑓1) and is determined as follows:

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𝑚𝑖𝑛 𝑓1(𝑋𝑛𝑘,𝑌𝑚𝑛) = ∑𝑛

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The equation (3.3) comprises three parts. The first part calculates the expected construction cost

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of C&DWRP. The second part expresses the expected transportation costs of C&DW. The third part

14

indicates the expected disposal costs of C&DW.

𝑁

∑𝐾 𝑇 𝑋 = 1 𝑘 = 1 𝑛𝑘 𝑛𝑘

𝑀 𝑁 𝑀 𝑁 𝐾 + ∑𝑚 = 1∑𝑛 = 1𝑃𝑚𝑙𝑚𝑛𝛼𝑌𝑚𝑛 + ∑𝑚 = 1∑𝑛 = 1∑𝑘 = 1𝛽𝑃𝑚𝑋𝑛𝑘. (3.3)

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On the basis of an on-site survey of the Guangzhou Bureau of City Appearance, Environment, and

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Sanitation, the transportation cost per unit of waste unit is 𝛼 = 5 Chinese Yuan (RMB)/km·t; the

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processing cost per unit waste of the treatment station is β = 500 RMB/t.

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The second objective, namely, environmental impacts of the network (𝑓2), is as follows:

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𝑀 𝑁 𝑀 𝑁 𝑀 𝑁 𝐾 𝑚𝑖𝑛 𝑓2(𝑋𝑛𝑘,𝑌𝑚𝑛) = ∑𝑚 = 1∑𝑛 = 1𝜀𝑄𝑘𝑋𝑛𝑘 + ∑𝑚 = 1∑𝑛 = 1𝜔𝑌𝑚𝑛𝑙𝑚𝑛 + ∑𝑚 = 1∑𝑛 = 1∑𝑘 = 1𝜇𝑃𝑚𝑌𝑚𝑛𝑋𝑛𝑘. (3.4)

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The first term presents the environmental impacts caused by opening C&DWRP. The second term

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stands for the environmental impacts of transporting C&DW. The third term indicates the environmental

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impacts due to recycling C&DW in C&DWRP.

2

This paper considered GHG emissions related to energy consumption due to the construction of

3

C&DWRP, shipping of C&DW to reverse logistics supply chains, and processing of C&DW in C&DWRP,

4

which are all expressed in terms of CO2 emissions, to quantify the environmental impacts.

5

The emission used in the model is obtained from recognized data sources, including reports

6

(e.g., Ecoinvent Centre, 2007), research articles (e.g., Zhang et al., 2018), and commercial emission

7

calculators (EPA, 2015). For instance, Jeong et al. (2012) evaluated the environmental impacts of

8

materials consumed in construction in accordance with CO2 emission. Their results showed that the

9

average CO2 emissions of various construction materials are approximately 500 kg/l-CO2. Zhang et al.

10

(2018) evaluated the environmental impact (energy and CO2) of C&DW in reverse recycling supply chains.

11

Their results indicated that the CO2 emissions of transporting and processing C&DW differ. For example,

12

transportation fuel consumption produces 0.12 kg/km·t-CO2, whereas processing C&DW fuel

13

consumption produced 9.83 kg/l·t-CO2.

14 15

The environmental impacts of the parameter network indicated that 𝜀 = 500 kg/l·t-CO2, 𝜔 = 0.12 kg/ km·t-CO2, and 𝜇 = 9.83 kg/l·t-CO2.

16

As the objective function and its minimization are multiobjective, acquire the weight of the two

17

objectives through expert consultation. They were unanimous that cost is slightly more important than

18

environment. The scores were 3 and 2 respectively, and the scale was judged by AHP matrix. Meanwhile,

19

we finally getting the weight of the cost and environmental were 0.7 and 0.3, respectively. See Deng et al.

20

(2014) for details. Fig. 2 shows the relationship between transportation cost and residential environment.

21

The expression of objective function is 3.5, where λ1 = 0.7 and λ2 = 0.3.

