Dynamic planning of construction activities using hybrid simulation

Dynamic planning of construction activities using hybrid simulation

AUTCON-01810; No of Pages 17 Automation in Construction xxx (2014) xxx–xxx Contents lists available at ScienceDirect Automation in Construction jour...
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AUTCON-01810; No of Pages 17 Automation in Construction xxx (2014) xxx–xxx

Contents lists available at ScienceDirect

Automation in Construction journal homepage: www.elsevier.com/locate/autcon

Dynamic planning of construction activities using hybrid simulation Hani Alzraiee ⁎, Tarek Zayed, Osama Moselhi Department of Building, Civil, and Environmental Engineering, Concordia University, 1515 Ste.-Catherine, W., Montréal, Quebec H3G 1M7, Canada

a r t i c l e

i n f o

Article history: Received 7 February 2014 Received in revised form 24 August 2014 Accepted 26 August 2014 Available online xxxx Keywords: Traditional project planning method Simulation Construction CPM

a b s t r a c t Traditional planning methods such as CPM and PERT have been useful tools to manage construction projects' execution. However, models developed using these methods often fail to deliver realistic estimates of project duration, cost and productivity. Failure of traditional planning methods is attributed to the uncaptured causal–effect relationships that exist among the project variables. This paper presents a new method that integrates DES and SD models to address the operational and soft/strategic variables on a single computation platform. The expected outcomes are realistic project schedule networks and enhanced understanding of the interactions of the project's factors. Two cases from the construction sector are used to test and verify this method. Productivity and completion durations were monitored with/without the impact of factors, in which a significant discrepancy has been observed. The new method provided better understanding of the project behavior and contributed to overcome limitations associated with traditional planning methods. © 2014 Elsevier B.V. All rights reserved.

1. Introduction A project schedule is developed by disintegrating the Work Breakdown Structure packages into activities, and then establishing a logical relationship among those activities [1]. Such a model is expected to be the tool for achieving a complex planning, executing and controlling of the project. However, on many occasions, project schedule models fail to provide a concise depiction of the project structure and its real behavior. Such failure is attributed to the complexity, dynamics, uncertainty and the heterogeneous nature of the construction environment [2–4]. The interrelationships among the project variables and surrounding factors are in reality complex causal–effect relationships and not linear as traditional methods suggest [4]. Interactions among project elements, whether internal among the elements themselves or external due to the surrounding environment, are a source of challenges that can hinder developing realistic and a representative planning models. The other aspect of the problem is related to the changing behavior of the project system over time (dynamics), e.g., impact of weather condition on schedule and productivity, and consequence of overtime policy on productivity. Those dynamics significantly impact the execution of project plans and almost are neglected in traditional planning methods. Consequently, Critical Path Method (CPM) based schedule baselines always experience high uncertainty in execution and require continuous revisions and enhancements to capture the dynamics generated during

⁎ Corresponding author at: Department of Building, Civil, and Environmental Engineering, Concordia University, Montreal, Quebec H3G 1M8, Canada. Tel.: + 1 514 848 2424x7091. E-mail address: [email protected] (H. Alzraiee).

the project's execution. In reality, the project is a system of interrelated elements, in which each element is unique in nature, and interacts with other element(s) to generate behavior. Current planning tools consider planning from a static and fragmented perspective. The integrity of a project's strategic and operational variables and their interconnectivity has never been addressed by traditional methods. In contrast, traditional methods break the project system structure into subsystems and deal with one aspect only (usually operational). Eventually, project plans (e.g., schedules) developed using traditional methods result in models that are discrete in nature and not representative of the system. Since introducing CPM in the mid-50's, and the later evolution of the Precedence Diagramming Method (PDM) and Program Evaluation and Review Technique (PERT), traditional scheduling tools provide a useful support although without high precision to decision makers. The shortcoming of traditional planning tools as discussed previously, can be overcome by a more integrative computation environment, possibly coupling with other techniques. The limitations associated with CPM have been addressed by many researchers [5,6]. The strength of traditional methods lies in their ability to model details at the activity level or entity (e.g., duration, resources, and cost); however, they neglect the interrelationships and dynamics among these activities. While developing a CPM-based network, it is assumed that any surrounding factors such as weather, overtime, schedule pressure, rework cycle has minor effects on the outcomes; in addition, the activities are considered dynamically unrelated. This assumption has been proven a major pitfall of CPM-based networks, and many projects are experiencing cost and schedule overruns due to the simplified and linear approach of addressing project activity networks. In reality, the interrelationships among project influential factors are more complex than what have been suggested by traditional methods [7–11].

http://dx.doi.org/10.1016/j.autcon.2014.08.011 0926-5805/© 2014 Elsevier B.V. All rights reserved.

Please cite this article as: H. Alzraiee, et al., Dynamic planning of construction activities using hybrid simulation, Automation in Construction (2014), http://dx.doi.org/10.1016/j.autcon.2014.08.011

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H. Alzraiee et al. / Automation in Construction xxx (2014) xxx–xxx

In addition to neglecting a project's dynamics, traditional project planning methods have shortcomings in addressing the uncertainty observed in estimating/computing costs and duration. For instance, the estimation of an activity's duration and cost is performed based on a deterministic approach that eventually results in a deterministic number. However, in reality, durations/costs are uncertain and continuously change based on the internal/external interactions of a project's elements. As such, those parameters are better represented using a probability distribution rather than a crisp number. This uncertainty has been addressed by the PERT method, where three numbers are used to estimate most probable costs or durations. However, PERT tends to jump to results considering a certain allowance for contingencies, and not addressing the reasons behind the cause of the contingencies. It is widely believed that contingency allowances are added to account for uncertainty and dynamics effects. To address those concerns, simulation tools mainly hybrid ones are considered a promising platform. Methods such as Discrete Event Simulation (DES) and System Dynamics (SD) can be utilized to account for uncertainty and dynamics. DES is effective in analyzing the stochastic nature of parameters at the tactical level. However, DES falls short in modeling a project's holistic level, as well as the interrelationships (feedback process) among its elements. On the other hand, SD is a powerful tool in addressing DES pitfalls. SD is an excellent tool in modeling feedback process in a project, which represents the interaction between project elements, as well as at the holistic level. The objective of this paper is to present a planning and scheduling method that addresses the uncertainty of estimates (e.g., costs and durations) as well as the project's dynamics, simultaneously. The proposed method utilizes a CPM-based network built in a DES environment and integrated with an SD model. It quantifies the management policies, decisions, and soft variables surrounding a project, and then considers their impacts on a schedule built using a traditional planning method. The paper commences by presenting the background to state-of-theart in scheduling and simulation, in addition to the strength and weakness of each technique through a comparative study. The developed hybrid simulation method is presented in Section 4, followed by implementation using two cases from the construction sector to test the applicability of the proposed method. In conclusion, the paper summarizes the limitations of the planning method, future suggestion and improvement. 2. Background Modeling and simulation are powerful tools in representing a system's real behavior in the virtual world [12]. A good simulation model that is capable of mimicking reality should consider four major requirements: 1) type of decision levels; 2) the nature of each variable; 3) complexity of system; and 4) the relationships between variables [13]. Decision levels in construction projects are divided into two parts: a) strategic and b) operational [14]. The strategic definition in this paper is different from the definition pertaining to organizational management. Strategic Level relates to achieving the project set of objectives within the project policy framework. This involves adjustment of certain parameters like costs, resources, and time to meet a prior set of goals [7]. On the other hand, the Operational Level is related the necessary actions taken to meet the project goals set at the Strategic Level. The Operational Level focuses on daily operational details required at the micro level of the project. Generally, construction projects involve discrete and continuous variables. Those variables are related in causal–effect relationships. The system behavior is mainly generated based on the internal and external interactions of those variables within the feedback process that result in a more complex but more representative system. Several techniques and tools were developed to plan, schedule, and control the execution of construction projects and assist management in making informed decisions [15]. The scheduling techniques address the Operational Level (activity), assuming no effects due to uncertainty,

