An effective simulation mechanism for construction operations

Cheng, Tao-Ming; Feng, Chung Wei

doi:10.1016/s0926-5805(02)00086-9

Cited by 48 publications

(30 citation statements)

References 9 publications

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“…Several models for simulation the construction operation have been developed (Cheng and Feng, 2003;Lu et al, 2003;Zayed and Halpin, 2001). Furthermore, the efficient and flexible models based on optimization techniques were proposed (Naso et al, 2004;Zayed and Minkarah, 2004;Yan et al, 2008).…”

Section: Methodsmentioning

confidence: 99%

Production Scheduling for Dispatching Ready Mixed Concrete Trucks Using Bee Colony Optimization

Srichandum¹

2010

American Journal of Engineering and Applied Sciences

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Problem statement: This study proposed a systematic model of delivering Ready Mixed Concrete (RMC) that optimizes the schedule of dispatching RMC trucks. Approach: Firstly, the factors that impact the RMC delivery process are analyzed. Secondly, a model based on Bee Colony Optimization (BCO) was developed in order to find the best dispatching schedule that minimize the total waiting time of RMC trucks at construction sites. Results: To demonstrate its efficiency, the BCO algorithm was applied to solve two dispatching RMC problems. The results obtained from the BCO are compared to those achieved from the conventional approaches i.e., Genetic Algorithm (GA) and Tabu Search (TS) algorithm. Conclusion/Recommendations: The experimental results showed that the BCO approach can quickly generate efficient and flexible solutions to dispatch RMC trucks. Furthermore, the obtained results had higher quality solution efficiently and faster computational time than those obtained from the conventional approaches.

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Section: Methodsmentioning

confidence: 99%

Production Scheduling for Dispatching Ready Mixed Concrete Trucks Using Bee Colony Optimization

Srichandum¹

2010

American Journal of Engineering and Applied Sciences

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“…Table 1 presents the basic modeling elements used in CYCLONE and reader may refer to [8] for the details. Since CYCLONE is widely used in the design and analysis for construction operations, several computer simulation systems or programs are developed based on it [5]. Among them, COST (Construction Operations Simulation Tool) was the newly developed one.…”

Section: Cyclone Methodology and Cost Programmentioning

confidence: 99%

“…These researches include applying GA-simulation in the optimization of the pastoral dairy farm management [1], shipping and shipyard layout planning [2], the hard disk drive production scheduling [3], and ready-mixed-concrete trucks dispatching management [4]. Moreover, Cheng and Feng [5] integrated GA and simulation in the development of general purpose simulation system for use in the analysis of construction operations. Similarly, Hegazy and Kassab used GAsimulation technique for resource optimization in construction planning [6].…”

Section: Related Researches For Integration Of Ga and Simulation Techmentioning

confidence: 99%

“…In other word, user does not to input 10 models manually as conventional way. The input parameters for running GA such as population size, generation number, crossover rate and mutation rate are set to 40, 30, 0.5, and 0.05 respectively according to Cheng's and Feng's suggestion [5]. As a result, the best system production rate and unit cost obtained by running new mechanism are 27.574 cycles per hr and $435 per unit for system production rate and unit cost, respectively (final results shown in Table 6).…”

Section: Design Mechanismmentioning

confidence: 99%

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Automatic Design Mechanism for Resource Distribution for Construction Operations Simulation

Cheng¹,

Hsu²,

Chen³

et al. 2004

Proceedings of the 21st International Symposium on Automation and Robotics in Construction

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A planner may use simulation for in the analysis and design of construction operation processes to optimize overall performance of a construction system. Normally, the basic elements used in construction operation process simulation system such as CYCLONE (CYCLic Operation NEtworks) are activities and queues. Activity is used to model the execution of the task work which consumes resources and time to perform. Queue acts as a storage location for resources entering an idle state. In the simulation system, queues have to be created according to the ways of assigning resources to activities. Conventionally, planner creates queue at his/her judgment distinguishing what resources and how many should be allocated to which activity. This paper introduces a Genetic Algorithms (GA) based modeling mechanism for facilitating the automation of modeling resource distribution scenarios.

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“…For instance, AbouRizk and Shi (1994) used a heuristic algorithm (HA) to find the best resource allocation in the simulation system [7]. Several researchers proposed a hybrid model with combining genetic algorithms (GAs) and simulation for optimizing resource allocation regarding minimum unit cost or maximum productivity rate of the simulation model [8][9][10]. Senouci and Adeli (2001) used dynamic neural network to optimize resource allocation subjected to project network and available resource constrains that was neglected in the previous research [11].…”

Section: Introductionmentioning

confidence: 99%

Finding Optimum Resource Allocation to Optimizing Construction Project Time/Cost through Combination of Artificial Agents CPM and GA

Khanzadi

Attar

Bagherpour

2016

Period. Polytech. Civil Eng.

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In order to plan a construction project, computer simulations are frequently used to predict the performance of the operations through simulating the process flows and resource selection procedure. However IntroductionDespite the fact that different robust methods have been developed for project scheduling and planning, in the construction industry, Critical Path Method (CPM) is still the most popular method. The first major cause of this popularity is the simplicity of the CPM. On the other hand, for finding the optimum allocation, planners must test all of the alternate construction technologies and resource allocations, including crew sizes for all of the project activities. In each activity, each of its alternatives cause specific duration and cost. In the complicated projects, with increase in the number of activities and the types of resources, these alternatives increase progressively. Therefore, the effective technique is necessary to analyze and determine the optimal resource allocation to complete a project with the minimum cost or time. Several researchers have used discrete event simulation (DES) to develop such a technique for analyzing the effect of different resource allocations. Examples include evaluating the effect of different resource allocation on the concrete batching operations, [1], earthmoving operations [2], residential construction inspection process [3], precast concrete workshops [4], and tunnel planning [5].Although these simulation techniques could find optimum resource allocation, examination of all resource allocation combinations to determine the best solution is too lengthy. Therefore, sensitivity analysis was proposed by [6] to facilitate such enumerations. However, with increase in the complexity of simulation model and number of available resource combinations, sensitivity analysis becomes an extremely time-consuming process. In this regard, different researchers have used heuristic algorithms (HAs) to efficiently search for appropriate resource allocation under specified objectives. For instance, AbouRizk and Shi (1994) used a heuristic algorithm (HA) to find the best resource allocation in the simulation system [7]. Several researchers proposed a hybrid model with combining genetic algorithms (GAs) and simulation for optimizing resource allocation regarding minimum unit cost or maximum productivity rate of the simulation model [8][9][10]. Senouci and Adeli (2001) used dynamic neural network to optimize resource allocation subjected to project network and available resource constrains that was

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An effective simulation mechanism for construction operations

Cited by 48 publications

References 9 publications

Production Scheduling for Dispatching Ready Mixed Concrete Trucks Using Bee Colony Optimization

Production Scheduling for Dispatching Ready Mixed Concrete Trucks Using Bee Colony Optimization

Automatic Design Mechanism for Resource Distribution for Construction Operations Simulation

Finding Optimum Resource Allocation to Optimizing Construction Project Time/Cost through Combination of Artificial Agents CPM and GA

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