22

𝑚𝑖𝑛 𝐹(𝑋𝑛𝑘,𝑌𝑚𝑛) = 𝜆1𝑚𝑖𝑛 𝑓1(𝑋𝑛𝑘,𝑌𝑚𝑛) + 𝜆2𝑚𝑖𝑛 𝑓2(𝑋𝑛𝑘,𝑌𝑚𝑛). (3.5) 13 / 34

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Impact on residential environment

Cost

the optimal balance point

1

Distance to residential area

2

Fig. 2. Cost and environmental impact changes under dual goals

3

Fig. 2 illustrates that the impact on the residential environment is inversely proportional to the

4

transportation cost. The farther the plant from the residential place, the higher the transportation cost and

5

the smaller the environmental impact. The public opposition to the location and environmental effect of

6

facilities near the inhabitant areas is measured by a decreasing function of the distance from facilities.

7

The main task of this paper is to find the optimal balance point of cost-environment of the site selection.

8

3.1.3 Constraints:

9

𝑋𝑛𝑘 ∈ (0，1)，𝑌mn ∈ (0,1)，(∀𝑛 ∈ 𝑁,∀𝑚 ∈ 𝑀,∀𝑘 ∈ 𝐾).

(3.6)

10

Constraint (3.6) assures that the station construction and transportation occur.

11

𝑃𝑚 ≥ 0,∀𝑚 ∈ 𝑀. (3.7)

12

Constraint (3.7) assures that a certain amount of C&DW is generated every day.

13

∑𝑚 = 1𝑃𝑚𝑌𝑚𝑛 ≤ ∑𝑘

14

∑𝑚 = 1𝑃𝑚𝑌𝑚𝑛𝑋𝑛𝑘 ≤ ∑𝑘

15

Constraints (3.8) and (3.9) guarantee that the total amount of C&DW generated will not exceed the

16

𝑀

𝑀

𝐾

𝑄 𝑋 ,∀𝑛 = 1 𝑘 𝑛𝑘 𝐾

∈ 𝑁, (3.8)

𝑄 𝑋 ,∀𝑛 = 1 𝑘 𝑛𝑘

∈ 𝑁 (3.9)

storage capacity of C&DWRP. 𝑁

17

∑𝑛 = 1𝑌𝑚𝑛 = 1，∀𝑚 ∈ 𝑀. (3.10)

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Constraint (3.10) guarantees that the C&DW from a generation site can only be recycled by a

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C&DWRP. 𝐾

2

∑𝑘

3

Constraint (3.11) indicates that a preparation station can only construct one capacity-level C&DWRP.

4

{𝑄1 = 150,𝑄2 = 200,𝑄3 = 300}. (3.12)

5

Constraint (3.12) shows that the capacity of C&DWRP has three levels, and the unit is 10000 kg per

𝑋 = 1 𝑛𝑘

≤ 1,∀𝑛 ∈ 𝑁. (3.11)

6

day.

7

3.2 Problem solution

8

In the introduction of the problem solution, a simple comparative analysis of traditional mathematical

9

programming and evolutionary algorithms has been performed. In solving some instances and depending

10

on the complexity of a problem, if research address the problem with five C&DWRP centers in a reverse

11

logistics network and 10 C&DW generation points, then the result will produce 30240 options. This

12

problem solution cannot be achieved via conventional approaches, which include mixed-integer linear and

13

nonlinear programming methods. These approaches are not parsimonious due to their time consumption.

14

Some algorithms are used in solving these problems, which can provide acceptable results by

15

validating and improving an array of results. These algorithms are referred to as evolution (or heuristics)

16

algorithms; specifically, GAs is proven to be suitable options (Soleimani et al., 2017).

17

3.2.1 Genetic Algorithm

18

Genetic Algorithm (GA) is a type of evolution algorithm inspired from biology and is applied in heredity,

19

mutation, natural selection, and admixture. The basic idea of GA is to transfer heredity characteristics by

20

genes. Assuming that the total characteristics of each generation are transferred to the next generation

21

through its chromosome, each gene in the chromosome represents a characteristic.

22

This research follows the principles of genetics science for designing an algorithm to obtain solutions 15 / 34

ACCEPTED MANUSCRIPT 1

to the problem. Chromosomes should be coded in preparation for mutation. The meiosis of the selected

2

chromosomes in each generation is used to obtain the best chromosomes.

3

A function for identifying the fitness of these chromosomes in each iteration of the algorithm is defined

4

to evaluate them. After the first generation and evaluation of their fitness, in accordance with the algorithm

5

parameters and selection strategy, including the crossover parameter (pc), mutation parameter (pm), and

6

population size (npop), some members are selected to produce a new generation and the defined

7

operators are applied to them. The new generation is reassessed, and the best generation is preserved

8

for the next generation until the stop standard is reached.