dynamics, and interrelationships. Thus, traditional techniques describe the project as top-to-bottom hierarchy through decomposition of project elements into the smallest acceptable level, where work packages could be described easily by activities. Thereafter, costs, durations, and resources are estimated, mainly from experience, as deterministic numbers. Then the project's job logic is demonstrated as a network of activities interconnected based on work sequence and logic. The apparent purpose of this process is to develop a prototype that depicts the execution of the project in reality. One of the main concerns in such static and linear philosophy of modeling a project's plan lies in the ability of the restructured activities to behave based on assumptions considered at the project's decomposition stage. Furthermore, management in reality is dynamic and responsive to new changes to keep a project on track, rather than adhering to the original plans. Those original plans are targets, or baselines, of the management. When those targets are threatened by overrun, then management actions are triggered to streamline a project toward its stated targets. Hence, traditional planning methods, in reality, produce static baseline plans, which are implemented in a dynamic environment. Those dynamics are resultants from the causal– effect feedback loops that characterize any system with changing behavior over time. Researchers have addressed the limitations associated with traditional planning methods, such as quantifying subjective factors to better estimate project completion duration [16], developing project schedule in a DES environment to generate comprehensive information for project planning [17], combined DES and continuous simulation to account for factors surrounding the project and uncertainty [8], and many other efforts in this field. In general terms, the research conducted in this regard is specific to certain limitations and can be categorized as special purpose models, e.g. weather impact, uncertainty in duration estimates. In this paper, the authors present a generic method and platform that is more capable of addressing the project dynamics, subjective factors, and operational level (activity) than traditional planning methods. The SD method was introduced by Forrester [18] as a method for modeling and analyzing complex system behavior in industrial management. The method has been used solely or coupled with other methods in different fields of social science where a holistic view and feedback process are critical in understating the evolution of the system behavior [19–22]. The SD model aims to capture the feedback processes responsible to the system behavior within a predefined boundary. When a project management team strives to close the gaps between project actual performance and preset targets, such practice is an application of one principal of SD in project management and control [23]. The feedback process experienced in Case Study No.1 shown in Fig. 1 is considered to elaborate on the dynamics process as generated during execution. Productivity of engineers is influenced by many factors such as high schedule pressure, skill level, overtime, and rework. When project execution starts, the expected output is slow and takes the pattern of ramping up in the first 1/5 of project life. The ramping up continues until normal productivity level is reached. The case study being analyzed had the completion duration underestimated; however, it was still required to finish the project on time. Many factors played a role at this stage. For instance high schedule pressure had caused a need for increase drawing production beyond normal limits. When actual productivity of a project falls behind the perceived required productivity, the anticipated completion date becomes invisible. Consequently, management must adopt certain policies to reduce the adverse effects of productivity loss. These policies can be overtime, hiring new workers, extending the project completion duration to improve productivity, etc. in order to attempt to finish the project on time. Another aspect that is of concern is that not all work completed meets quality requirements. A rework cycle during construction phase is inevitable, and initial error correction may cause a secondary errors. The dynamics generated between these factors and other factors extends the project completion duration and creates reinforcing and balancing loops. For instance, one

Please cite this article as: H. Alzraiee, et al., Dynamic planning of construction activities using hybrid simulation, Automation in Construction (2014), http://dx.doi.org/10.1016/j.autcon.2014.08.011

H. Alzraiee et al. / Automation in Construction xxx (2014) xxx–xxx

estimated completion time

remaining time in + schedule

3

project deadline + +

time needed -

days lost from schedule + effect of morale

R

++ error generation rate +

+

R

+ schedule pressure + +

productivity loss +

project actual productivity -

perceived completion rate

+

overtime

+

B

fatigue

drawings quality -

R

+ work completed + with error + work with errors +

B

productivity noise

+ new workforce

work remaining

+ +

project scope

-

Fig. 1. Feedback processes in a project execution cycle (Case 1).

of the loops shown in Fig. 1 depicts what happens when project completion duration is underestimated. As can be seen at the top of the figure, two external factors affect the schedule pressure, “estimated completion time” and “remaining time of work already started”. When the schedule pressure rate increases to a level that impacts productivity (e.g. greater than 1.5), different polices can be applied to overcome this effect. This process can be overcome either be considering working overtime or hiring new workers. Significant overtime or hiring new workers has impact on the quality of work. In both cases, more rework and hence increasing scope of work to be completed and fatigue level will also be at a higher level. This cycle of cause-and-effect will continue to a point where another feedback loop must be enforced to counter these adverse effects. For instance, training, incentives, and revising the project completion date, could be solution to balance the negative effect of schedule pressure on productivity. In the case study being analyzed, no such polices where considered to neutralize the adverse effects of the negative effect loops. 3. Planning methods comparison In this section, the differences between core characteristics of the traditional planning methods are presented. The purpose is to provide a clear and concise focus on the main differences so that strength of each method can be utilized in developing the proposed method. As stated earlier, planning methods can be classified as methods that address the project process level, uncertainty, dynamics, and interaction. There are many studies in literature that compare and contrast planning

and simulation methods [24,25]. We summarize these findings in seven main perspectives as shown in Table 1. The CPM-network and DES methods are appropriate for modeling issues that are operational in focus, reductionism in perspective, quantitative in nature, discrete in change, and narrow in details. The only difference between the CPMnetwork and DES is the later addresses uncertainty. On the other side, the SD method is appropriate for problems that are of strategic/context focus, holistic in prospective, qualitative in nature, continuous in behavior, and broad in details [24]. The main strength of SD modeling method is its ability to mitigate the limitations associated with CPM-network and DES [25]. Thus, both SD and DES/CPM can be viewed as a complementary to one another and the limitations associated with each can be overcome when both methods are integrated. From analysis of the strengths and shortcomings of each method, an integrative platform that builds on the strengths of each method will inevitably enhance the practice of project planning and thereafter execution and control. In contrast to CPM-networks and DES, SD modeling and analysis offer a different perspective on understanding system behavior. This is because an SD model developer first understands the underlying influences responsible on the outcomes, while a developer of CPM-networks tries to anticipate to the outcomes without considering the underlying influences. In CPM, the outcomes are usually computed based on several approximations that eventually neglect an important aspect of the problem, such as, for example the surrounding environment. The need to have a hybrid-planning platform is immense as results of the increasing complexity of construction projects and their planning became evident. Hybrid planning can be achieved in different ways based on the

