A fuzzy clustering-based genetic algorithm approach for time–cost–quality trade-off problems: A case study of highway construction project

Mungle, Santosh; Benyoucef, Lyès; Son, Young-Jun; Tiwari, Manoj Kumar

doi:10.1016/j.engappai.2013.05.006

Cited by 96 publications

(50 citation statements)

References 24 publications

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“…Mungle et al [33] identified 25 solutions for the same benchmark problem using fuzzy clustering-based optimization using the MOGA, and the best compromise solution was also identified. In order to demonstrate that the currently proposed methodology can generate more efficient schedules for construction projects, the reported results from Mungle et al [33] were ranked based on the evidential reasoning (ER) approach. The weights of the attributes were assumed to be equal in order to make the comparison valid.…”

Section: Resultsmentioning

confidence: 99%

Time–Cost–Quality Trade-Off in a Broiler Production Project Using Meta-Heuristic Algorithms: A Case Study

et al. 2019

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The global production of broiler meat was forecasted to be 97.8 MT in 2019. The cost fluctuations create risks in production. In order to have an effective management system, process uncertainty must be taken into account. This approach considers the process as an interval with fuzzy numbers and, for managing the risks, it uses the variable α, a parameter determined by the manager in an interval between 0 and 1. Then two algorithms, namely the multi-objective imperialist competitive algorithm (MOICA) and multi-objective particle swarm optimization (MOPSO), were compared and applied. Since the process of production has many activities and each activity has possible options, the process does not have a unique solution. Therefore, the objective function and its assigned weights in terms of time, cost, and quality can be applied to select the best solution from those obtained. A vast amount of uncertainty can be observed, and effective management necessitates dealing with these uncertainty issues. The MOPSO algorithm showed better performance than the MOICA algorithm in this problem. Based on fuzzy logic and influenced by the uncertainty condition (α = 0), time, cost, and quality in the MOPSO and the MOICA algorithms were 1793.8 h, $260,571.7, and 46.66%, and 1792.5 h, $260,585.7, and 51.19%, respectively.

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Section: Resultsmentioning

confidence: 99%

Time–Cost–Quality Trade-Off in a Broiler Production Project Using Meta-Heuristic Algorithms: A Case Study

et al. 2019

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“…The project owner invests money in the project for his own benefit, while the contractor is paid to carry out the project construction work. Because the owner cares more about the project completion time and the construction quality, to reduce the project completion time or improve the construction quality, he may consider higher payments (investments) to the contractor through the provision of incentives or financial support [7]. The owner can control the contractor's decision through his investment to some degree, such as through incentives or financial support.…”

Section: B Assumptionsmentioning

confidence: 97%

“…Song and Liang [4] described the realization of lean construction from a contractor's perspective. From past research, it has been observed that the owner has implemented various contracting methods for construction projects, such as: 1) providing incentives to the contractors for improving the performance on quality [5]; 2) furnishing contractors with financial support to reduce project completion time [6]; and 3) attempting to improve construction quality by making contractors liable for the performance of the facility after project completion [7]. Most studies concerning multiple decision makers have been conducted using scenario simulation [4] or surveys [1].…”

Section: Introductionmentioning

confidence: 99%

A Bilevel Model for Project Scheduling in a Fuzzy Random Environment

2015

IEEE Trans. Syst. Man Cybern, Syst.

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In this paper, a new bilevel model with multiple decision-makers is proposed for a project scheduling problem (PSP) which considers the interests of both the project owner and contractor. In this model, the project owner is considered to be the leader and the contractor, the follower. The project owner has two objectives: 1) to maximize profit and 2) minimize makespan, while the contractor's objective is to minimize cost only. A fuzzy random simulation-based bilevel global-local-neighbor particle swarm optimization technique is proposed to solve the multiple decision-maker PSP (MDPSP). A case study based on the Wanjiakouzi Hydropower Station is used to demonstrate an application of the developed model and illustrate the scope of the MDPSP.Index Terms-Bilevel programming (BP), fuzzy random variables, multiple decision-makers, particle swarm optimization (PSO), project scheduling. NOMENCLATURE Notations for the Mathematical ModelTo facilitate the mathematical description, the following notations are introduced.

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“…Recently, the published researches mostly have focused on the integration of the other affecting issues such as the quality of outcomes, resource leveling and resource constrained with the traditional TCT problem. For instance, Amiri et al, Mungle et al, Zhang and Xing, and Zhang et al discussed the time-cost-quality trade-off in construction projects [11][12][13][14]. Kim et al studied the effect of potential quality lost cost in timecost optimization problem [15].…”

Section: Introductionmentioning

confidence: 99%

A time-cost trade-off model by incorporating fuzzy earned value management: A statistical based approach

Salari

Bagherpour

Reihani

2015

Journal of Intelligent &Amp; Fuzzy Systems

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Time-cost trade-off problems (TCT) are well-known in project management contents. This approach normally applied for scheduling of a project especially where the project should be completed under pre-determined deadline. This paper aims to extend time-cost trade-off (TCT) problems in order to provide a well-organized mechanism for both scheduling and rescheduling processes of a project. The proposed mechanism includes project scheduling which concerns with the TCT problem, monitoring of project performance during execution phase using earned value management (EVM), and also predicting project future performance through statistical modeling. Once the predicted values of project performance indicate the necessity for rescheduling, the initial TCT problem is modified to determine a new mode for the execution of the project. In the proposed model, also several options have been considered with specific time and cost for an individual activity where options indicate different execution methods (EM) for the implementation of the whole project. Furthermore, due to vagueness and impreciseness associated with data of real case projects, the time and cost behavior of each option presumed as fuzzy numbers. The proposed control mechanism can help project managers to take the advantage of a comprehensive model to schedule, control, and reschedule a project through the life cycle. An illustrative case is then presented to successfully demonstrate the application of the proposed approach.

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A fuzzy clustering-based genetic algorithm approach for time–cost–quality trade-off problems: A case study of highway construction project

Cited by 96 publications

References 24 publications

Time–Cost–Quality Trade-Off in a Broiler Production Project Using Meta-Heuristic Algorithms: A Case Study

Time–Cost–Quality Trade-Off in a Broiler Production Project Using Meta-Heuristic Algorithms: A Case Study

A Bilevel Model for Project Scheduling in a Fuzzy Random Environment

A time-cost trade-off model by incorporating fuzzy earned value management: A statistical based approach

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