9

An important stage in designing a metaheuristic algorithm is to adjust the parameters that can affect

10

the algorithm effectiveness. In the proposed GA, the roulette wheel is used to select the parents for

11

crossover for determining a better fit than the previous generation in the underline population, and random

12

selection is adopted for mutation. Changes in parameters, such as pc, pm, and npop, can lead to different

13

results (Soleimani et al., 2017). The GA lacks a specific criterion for adjusting the parameters (Soleimani

14

et al., 2017); nevertheless, a comparison of application results of different parameter sets shows that,

15

when npop = 200, pc = 0.8, and pm = 0.3, the optimal result can be obtained.

16

3.2.2 Multiobjective location model-based GA method

17 18

Fig. 3 shows the proposed facility location process for C&DWRP. The role of each entity is explained as follows.

16 / 34

ACCEPTED MANUSCRIPT

According to the construction waste disposal station site selection optimization parameter set

The binary decision variables X and Y are encoded into bit strings

Initialize the population 1, bit string interpretation to get the parameter 2, calculate the objective function 3, the function value to the adaptation value mapping 4, the adjustment of fitness value

Calculate individual fitness values

1, choose 2, cross 3, mutation 4, statistics

Genetic manipulation

NO

Whether to meet the termination conditions YES New population after optimization

1 2 3

Decoding gives the best result

Fig. 3. GA operation flow The specific steps of GA operation flow are as follows:

4

Step 1: The decision variable is determined in accordance with the parameter set of the location

5

optimization problem of construction waste disposal station, the decision variable is binary coded, and the

6

length of the encoded bit string is measured.

7

Step 2: An initial population of bit strings of a certain length is randomly generated. The initial

8

population is composed of randomly generated K initial string structure data, in which each string structure

9

datum is called an individual. GA starts with this initial group, followed by iteration. The basic parameter

10

settings are as follows: the evolutionary algebra counter n is set; the maximum evolutionary algebra N is

17 / 34

ACCEPTED MANUSCRIPT 1

set; T individuals are randomly generated as the initial population P (0); the population size NPOP = 200,

2

crossover rate Pc = 0.8, and mutation rate Pm = 0.3 are set.

3

Step 3: The fitness values of each individual in the initialized population are calculated.

4

Step 4: The genetic operation includes the following steps: (1) the selection operator is applied to the

5

group; (2) the crossover operator is applied to the group; (3) the mutation operator is applied to the group;

6

(4) the statistical results are obtained. After selection, crossover, and mutation operation, the next

7

generation population is obtained, in which the selection operation includes using the best-preserved

8

roulette selection method, from the nth generation to the nth+1 generation, that is, to copy the next

9

generation of individuals. In addition, the crossover operation involves adopting a single-point crossover,

10

that is, to select a crossover point randomly and exchange the genes behind the crossover point. Different

11

operations are based on discrete variation. Individuals with high fitness may be duplicated, whereas

12

individuals with low fitness may be eliminated.

13

Step 5: Termination condition judgment: Genetic operations are performed to generate a new

14

population in accordance with a certain genetic probability. If n > N, then the termination conditions are

15

satisfied; the individuals with the greatest fitness are obtained as the optimal solution outputs in the

16

evolution process. For the termination operation, if n < N, then the termination conditions are not satisfied;

17

step 2 is repeated to continue the operation.

18

3.3 Case study

19

3.3.1 Data

20

The survey indicates that most of the projects under construction are concentrated in the Panyu and

21

Nansha Districts at the southern part of Guangzhou. The amount of C&DW is also increasing. However,

22

with the accumulation of a large number of C&DW, it has a huge impact on the environment and the 18 / 34

ACCEPTED MANUSCRIPT 1

residents. On the basis of the Layout Plan of the Guangzhou Municipal Construction Waste Disposal

2

Venue (2012–2020) and the open bidding document issued by the Guangzhou City Administrative

3

Commission for the preparation of a report on the environmental impact of the construction of a waste

4

disposal market, Guangzhou City is selected as the project candidate location (Fig. 4). From the research

5

of this project group, the locations of candidate land are in southern Guangzhou (taking Beijing Tiananmen

6

Square as the origin of coordinates). Therefore, this area is suitable for site selection research. Table 1

7

presents the locations of candidate land.