Table 1 Comparison between planning methods. Perspective

Traditional method CPM-network

DES method

SD method

Focus Level of details Behavior Model type Data type State change Complexity

Activity High details Linear Interrelated but distinct packages Quantitative Discrete Specific

Operation High details Stochastic Interrelated but distinct packages Quantitative At discrete points in time Narrow and focus on complexity and details

Holistic and feedbacks Little details Deterministic Continuous flow Qualitative Continuous Wider focus, general and abstract system

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project management team needs. Rodrigues and Bowers [7] suggested three approaches that can be utilized to develop a comprehensive project-planning model:

Start

Build Hybrid simulation model SD/DES

1- A more sophisticated network model including the feedback processes and detailed mechanisms for modeling activity durations and costs in order to reflect the underlying influences, 2- A more detailed and phased SD model, and 3- Adopting lessons learned from SD models in the forms of set of rules for use in planning and estimation.

Build new SD/DES model

Identify model hybrid structure

The three aforementioned approaches attempt to utilize SD in addressing the two-decision Levels in projects (Operational and Strategic). Attempts have been successful at strategic level modeling; however, at the Operational Level, using SD has not been proved sound despite its use in limited and small applications to model the process level. Whenever SD model has been used at the Operational Level, the model became too complex and involved tedious work. In addition, in cases where SD modeling was used at the Operational Level, the modelers broke down the whole project system into subsystems. Such practice violates one of the fundamental principles of SD (holistic view). Therefore, it is more appropriate to use each simulation method in areas where it shows strength, consistency, and simplicity; otherwise, the applicability of the method by the end-user becomes cumbersome and inappropriate.

Define model boundary, function & role for DES_SD system

Define inputs/outputs and origin of data source No

Are Inputs /outputs consistent with model function and boundary

Yes Define the interface variables in SD/DES model No

4. Methodology

Are selected points consistent with model objectives

4.1. Integrating DES and SD

Yes

The systematic process of Integrating DES with SD is summarized in Fig. 2. The method consists of: (1) developing DES and SD models, (2) formalism to describe the models, (3) synchronizing DES and SD models, and (4) execution platform (Executer).

Ready to formalize interface variables

End

4.2. Developing simulation models and identifying interface variables Step 1 involves developing simulation models and selecting interface variables through which mapping between the variables of both models will take place as shown in Fig. 3. The DES model will address the project schedule network at the operational level while the SD model will be concerned with quantifying the dynamics generated from the interactions of the significant factors. The interactions between the two models will occur through the interface variables that receive data from sender variables and deliver data to receiver variables. The data exchange between the simulation models is unilateral. This means the hybrid model structure that consists of DES and SD will govern this process. For instance, in this paper, the purpose of modeling Case No. 1 is to study the impact of the surrounding factors and strategic variables on a CPM-network developed in a DES Developing Hybrid Simulation Model

Fig. 3. Building DES and SD models for a hybrid simulation model.

simulation environment. Therefore, the data flows from the SD model to the DES models through the interface variables. In Case No. 2, the purpose is to study the influence of operational level variables on the global SD model. Next step involves identifying the interface variables and the interactions direction and then variables are described using the mathematical formalism. 4.3. Formalism of DES and SD models Simulation modeling is not accomplished by directly writing out a dynamic system structure, but indirectly, by using system specification formalism. System specification formalism is a shorthand for specifying a system [26]. In this paper, formalism is the method used to describe the simulation modules to the Executer. For the purpose of the integration of DES and SD models, formalism must specify the following properties:

Formalism of DES and SD

1. 2. 3. 4.

Synchronization of Simulation Clocks

Executer

Model type: whether it is DES or SD. Input variable (receiver) and source of the input variable. Output variable (sender) and source of the output variable. Synchronization function that describes the simulation time management and the interfacing variables. The aforementioned four requirements are summarized in Eq. (1).

Implementation Input Fig. 2. Hybrid simulation framework.

Hybrid DES&SD model ¼ ðI; O; M; SÞ

ð1Þ

Where: ▪ I: Set of inputs ▪ O: Set of outputs

Please cite this article as: H. Alzraiee, et al., Dynamic planning of construction activities using hybrid simulation, Automation in Construction (2014), http://dx.doi.org/10.1016/j.autcon.2014.08.011

H. Alzraiee et al. / Automation in Construction xxx (2014) xxx–xxx SD variable state update behaviour

▪ M: Set of modules (DES or SD) ▪ S: Synchronization process (time and interfacing) The generic Eq. (1) provides a base for deriving Eq. (1.1) to represent either DES or SD model to the simulation Executer. The proposed simulation Executer requires three sets and two functions to describe DES or SD modules (m), as shown in Eq. (1.1). m ¼ ðmt ; vall ; min ; mou ; T b Þ

5

DES_SD Hybrid State after variables interfacing

State update

DES variable state update behaviour

ð1:1Þ

Where: ▪ ▪ ▪ ▪ ▪

mt: Module type (SD or DES) vall: Set of all interface variables in the module min: Set of module input variables (receiver), described by Eq. (1.2). mou: Set of module output variables (sender), described by Eq. (1.3). Tb: Time point where the interfacing of variables occur

n o min ¼ ðm; vi ; ms ; opms ; md Þ=m∈M; vi ∈V M ; ms ∈M; opms ∈ outportsall; md ⊂vall

ð1:2Þ

Tb1

Tb2

Tb3

Tb4

Tb5

Tb6

Tb7

Simulation length L Fig. 4. Synchronization of DES and SD models using time bucket.

points, rather than at the occurrence of events. Tb is set to be equal to the SD model STEP TIME as shown in Eq. (2) and Fig. 4. Tb ðhybrid modelÞ ¼ SD model STEP TIME

