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

Author

Listed:
  • Erfan Khosravani Moghadam

    (Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering & Technology, College of Agriculture & Natural Resources, University of Tehran, Karaj 31587-77871, Iran)

  • Mohammad Sharifi

    (Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering & Technology, College of Agriculture & Natural Resources, University of Tehran, Karaj 31587-77871, Iran)

  • Shahin Rafiee

    (Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering & Technology, College of Agriculture & Natural Resources, University of Tehran, Karaj 31587-77871, Iran)

  • Young Ki Chang

    (Department of Engineering, Faculty of Agriculture, Dalhousie University, Truro, NS B2N 5E3, Canada)

Abstract

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.

Suggested Citation

  • Erfan Khosravani Moghadam & Mohammad Sharifi & Shahin Rafiee & Young Ki Chang, 2019. "Time–Cost–Quality Trade-Off in a Broiler Production Project Using Meta-Heuristic Algorithms: A Case Study," Agriculture, MDPI, vol. 10(1), pages 1-18, December.
    • Handle: RePEc:gam:jagris:v:10:y:2019:i:1:p:3-:d:300495
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    References listed on IDEAS

    as
    1. Hong Zhang & Heng Li, 2010. "Multi-objective particle swarm optimization for construction time-cost tradeoff problems," Construction Management and Economics, Taylor & Francis Journals, vol. 28(1), pages 75-88.
    2. Ventresca, Mario & Harrison, Kyle Robert & Ombuki-Berman, Beatrice M., 2018. "The bi-objective critical node detection problem," European Journal of Operational Research, Elsevier, vol. 265(3), pages 895-908.
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