8 9

Fig. 4. Distribution of the candidate locations

10

Table 1. The coordinates of C&DW candidate processing positions.

C&DWRP candidate positions

X axis (km)

Y axis (km)

B1

2,544.60509

427.25699

B2

2,533.28259

430.07825

B3

2,532.96884

447.53756

B4

2,527.24678

428.71874

B5

2,517.96501

445.08049

11

The social survey of Panyu and Nansha Districts, Guangzhou, estimates the large-scale construction

12

of 10 new projects and the average daily output of construction waste. The estimated formula (MOHURD, 19 / 34

ACCEPTED MANUSCRIPT 1

2017) is as follows: average daily output estimate = (total construction area * 0.13 area * 0.05)/duration.

2

Table 2 shows the coordinates and average daily production of the production point.

3

On the basis of the market research, Table 3 presents the grade, capacity, and corresponding

4

construction cost of the treatment station; the transportation cost per unit of waste is 𝛼 = 5 RMB/ km·t;

5

the processing cost per unit of waste of the treatment station is β = 500 RMB/t. Table 4 exhibits the

6

shortest distance between the C&DW candidate processing positions and generation point on the basis

7

of Google Earth.

8

Table 2. C&DW generation point coordinate position.

C&DW

Estimated average Duration Land area Total building

generation

X axis (km)

daily yield of

Y axis (km) （day）

（m2）

area（m2）

point

C&DW(t/d)

A1

2,526.44893

433.40578

1,110

130,000

310,000

42.2

A2

2,522.18315

453.58263

1,230

207,000

544,000

65.9

A3

2,520.33385

460.03025

2,325

400,000

1,200,000

75.7

A4

2,523.40626

452.04486

360

25,952

34,389

16

A5

2,521.01577

451.63066

1,960

243,000

881,000

64.6

A6

2,548.24701

433.69223

1,230

223,000

392,000

50.5

A7

2,545.60333

433.69223

900

54,430

370,000

56.5

A8

2,544.41109

434.11699

720

17,000

101,000

19.4

A9

2,522.24652

454.04961

720

6,000

195,000

35.6

A10

2,515.31231

455.67782

540

12,000

76,000

19.4

20 / 34

ACCEPTED MANUSCRIPT

sum

1

8,910

1,318,382

4,103,389

445.8

Table 3. Level, capacity and construction costs of treatment stations. Level 𝑘

Capacity 𝑄𝑘 (t / day)

Construction costs 𝑇𝑛𝑘 (M)

1

150

300

2

250

400

3

400

500

2 3

Table 4. Actual transportation distance from C&DW generation point to resource processing station 𝑙𝑚𝑛

4

(km).

Produces the shortest distance between the point and the processing

B1

B2

B3

B4

B5

A1

25.431

13.432

21.324

10.534

20.653

A2

40.352

31.985

18.254

28.324

14.845

A3

45.952

36.543

23.523

36.724

21.532

A4

37.938

27.252

14.124

28.974

13.134

A5

37.234

29.673

17.459

28.869

12.370

A6

13.413

20.852

24.264

27.686

37.590

A7

10.468

16.237

24.661

25.583

33.851

A8

11.457

16.293

22.549

22.478

32.543

A9

37.661

30.482

17.752

29.247

14.325

A10

44.247

34.652

25.977

34.901

15.696

station

5

3.3.2 Calculation results 21 / 34

ACCEPTED MANUSCRIPT 1

According to the designed GA, the location problem of C&DWRP is solved by programming in the

2

Intel CORE CPU 3.0GHz environment with MATLAB 7.0. The parameters involved in the GA are as

3

follows: population size of 200, crossover probability of 0.8, mutation probability of 0.3, and evolution

4

algebra of 1000. The results are as follows.

5 6

(a) Obtained Pareto optimal solutions in 3-D figure.

(b) Fitness change value of non-inferior

7 8 9

(c)

C&DWRP location results

Fig. 5. Simulation of C&DWRP location results

22 / 34

ACCEPTED MANUSCRIPT Target quality 1.0 0.9

Environmental impact

0.8 0.7

Target completion

0.6 0.5

Cost

0.4 0.3 0.2 0.1

1 2

1

2

3

4

5

6

7

8

9

10

Wast generation point

Fig. 6. The Quality Pedigree of Construction Waste Generation Point under Double Targets

3

Through 1,000 iterations of the objective function by using the GA program compiled by MATLAB

4

7.2, this research determined that three C&DWRPs should be established, and the environmental impact

5

degree (negative environmental effect) of 1, 5, and 3 construction waste treatment points rises in turn.