ð2Þ

Where: ▪ vi: Input variables to module m ▪ ms: The module source from which input variables are imported ▪ opms: The output port in ms from which the input variables are imported to m module ▪ md: Describes the variables in m that need to use the input variables from ms. (md describes the variable input vi input port (ipm) in m, the variables in m need to use vi and the time point in the simulation clock where the interfacing of variables occur) ▪ M: Describes the simulation model (DES and SD) in the hybrid environment (hybrid model) ▪ VM: All variables in model M ▪ outportsall: Set of all output ports in m ▪ vall: All variables in m

mou ¼

n o  m; op; ov; =m ∈ M; op ∈ outport m ; ov ∈ vall

ð1:3Þ

Where: ▪ op: Output port in m ▪ ov: Output variable given through op 4.3.0.1. Simulation clock synchronization. Time management or “synchronization” means the execution of events that occur in distributed simulation in a specified order that ensures repeated executions of a simulation produces exactly the same results under the same inputs [27]. The simulation clock of the DES method advances in a different fashion than the SD method. In DES, the simulation time is driven by the occurrence of events and subsequently system states are updated. Events usually occur at unequal time intervals. In SD, the simulation clock advances at equal time intervals and system states are updated at the end of each time interval. The proposed synchronization method utilizes the concept of Time Bucket [28,29]. It divides the hybrid simulation time of length L into equal time intervals called Time Buckets (Tb). At the end of these time intervals, interfacing of simulation models takes place. Tb should be small enough to capture any significant changes in system state and large enough to discard unnecessary overhead computations. The rationale behind using the Time Bucket is that SD updates states at equal and pre-known time intervals while DES updates states at the occurrence of events. Those events generally occur at unequal time intervals; therefore, it is easy to trace states of the simulation model at stipulated time

At the start of the simulation time length L, the proposed hybrid simulation method initializes the simulation clocks of DES and SD engines, as well as, the variables data. Now, DES_SD code is positioned to advance the simulation clock to Tb1, where, Tb ¼ Tb1 ¼ Tb2 ¼ …:Tbn

ð3Þ

At the end of Tb1, the interface of the variables and their respective data exchange occurs between DES and SD as shown in Fig. 4. The data flow direction between the two models is based on the structure of the hybrid model through the interface variables. Upon completion of this process, the hybrid DES_SD advances the simulation clock to Tb2 and again at the end of the Tb2 interfacing of variables occur. The events of the synchronization process that take place between Tb1 &Tb2 are illustrated in the algorithm shown in Fig. 5 [30]. The developed algorithm works as follows. Initially, for the DES engine to start advancing the simulation time, a condition such as, the required resources and entities should be available at the start of TB1. Now the simulation is in position to start advancing at the beginning of TB1, if entities seize the required resources, then all data of active resources and entities in the simulation model are read and saved, otherwise, idle resources data are read and saved. If the process involving the active resources has not finished processing the entity at the end of TB1, then pause DES simulation clock advancement, save all data and perform DES and SD modules interfacing. Otherwise, eliminate saved resources and entity data and return to re-allocate the next process and its entities, attributes and resources. In the DES model, events having occurrence time less than the TB1 finish their processes before the interfacing of variables can take place, hence their data are not captured in the next earliest scheduled interfacing, but their effects are propagated to the second event. Therefore, in order to avoid events that start and finish before the end of the TB, it is advised to set the SD TIME STEP less than or equal to the lowest expected event time in the model. After the interfacing is accomplished, all saved data at the end of TB1 (interface time point) of active or idle resources, are used by the DES engine for the next round of computations that begins by the commencement of TB2 and continue in the same sequence explained in TB1. The time point between the end of TB1 and start of TB2 is the point where the simulation clock resumes the progressing of model simulation. The algorithm continues until the model reaches the initially set simulation time of length L and then terminates the simulation run.

Please cite this article as: H. Alzraiee, et al., Dynamic planning of construction activities using hybrid simulation, Automation in Construction (2014), http://dx.doi.org/10.1016/j.autcon.2014.08.011

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H. Alzraiee et al. / Automation in Construction xxx (2014) xxx–xxx Start Tb+1 Resources and Entities are available at start of Tb

Advance simulation clock

No

Do entities seize resources

Idle resources

Yes

Active resources

Read and save resources ID Read and save resources idle state Read and save queue length of Idle resources Read and save times

Read and save resources ID Read and save entity ID Read and save queue ID Read and save queue length Read and save start processing time Read and save server time Read and save served resources time

Yes Is idle state of resource changed before Tb end?

No

Yes Is entity processing completed before end of Tb?

No

Eliminate saved resources data

Eliminate saved resources and entities data

Pauses DES simulation clock advancement, save all data stated and perform models interface

Pauses DES simulation clock advancement, save all data stated and perform models interface

Interface Completed Resume simulation starting from all saved values at end of Tb with considering the new values resulted from the models interface

Clear all saved data

End

Fig. 5. Algorithm to control advancement of simulation clock.

4.4. Executer The Executer is the VB code developed to integrate DES and SD models through utilizing the components of the developed hybrid simulation method. It focuses on integrating and controlling the hybrid simulation model. The Executer interacts with DES and SD simulation engines to facilitate the integration process based on the developed mathematical formalism, the synchronization protocol, and the discrete simulation clock algorithm. The Executer is responsible for performing the following tasks: 1. Providing the user interface layer that allows inputs and outputs as required by the user. 2. Providing models data import–export management. The selected interface variables that are designated to receive or share their values are specified in the Executer. 3. Implementing the DES simulation clock-advancing algorithm. The aforementioned components of the simulation method are utilized and integrated to develop the hybrid simulation system shown in Fig. 6. The DES and SD simulation models are developed using DES (Stroboscope) and SD (Vensim) engines respectively, the lower part of Fig. 6. The input information for the hybrid simulation model, such as simulation run length, definition of the interface variables, hybrid model structure etc. are entered through the interface layer. As soon as the simulation engine advances the simulation time and reaches the defined time step, synchronization of the interface variables takes

place based on the developed algorithm and the messages exchange sequence. The structure of the hybrid simulation model plays the main role in executing and progressing the simulation model computation. For instance, if the SD model is designed to act as a global model, and certain variables within the SD model need to be computed by the operational level model (DES) and exported to the SD model, then the DES engine will compute those variables, dump them into a spreadsheet from where the SD model will import and receive those variables at every time step. Finally, the outputs of the simulation model such as project completion duration, productivity, and cost are exported into PDF format for end user analysis. 5. Implementation The proposed hybrid simulation method is implemented in two ways, using two different case studies. 1- In the first case study, the purpose is to investigate the impact of the surrounding factors on a CPM-network. This means, using the SD model to quantify certain factors, then impact the CPM-network with those factors to monitor the changes on duration and productivity, and, 2- In the second case study, the logic is inversed by using another case study (earthmoving operations). The whole project is modeled using the SD model except at the operational level where DES modeling is used to quantify the operational variables. The productivity of each

Please cite this article as: H. Alzraiee, et al., Dynamic planning of construction activities using hybrid simulation, Automation in Construction (2014), http://dx.doi.org/10.1016/j.autcon.2014.08.011

H. Alzraiee et al. / Automation in Construction xxx (2014) xxx–xxx

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Interface Layer Simulation data Interface variable Simulation length Hybrid structure selection

Project performance Total cost

DES_SD Executer

20 M Start

15 M

Tb +1

Resources and Entities are available at start of Tb

$

Start advance simulation clock

10 M

No Do entities seize resources

Excel Application

Where: I: Set of inputs O: Set of outputs M: Set of modules (DES or SD) S: Synchronization process (time and interfacing) m = (m t, v all, m in, m ou, T b)

Executer

SD Model

DES Model

(1)

Active resources

(2) (3)

Idle resources

Read and save resources ID Read and save entity ID Read and save queue ID Read and save queue length Read and save start processing time Read and save server time Read and save served resources time

(4)

Yes Is ideal state of resource changed before Tb end ?