6

Table 5 shows the detailed description.

7

Table 5. The C&DWRP site should be constructed in C&DW candidate processing positions.

C&DWRP C&DWRP Capacity 𝑄𝑘 (t / day)

C&DW generation point

environmental

establishment point negative effects

8

1

200

1、6、7、8

10

3

300

2、3、4、5

40

5

150

9、10

30

3.4 Result analysis

9

Fig. 5(a) illustrates that the optimal Pareto solution set is obtained by solving the proposed example.

10

Fig. 5(b) displays that the non-inferior fitness value presents a rapid change state and has reached a

11

stable value of 1.55×10−5 after 200 iterations. Fig. 5 (c) shows that the simulation of C&DWRP location

12

results in optimal Pareto solutions. In the computer Intel CORE CPU 3.0GHz environment, the computing 23 / 34

ACCEPTED MANUSCRIPT 1

time is less than 1 h, and its computing efficiency has been greatly guaranteed. Soleimani et al. (2017)

2

also proved this point.

3

The investigations indicate that the proposed GA is effective in solving the described model on a large

4

scale, and the problem solution cannot be achieved via conventional approaches and provides acceptable

5

results. In addition, the time required by the algorithm to solve the model described in this research is

6

acceptable.

7

4. Location optimization of C&DWRP in a random environment

8

In Section 3 has discussed the optimization of the location of C&DWRP in a defined (hypothetical)

9

environment, but the previous articles ignore the existence of the largest uncertainty (waste production)

10

in the real world. Few studies have dealt with the uncertainties in waste generation. They typically address

11

the waste management planning problem to minimize the associated treatment costs through interval

12

analysis, chance-constrained, stochastic, and fuzzy programming approaches. Guo et al. (2008) combine

13

with stochastic programming, integer programming, and interval semi-infinite programming approach for

14

a Solid waste management system. To address the uncertainties affecting coefficients in both hand-sides

15

of probabilistic constraints through probabilistic distributions. Huang et al. (1998) propose an approach

16

based on Grey Linear Programming (GLP), one of the fuzzy programming, to deal with interval input data.

17

Using GLP, the final output is a set of stable interval values for the objective function and for all decision

18

variables related to uncertainty, such as the quantity of waste generated at each district. Yeomans et al.

19

(2003) combine a genetic algorithm with simulation to solve the problem of municipal waste flow allocation

20

under uncertainty, improving the work in Huang et al. (1998).

21

However, they have some shortcomings. For example, fuzzy optimization is a kind of soft constraints,

22

which inevitably leads to conflicts between constraints. Probabilistic robust optimization (PRO) differs from 24 / 34

ACCEPTED MANUSCRIPT 1

them in that it emphasizes the robustness of hard constraints, namely, for uncertain parameters in each

2

uncertain set, the optimal solution must always be feasible. This paper through establish the probabilistic

3

robust optimization (PRO) model of the construction waste reverse network with the lowest total cost in a

4

random environment in Section 3.4 to correspond the study to the actual situation of C&DW recycling.

5

This paper aim to optimize the site selection of C&DWRP and maximize the benefit of the double-effect

6

goal in a random environment (waste production is not fixed).

7

The proposed model is under the following assumptions and features:

8



The basic assumptions in a random environment are similar to the basic assumptions for determining

9

the environment. The main differences are that the amount of waste produced at each waste

10

generation point is unknown but can be expressed by a limited number of possible combinations and

11

the probability of occurrence of various scenarios is known (multiplicity 0.6, probability 0.4). The

12

model only considers the location of the solid waste treatment station from an economic perspective

13

to simplify the complexity of the research problem but specifies the minimum distance between the

14

unpleasant facility (e.g. unpleasant noise and dust) of the treatment station and the residential area.

15

The complete proposed multiobjective location model is presented as follows:

16

Sets and indices:

17



𝑃𝑠𝑚: Under the 𝑠 scenario, the daily production of 𝑚 waste output;

18



𝐶𝑠𝑚𝑛: Under the 𝑠 scenario, the daily amount of waste delivered from the waste generation point m

19 20 21 22

to the treatment station 𝑛. The objective function tries to minimize total costs in the real world and is determined as follows: 𝑆

𝑚𝑖𝑛 ∑𝑠

𝑃𝑠 𝑋 =1 𝑚 𝑠

𝑆

+𝜋∑𝑠

𝑃𝑠 =1 𝑚

[𝑋𝑠 ― ∑𝑆𝑠 = 1𝑃𝑠𝑋𝑠 + 2𝜃𝑠] +𝜏∑𝑠𝑠 = 1∑𝐼𝑖 = 1𝑃𝑠𝑚𝑈𝑠𝑖. (3.13)

Eq. (3.1) consists of three parts. The first item indicates the expected cost of establishing a C&DWRP. 25 / 34

ACCEPTED MANUSCRIPT 1

The second term denotes the total cost deviation, which indicates the means to ensure stability. The third

2

item presents the means to ensure the robustness of the model, indicating that the penalty for C&DW not

3

recovered by the treatment station 𝜔 is the non-negative penalty weight.

4

4.1 Constraints

5

𝑋𝑠 ― ∑ 𝑠

6

∑𝑚 = 1𝐶𝑠𝑚𝑛𝑌𝑚𝑛 ≤ ∑𝑛 = 1𝑄𝑘𝑋𝑛𝑘 ∀𝑠 ∈ 𝑆,𝑛 ∈ 𝑁,𝑚 ∈ 𝑀. (3.15)

7

Constraints (3.14) and (3.15) guarantee that the total amount of generated C&DW will not exceed the

8

storage capacity of C&DWRP.

9

∑𝑛 = 1𝐶𝑚𝑛 = 𝑃𝑠𝑚 ― 𝑈𝑠𝑚

𝑆

𝑃𝑠 𝑋 =1 𝑚 𝑠

+2𝜃𝑠 ≥ 0 𝜃𝑠 ≥ 0,∀𝑠 ∈ 𝑆,𝑚 ∈ 𝑀

𝑀

. (3.14)

𝑁

𝑁

∀𝑚 ∈ 𝑀,𝑛 ∈ 𝑁. (3.16)

10

Constraint (3.16) indicates the actual traffic volume of C&DW under the S scenario.

11

∑𝑘 = 1𝐿𝑚𝑛𝑋𝑛𝑘 ≥ 𝐷,

12

Constraint (3.17) ensures the minimum linear distance between the C&DWRP and residential area,

13

as shown in Fig. 6.

14

𝑋𝑠 = ∑ 𝑛

15

Constraint (3.18) indicates the total cost of C&DW processing under the S scenario.

16

Other constraints are the same as Constraints (3.5), (3.6), (3.7), (3.9), and (3.10).

𝐾

𝑁

∀𝑘 ∈ 𝐾,𝑚 ∈ 𝑀. (3.17)

𝐾 ∑ 𝑇 𝑋 = 1 𝑘 = 1 𝑛𝑘 𝑛𝑘

𝑀

𝑁

+ ∑𝑚 = 1∑𝑛

𝑃 𝑙 𝛼𝑌𝑚𝑛 = 1 𝑚 𝑚𝑛

𝑀

𝑁

+ ∑𝑚 = 1∑𝑛

𝐾 ∑ 𝛽𝑃𝑚𝑋𝑛𝑘. (3.18) =1 𝑘 =1

Dual target quality Environmental impact Transportation cost Minimum environmental requirements

17 18

Distance to residential area

Fig. 7. Cost and environmental impact changes under the minimum environmental requirement 26 / 34

ACCEPTED MANUSCRIPT 1

Fig. 7 shows that the transportation cost (gray) of C&DW is inversely proportional to the environmental

2

impact (white) of C&DW on residential areas under the defined minimum environmental requirements of

3

residents, i.e., the minimum distance D from C&DWRP to the residential areas.

4

4.2 Case studies in uncertain contexts

5

On the basis of Section 3, the C&DWRP position and C&DW generation point are unchanged; the

6

minimum distance is D = 7; the parameters are 𝜋 = 1 and 𝜏 = ∞; the unit C&DW transportation distance

7

cost and C&DW unit processing cost are 𝛼 = 5 RMB/km·t and β= 500 RMB/t, respectively. Table 6 shows

8

the output of the C&DW generation points in different situations.