No

Yes Is entity processing completed before end of Tb ?

(5) (6) (7)

Eliminate saved resources data

Keep saved resources data

Productivity Project Cost Schedule

5M

0

25

50

75

100 125 150 175 200 225 250

Time (Hour)

Actual releassed production

Eliminate saved resources and entities data

No

450

(9) (10)

0

Total cost : 1 600

Pauses DES simulation clock advancement , save all data stated and perform modules interface

(8)

Tb1

Read and save resources ID Read and save resources idle state Read and save queue length of Idle resources Read and save times

Yes

Resume simulation starting from all saved values at end of Tb

Clear all saved data

m3/Hour

Hybrid DES_SD = (I, O, M, S)

300

150

End

Formalization

Synchronization

Sim clock Adv. Algorithm

00

25

50

75

100 125 150 175 200 225 250

Time (Hour) Actual releassed production : 1

Specify Hybrid Structure and Interface Variables

Strategic/Context layer

Operational Layer Dumped

Loader Idel

Rocks

>0 , 1 1

Rocks to Move

DES Operational level

>=38 , 38

38

Load

Haul Triangular [4.9,5.6,5.7]

Uniform [2,2.2]

Dump Uniform [1.4,1.6]

SD Strategic and context level

>0 , 1

Truck Wt.

1

Return Uniform [3.4,3.6]

DES Engine

SD Engine Fig. 6. Architecture of the hybrid simulation system.

operation (excavation, hauling, and dumping) is inputted through the interface variables into the SD model to get impacted by the project dynamics and subjective factors. 5.1. Case Study1: engineering drawings production by a design firm The proposed planning method is implemented as described in the methodology section. An SD model is developed to capture the effect of the surrounding factors and dynamics such as limitation of skilled labor, rework cycle, and schedule pressure (the paper focuses on the idea itself rather than explaining the development of the SD model structure). The SD model creates a dynamic framework that exhibits the classic characteristics of a project's dynamics. Within this framework, a CPM-based network is developed in a DES environment to describe the job logic and compute the project's completion duration considering uncertainty. The implementation of the case study in the simulation environment is demonstrated in Fig. 7. The project scope is decomposed into smaller units to develop the work breakdown structure (WBS), from which activities are identified. Each activity's duration and cost are inputted as probability distributions. ProbSched is utilized to develop the CPM network [31], as shown in Fig. 7. The ProSched is a probabilistic scheduling package that uses Stroboscope as its engine

and Microsoft Visio as its Graphical User Interface. ProbSched allows the definition of CPM networks where the cost and duration of each activity can be defined probabilistically. ProbSched produces graphical output to indicate the criticality of each activity and statistics of the early and late times and floats of each activity and the project. The second component of the implementation involves developing SD model responsible on modeling the subjective factors. The SD model is developed using Vensim Software Package from Ventana Systems, Inc [32]. The Gantt chart shown in the figure is a sample output of the hybrid model and represents the schedule network of activities after impact by the SD model. 5.1.1. Data of Case Study1 A design firm was hired by a client to produce a variety of engineering drawings for expansion of Oil Sands existing facilities. The engineering drawings were: 1) civil (Task1); 2) mechanical and structural (Task2); 3) electrical (Task3), and 4) piping (Task4). The designs were prepared by different engineering departments within in the same firm. A resources loaded schedule that depicts the logical sequence among the four activities was developed using the traditional methods and provided to the client. The main purpose of the schedule was to estimate the completion durations of the project, and track the progress of

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H. Alzraiee et al. / Automation in Construction xxx (2014) xxx–xxx

Gantt Chart of Simulated CPM network after Impacted by SD model

Discrete Simulation Engine (CPM) CPM or PDM

Project Policies and information

Total Rework

Percentage of Compacted Soil

Activities

Rework Process Rate Duration

Error Generation Rate

Spread Soil

Rework process rate

Rework Error Rate

Rework rate

Avg, QP Duration

Quality Check Process Rate

Scope Task is Done C

Durations Costs

Scope Task Start Flag C

Resource

Quality Process Rate

Perceived Quality Rate

Scope Start Percentage of Compaction

Scope Task is Active C

Soil Compacted and ready for Quality Check

Soil to Compact Compaction Rate

pread

Productivity Rate

Total Soil Compacted and Ready for Quality Check

effect of schedule pressure on productivity





Activities Information

Final Work Completed

Total Project Work of Soil, Hauled, Dumped, Spread, and Compacted



Max Compaction Rate

Perceived Rework Rate

Effect of Schedule Pressure on Productivity Lookup

SD Simulation Engine (SD Model) Fig. 7. Layout of the proposed planning system.

Table 2 Case study data. Task name

Task scope in units

Triangular probability dist. of task duration in weeks

Task1(T1) Task2(T2) Task3(T3) Task4(T4)

40,000 50,000 18,000 100,000

20, 20.4, 20.9 20, 20.2,20.6 10, 11.7, 12 39.9, 40.5, 40.8

phenomenon is well known in construction work execution and is depicted in Fig. 8. 5.1.2. Models development, result and discussion The developed hybrid model consists of a CPM-based network (Fig. 9) and SD model. The purpose of the CPM network is to capture the operational level parameters such as durations and the logical sequence of the activities, while the SD model will capture the dynamics generated from the model's variables due to internal and external interactions. The CPM network is compiled in DES engine (Stroboscope) using a ProbSced add-on application. The activity start time is similar to the activity start date in the conventional schedule network; 1

Performance Factor

completed drawings. Based on the CPM schedule, the project was expected to be completed in 70 weeks; however, the actual project completion duration exceeded this initial estimate by 40% (28 weeks). Therefore, this was a base for a good case study to investigate, model, and analyze. The data collections also involved understanding the circumstance surrounding the projects, such as maximum available manpower, overtimes, management policies, and schedule pressure level as this helps in understanding the dynamics of the project system. The firm used units system to quantify the efforts needed to finish a single drawing (e.g., a drawing requires 2000 units of work to complete). The productivity of individuals is measured by the number of drawings completed and checked per week. The data of the four tasks is shown in Table 2. Each Task duration was estimated using three points: optimistic, most likely, and pessimistic. The maximum available skilled workforce dedicated to this project was 160. It was recorded by the project manager that maximum production per drawing is reached when 20% of the drawing is complete and declined in the last 20%. This

20%

Percentage of drawing 80% Completion Fig. 8. Planned work profile.

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Gross productivity½task ¼ normal productivity½task effect of morale on productivity½task effect offatigue on productivity½task effect of schedule pressure on productivity½task Units : dwg=person=week ð5Þ

Task 1 Task 2 Task 3 Task 4

Undiscovered Rework½task ¼ ðgross completion rate½task ð1‐work quality½taskÞ –rework discovery½task; 0Þ:

20 40 60 80 Simulation Time (weeks) Fig. 9. Gantt chart of the tasks before simulation.

however, since the activities are bonded to the simulation time length, we will refer to the start date as the start time as shown in column (1) in Table 3. The durations for every activity are inputted into the CPM-discrete simulation network as a triangular distribution. The model ran for 500 cycles and the average duration for each Task were computed as shown in column (2) in Table 3. The total completion duration as computed by the CPM network in the DES environment was 70.9 weeks. It was noticed that the difference between the deterministic and stochastic total project duration is minor, and this was attributed to the minor differences in the three estimated durations of the activities. The next stage was developing an SD model to represent the project's dynamics based on the causal loops diagram shown in Fig. 1. These loops are rigorously researched and cited in literature by many researchers [21,23]. The developed SD model is composed of four modules (workflow, rework, quality, and labor demand). The workflow module describes the workflow from execution to completion. A rework cycle module is added to account for work that does not pass the quality standards and needs to be reworked. The scope of rework is returned to the initial stock for further processing. The schedule pressure resulting from low productivity and increasing rework is captured as well. The strategy of the firm in addressing mounting schedule pressure was by hiring likely additional workforce or considering overtime for the current staff. The SD model structure was developed in such a way to capture the effects of schedule pressure, fatigue, overtime, and rework cycle on quality of work and project completion duration. All of these interactions are captured within the causal–effect loops that depicts the dynamics specified in the SD model framework (Fig. 1). Samples of the equations used are shown in the equations numbered 4 to 9. Work Completed Correctly½task ¼ ðgross completion rate½task  work quality½task; 0Þ Units : dwg

ð4Þ

ð6Þ

Rework discovery½task ¼ MAXð0; MINðUndiscovered Rework½task= TIME STEP; UndiscoveredRework½task= rework discovery time½task þSUMðdownstream rework disc½downstream!; taskÞÞÞ Un3its : dwg=week ð7Þ

Work remaining½task ¼ MAXð0; TASK DEFINITION½task ‐reported work complete½taskÞ Units : dwg

ð8Þ

Reported work complete½task ¼ Work Completed Correctly½task þUndiscovered Rework½task Units : dwg ð9Þ

The SD model was simulated, and a sample of the results is shown in Fig. 10. The project completion duration estimated by the discrete simulation model had been extended from 70.9 weeks to 92 weeks, Fig. 10a. This represents an additional of 32% to the project planned completion duration. In addition, it can be observed in the figure that the start of Task 4 was delayed from the 30th week to the 48th week. This delay is attributed to the interesting pattern observed in Fig. 10c– d during this period. In the figure, the quality standard of the executed drawings was degraded in the period from the 25th week to the 48th week. The poor quality evident is mainly due to the delays of starting Task 4, which demanded more resources than was planned. Consequently, limited resources and significant delay increased the schedule pressure; therefore, a higher productivity rate was required from the overstretched resources. Increasing the productivity rate beyond the allowable limit caused some drawings to be finished not per accepted quality standards. Therefore, it can be observed in Fig. 10d that the number of the drawings that need to be reworked has increased to the maximum level between the period of the 30th week and the 45th week.

Table 3 Simulation data inputs for the CPM–DES schedule. Task Name

Task start time weeks(1)

DES Simulated average duration in weeks (2)

Completion milestones in Weeks (3)

Predecessor required to start (4)

Productivity of engineer drawing/week (5)

Task1 Task2 Task3 Task4

0 20 40 30

20.5 20.35 11.4 40.90

20.5 40.35 51.4 70.90 70.90

– T1 T1,T2 T1,T2

20 25 15 25

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60,000

task is active[task]

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2

2

2

2

2

2

2

2

45,000

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2

30,000 TASK2

4

15,000

4 12

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TASK3

1234

25

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75

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234

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TASK4

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34

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34

3

a) Gantt chart of completed Tasks

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4

50 60 Time (Week)

70

Cumulative Of Completed Work Units Per Task[TASK1] : Run1 Cumulative Of Completed Work Units Per Task[TASK2] : Run1 Cumulative Of Completed Work Units Per Task[TASK3] : Run1 Cumulative Of Completed Work Units Per Task[TASK4] : Run1

100

41

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80

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100

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dwg dwg dwg dwg

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b) Cumulative of completed work units per Task 4,000

1 1

Units

1 1 2 34

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c) Quality of executed drawings

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3,000

4

123

d) Undiscovered rework

4

Units

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34 1

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3

6,000

2341

50,000

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1,500 0

25,000 1

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234 2341

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34 1

341

30

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4 4 4 4 12 3 1 2 3 1 2 3 1 2 3 1 2 3

50 60 Time (Week)

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1

4 1 23

4

0

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90

100

1

0

1

1

10

1

20

30

40

50 Time (Week)

60

70

80

90

100

f) Accumulated completed units

e) Productivity per Task

Fig. 10. Sample of the simulation model outputs.

This added extra scope of rework increased the initial scope of work; consequently, increasing the schedule pressure, overtime demand, and fatigue level. The project's execution exhausted 50% of its planned duration while the actual productivity was not as perceived at the planning stage. These dynamics triggered the causal–effect loops that control the workforce, thus demanding additional workforce. However, at this stage, the maximum available resources had been attained. The positive influence of this loop was frozen, which caused other loops of negative influence to occur, such as the schedule pressure loop to build up. Higher schedule pressure, constrained by maximum resources triggered the need for overtime as a policy to increase productivity, Fig. 10.e. Overtime is associated with labor fatigue (physically and mentally), and consequently, this increased the flaws in the drawing production (Fig. 10d). The accumulated impacts of those factors extended the project duration

to 92 week (32% higher than planned). The final accumulation of the executed work is shown in Fig. 10f, which shows an s-curve behavior. Based on these outcomes, the causal–effect loops of the model should be reviewed and revised polices should be considered to address the negative results observed in the outcomes. A remedy to the negative impacts of certain factors in the model can be attained by tracing the problematic loops and minimizing their effects as well as maximizing the impact of the favorable loops. 5.2. Case Study2: earthmoving operations 5.2.1. Case study description In the second case study, the earthmoving operations involved in a dam construction were modeled and simulated using the proposed

Table 4 Scope of work. Soil type & layers

Stage 1 m3

Stage 2 m3

Stage 3 m3

Loose density (t/m3)

Bank density (t/m3)

Load factor %

Total of soil m3

Rock Granular Moraine Total

192,700 14,500 29,200 236,400

3,209,400 286,500 555,900 4,051,800

1,602,900 139,000 269,900 2,011,800

1.66 1.72 1.66 1.6

2.73 1.93 2.02 2.4

80 90 100 100

5,005,000 440,000 855,000 6,300,000

6.3 million m3 Excavation (river Bed)