9

Table 6. Production of construction waste generated at point S.

Daily Silence of Construction Waste under Different Situations (S) Construction waste generation point S1

S2

A1

35

63

A2

55

99

A3

63

114

A4

13

24

A5

54

97

A6

42

76

A7

47

85

A8

16

29

A9

30

53

A10

16

29

27 / 34

ACCEPTED MANUSCRIPT 1

The optimal solution of the objective function calculated by LINGO is 6,230,017 RMB, and 𝑢𝑠𝑖 = 0.

2

Tables 7–9 present the optimal solution of the sought variable.

3

Table 7. Selection location of C&DWRP in uncertain environments.

Need to establish the processing

The point where the station Capacity of the treatment station

station

service is generated

𝑄𝑘 (t / day)

4

5

6

1

250

1、6、7、8

5

400

2、3、4、5、9、10

Table 8. S1 scenario, the optimal solution to the allocation of construction waste. 𝑥111

𝑥125

𝑥135

𝑥145

𝑥155

𝑥161

𝑥171

𝑥181

𝑥195

𝑥110，5

35

55

63

13

54

42

47

16

30

16

Table 9. S2 scenario, the optimal solution to the allocation of construction waste. 𝑥211

𝑥225

𝑥235

𝑥245

𝑥255

𝑥261

𝑥271

𝑥281

𝑥295

𝑥210,5

63

99

114

24

97

76

85

29

53

29

4.3 Result analysis

7

After considering the uncertainty of C&DW production in a real situation, the robust model is used to

8

optimize the above case, and the results are close to the actual environment, as indicated in Tables 7–9.

9

Table 7 shows that the calculated location of C&DWRP in uncertain C&DW output optimizes the

10

candidate site, selects C&DWRP locations of 1 and 5, and reallocates the C&DW production points, which

11

also conforms to the Pareto optimization results, as indicated in Fig. 4(a). Tables 8 and 9 show that in the

12

S scenario, the amount of C&DW per waste production point processed by 1 and 5 C&DWRP per day is

13

approximately the same. 28 / 34

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5. Conclusions

2

This research made investigate a sustainable reverse logistics network design for C&DW recycling.

3

Through consider two pillars of sustainability in the mathematical model. The objective functions of our

4

model include the minimization of the cost and environmental impacts of the network. By considering

5

sustainability in the field of reverse logistics, the study found that the network design for the site selection

6

of C&DWRP covers a gap in the literature and improved the site selection optimization theory of C&DWRP.

7

The research draws on GA to address the problems of the inefficiency and incapability of traditional

8

algorithms to solve complex multiobjective models.

9

The research draws on constraint method and obtain optimal solutions on Pareto frontier to solve the

10

proposed model, and reoptimize the location of C&DWRP in an uncertain environment and provide the

11

allocation quantity of C&DW in different scenarios. Therefore, from the management perspective, the

12

method proposed in this study can provide substantial support to the two major considerations of cost and

13

environmental impact in the site selection of C&DWRP. Furthermore, a realistic and effective solution can

14

be obtained by considering the uncertainty of daily waste output in the real environment. What's more,

15

provided a theoretical basis for government decisions.

16

For future research should consider a few factors for defining social impacts in mathematical models,

17

which include public attitudes toward C&DWRP, and how jobs of the public for the community can be

18

quantified in models to measure social impacts. The developed model considered only the study location

19

before C&DW processing, but should evaluate and reoptimize the cost, environment, and social aspects

20

of C&DWRP in the construction and operation periods. Therefore, designing a multiproduct model in

21

additional periods is also recommended. Presenting a real case study with a metaheuristic algorithm for

22

solving these problems can be another interesting research gap. Because of the GA has many control 29 / 34

ACCEPTED MANUSCRIPT 1

variables, the setting process is complex and the deviation is easy to occur. Therefore, comparing GA

2

with other metaheuristic algorithms can also be a prominent area for future research.

3

Acknowledgments

4

We are truly grateful to editor and other reviewers’ critical comments and suggestions. The authors

5

would like to acknowledge Fundamental Research Funds (RMB) for the Central Universities

6

(No.2019CDSKXYJSG0047) and the National Natural Science Foundation of China (No.71801024).

7

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Chong, W.K., Hermreck, C., 2010. Understanding transportation energy and technical

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