1,038,000

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11

Table 5 Fleet configuration and characteristics. Hauled Material

Hauler Model

Loader Model

Hauled Soils (ton)

Load activity Time Dist. (m)

Haul activity Time Dist. (m)

Dump activity Time Dist. (m)

Return activity Time Distribution (m)

Rock Moraine Granular River Bed Soil

777D 773D 769 C 777D

992G 990 SII 988F 375L

81.67 45.82 34.36 51.41

(3.94, 4.15, 4.57) (3.01,3.2, 3.32) (2.3, 2.42, 2.5) (4.26, 4.48, 4.93)

(4.3, 4.53, 4.98) (19.47, 20.5, 22.55) (30.6, 32.34, 35.57) (5.32, 5.6, 6.16)

(1.9, 2.2) (1.6, 1.9) (1.3, 1.5) (1.6, 1.9)

(3.17, 3.34, 3.67) (16.71, 17.59, 19.35) (25.85, 26.51, 29.16) (2.86, 3.01, 3.31)

dynamic planning [33]. The scope of the work had two main parts. The first was excavating and preparing the riverbed for the dam's foundation, while the second was backfilling three types of soils to serve as the main dam structure in restraining the water. The scope of work of excavation from riverbed was to remove, haul, and dump 1.038 million m3 of soil. Thereafter, backfilling and compacting 6.3 million m3 of three soil types: 1) compacted moraine (clay) 2); granular (sand and gravel); and 3) rock, as shown in Table 4. The backfill operations involved processes such as loading, hauling, dumping, spreading, and compacting of the soil. 5.2.2. Fleet configuration and duration of operations The fleet configuration used to execute the project scope is shown in Table 5. Information related to equipment was obtained from the manufacturing specification manual of Caterpillar [34]. The fleet of equipment included three types of haulers (777D, 773D and 769C) served by three types of loaders (992G, 990SII, and 988F), respectively. The haulers' travel times under loaded and unloaded conditions corresponding to certain speeds were calculated by using manufacturer's charts (Rimpull–Speed–Gradeability and Brake Performance Charts), total resistances, and road segment lengths. Duration times needed by loaders to load a specific truck were calculated using loader specification charts and tables. 5.2.3. Elements of simulation model As shown in Table 6, the case study is composed of elements related to operational level and elements related to strategic level. The project behavior mainly resulted from those elements and their interactions. At the operational level, the excavation operation involved excavating, loading, hauling, and dumping processes while the soil backfilling operations involved loading, hauling, dumping, spreading, and compacting processes. From a policy and strategic perspective, perceived productivity, weather, overtime, cut depth, road condition and others were critical elements. The elements, when modeled and simulated, are expected to generate the real behavior of the operations in the virtual world. The classification into operational/strategic and selection of simulation method is performed based on the criteria presented in the background section. 5.2.4. Developing DES models Stroboscope [35] is a simulation language used to develop generalpurpose DES models. Processes such as excavating, loading, hauling, dumping, spreading, and compacting are modeled using DES. Ten DES simulation models were developed for excavation and backfilling operations. The excavation DES model computes the Max Excavation Rate

and the Max Dumping Rate while the backfilling DES models compute the Max Dumping Rate, Max Spreading Rate, and Max compaction Rate. Those five variables are used as input in the SD global model, and later in the synchronization implementation which will be called “interface variables”. The outcomes of the ten discrete simulation results are shown in Table 7.

5.2.5. Developing the SD model 5.2.5.1. Model boundary. An essential step in developing the SD model is defining the model's boundary. This boundary involves selecting the variables that generate the behavior of interest as specified by the model's purpose. Variables in the model are classified as endogenous, exogenous, and excluded. Endogenous variables are the main concern of all model variables. They are variables in a causal–effect structure whose values are determined by the states of other variables in the system. Exogenous variables are from outside of the model, and unexplained by the model's feedback structure. They are involved in a causal–effect structure whose values are independent from the states of other variables in the system. Variables categorized as excluded variables are cautiously not included in the structure of causal–effect feedbacks. The model's boundary of the developed SD model is summarized in Fig. 11.

5.2.5.2. Stocks and flows diagram. Dynamic behavior in SD is raised due to the principle of stock or level. As the name implies, stock represents a variable state resulting from decisions. Stock is an accumulation characterizing the system state, and it generates information upon which decisions and actions are based and accumulated. Stock changes only through flows, and creates delays in the model by accumulating the difference between inflow to and outflow from the stock. Stock: 1) has a memory; 2) changes the time shape of flow; 3) decouples flow; and 4) creates delays. Finally, stock is modeled by the mathematical integration of the sum of the flows coming in to the stock and the flows dispatched from the stock. On the other hand, flow represents actions or variables that influence the stock level or accumulation. Decoupling the rate (flow) from the system, stock becomes the source of disequilibrium in system dynamics [36]. The SD model is composed of four modules: (1) workflow module; (2) schedule pressure module; (3) rainfall and road condition module; and (4) cost module. Each module is responsible for modeling the behavior of the element that it represents. For instance, the workflow module is responsible for modeling the interactions of the variables that describe the job-logic sequence at execution stage.

Table 6 Summary of the simulation model elements. Operations

Operational level

Strategic/context level

Excavation

Excavation, Loading, Hauling, dumping, and Return

Cut depth, schedule pressure, road condition, operator skill, soil type, job efficiency, weather condition, and overtime. Schedule pressure, road condition, overtime, rework cycle, soil type, operator skill, weather condition, and overtime. SD

1

Rock11, Granular12, Moraine13, Rock21, Granular22, Moraine23, Rock31, Granular32, Moraine33 Simulation Method 1

Loading, Hauling, Dumping, Return, Spreading, and Compacting DES & traditional method

Rock 11 means, first layer & first stage (refer to Table 4).

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H. Alzraiee et al. / Automation in Construction xxx (2014) xxx–xxx

Table 7 DES model outputs.

Dump (m3/h)

Spread (m3/h)

Compact (m3/h)

Process

Excavation

Rock 11

Granular 12

Moraine 13

Rock 21

Granular 22

Moraine 23

Rock 31

Granular 32

Moraine 33

Scope of work (m3)

1,038,000

192,700

14,500

29,200

3,209,400

286,500

555,900

1,602,900

139,000

269,900

Haulers

7

8

10

8

8

10

8

8

10

10

Loaders

2

2

1

1

2

1

1

2

1

1

Bulldozers

2

3

1

1

3

1

1

3

1

1

Compactors

0

3

1

1

3

1

1

3

1

1

Max Min Average St. Deviation Max Min Average St. Deviation Max Min Average St. Deviation Duration (hour)

1421.33*/1367.39** 1222.65*/1200.56** 1320*/1284.52** 34.40*/28.27** – – – – – – – – 808.33

1462.43 1323.11 1393.21 35.20 1462.56 1323.05 1393.21 35.16 1464.98 1321.41 1393.21 36.17 138.44

216.87 157.30 187.21 15.37 216.94 157.18 187.43 15.275 218.26 155.15 187.43 16.25 77

347.88 260.10 304.55 22.19 343.95 264.07 304.55 20.32 341.79 266.14 304.55 19.32 96.79

1462.90 1323.08 1393 35.36 1456.75 1329.15 1393.21 32.16 1458.77 1327.09 1393.21 33.67 2303.70

221.28 158.10 190 16.88 221.65 158.26 190.32 16.67 221.66 158.06 190.68 16.63 1054.72

349.82 262.01 306.29 22.06 349.05 262.18 306.29 22.06 351.71 260.42 306.29 23.06 1814.44

1466.84 1319.26 1393.08 37.25 1462.94 1323.15 1393.08 35.25 1466.72 1319.79 1393.08 37.25 1151.85

223.70 156.76 190 17.88 221.81 158.47 190.23 16.88 221.83 158.11 190 16.88 730.42

424.90 337.55 381.49 22.13 434.98 327 381.49 27.13 430 331.00 381.49 25.13 707.63

Total backfill duration = 8504 h. By considering 50% overlapping, duration = 4620 h. * Excavation rate of riverbed, ** Dumping rate of Excavation of riverbed.

The workflow module is composed of five structures: 1) loading– dumping; 2) spreading; 3) compaction; 4) rework; and 5) released productivity as shown in Fig. 12. Those structures describe the real work execution sequence. The SD model is initialized at “Soil to Haul” stock. The initial value of this stock is the total scope of work (6.3 million m3), modeled mathematically, using the subscript control feature in Vensim to distinguish the different soil types. Then the scope is processed at the “Dumping Rate” flow that represents the impacted fleet dumping productivity. The “Max Dumping Rate” variable shown in Fig. 12 is the interface variable that receives and sends values from DES to SD. The exported DES variable (sender) represents the ideal rate. This variable is impacted by the causal–effects loops in the SD model to deliver near actual rates of production. The treated rate ends up in “Net Dumping Rate” (receiver variable). After processed by “Dumping Rate,” the scope is accumulated in “Soil Dumped” stock. Now, soil is ready for the next stage, which is the spreading and thereafter compaction. The same procedures are followed for “Net Spreading Rate” and “Net Compaction Rate” variables. The quality of compacted soil must be checked based on the

5.2.6. Identifying the interface variables The interface variables are the interface points between the DES and SD simulation models. Those variables are responsible to ensure that

Impact of scope change

Excluded

Environmental Impcat

Exogenous

Project deadline

Safety

design standards before the final release. Thus, the compacted soil is stocked at “Soil Compacted and Ready for Quality Check” stock. The soil that passes the compaction test is processed through the “Productivity Rate” flow, and the faulty compacted soil is passed to the “Rework” stock for further re-work to assure the required quality. The summation of the flow's “Productivity Rate” and “Rework Rate” represents the actual released work productivity. The Gantt chart of workflow execution is shown in Fig. 13. As observed in the chart, operations are scheduled with 50% overlapping between scopes. The excavation scope is not shown in this chart because excavation operation involved only “excavation” and “hauling–dumping” while the backfilling involved “hauling–dumping”, “spreading”, “compaction”, and “quality check”. Thus, the structure of the SD for excavation scope should be different from the SD model of the backfilling scope.

Operator skill Secondary error

Endogenous

Learning effect Overtime impact scope

Actual productivity Project progress Overtime Weather Schedule pressure Quality Forecasted Error rate

Cash flow constraints

production Project progress Road condition Grade of cut Depth Cut Workforce

Weather impact

Equipment maintenance

Soil type

Information flow

Workforce shortage

Fig. 11. SD model boundary.

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Rock11 Granular12 Moraine13 Rock21 Granular22 Moraine23 Rock31 Granular32 Moraine33

13

points that act to receive DES input from sender variables and deliver them to receiver variables in the SD model. 6. Results and analysis

0

1550

3100 Time (hr)

4650

Due to the harsh cold weather at the dam construction location, the project execution was planned in three phases. Each phase starts on April and ends by November. In the project-planning phase, several scenarios can be considered to execute the project. The simulation tools play an essential role in this context to show the discrepancy between the different scenarios and assist the manager in making informed decisions. To test the developed hybrid simulation model, three scenarios were considered as shown in Table 8. Each scenario was inputted to the model and simulated for 6200 h. The initial normal project duration was calculated by adding up the durations of the nine scopes computed by DES models resulting in a total of 8504 h. In the planning stage, and due to structural stability of the backfill layers, it was considered that every two successive activities were overlapping by 50% (the successive

6200

Fig. 12. Gantt chart of soil backfill scope.

hybrid structure functions based on the selected data mapping between the models. Referring to the SD model, Max Dumping Rate, Max Spreading Rate, and Max Compaction Rate shown in the red triangular shapes (Fig. 13) are the interface variables. Those variables are the interface

Compaction and Rework Cycle Loading-Dumping Operation

Rework Process Rate Duration Total Rework

Spreading Operation Error Generation Rate

Rework process rate

Perceived Rework Rate

Scope ID Scope Size

percentage of Dumped Soil

Percentage of Spread Soil

Rework

Percentage of Compacted Soil

Start Scope Task Percentag Spreading

Error Rate

Spread Soil

Total Scope

Rework rate

Avg, QP Duration

Quality Check Process Rate

Scope Task is Done C Scope Task is Done S

Start Task Flag Scope Task is Done D Start Scope Task Scope Task is Percentage Active D Dumping

Scope Task is Active C

Dumping Rate

Spreading Rate





Max Compaction Rate

LHD_DES Model Input Zone

Spreading_DES Model Input Zone



Final Work Completed

Total Project Work of Soil, Hauled, Dumped, Spread, and Compacted

Total Soil Compacted and Ready for Quality Check

Max Spreading Rate

Perceived Quality Rate

Productivity Rate

effect of schedule pressure on productivity



Total Soil Dumped

Max Dumping Rate

Soil Compacted and ready for Quality Check Compaction Rate

Total Soil Spread

Average time

Scope Start Percentage of Compaction

Soil to Compact

Soil Spread

Quality Process Rate

Scope Task Start Flag C

Dumped Soil Scope Task is Active S

Soil to Spread

Soil Dumped

Soil to Haul

Scope Task Flag S



Compaction_DES Model Input Zone

Effect of Schedule Pressure on

Final Project Completion Duration

Total Work Not Done

Schedule Index used to adjust released productivity to be same as the planned

Time Required Forecasted Productivity to Complete



Project is Done

Time needed to reached planned productivity

Perceived Productivity

Restart Fraction Project was Done Fraction Completed

Project Deadline Schedule Pressure Actual Final Released Productivity