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A bi-objective genetic algorithm approach to risk mitigation in project scheduling

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  • KIlIç, Murat
  • Ulusoy, Gündüz
  • Serifoglu, Funda Sivrikaya

Abstract

A problem of risk mitigation in project scheduling is formulated as a bi-objective optimization problem, where the expected makespan and the expected total cost are both to be minimized. The expected total cost is the sum of four cost components: overhead cost, activity execution cost, cost of reducing risks and penalty cost for tardiness. Risks for activities are predefined. For each risk at an activity, various levels are defined, which correspond to the results of different preventive measures. An MIP model and a heuristic solution approach based on genetic algorithms (GAs) is proposed. GAs provide a fast and effective solution approach to the problem.

Suggested Citation

  • KIlIç, Murat & Ulusoy, Gündüz & Serifoglu, Funda Sivrikaya, 2008. "A bi-objective genetic algorithm approach to risk mitigation in project scheduling," International Journal of Production Economics, Elsevier, vol. 112(1), pages 202-216, March.
    • Handle: RePEc:eee:proeco:v:112:y:2008:i:1:p:202-216
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    Cited by:

    1. Pfeifer, Jeremy & Barker, Kash & Ramirez-Marquez, Jose E. & Morshedlou, Nazanin, 2015. "Quantifying the risk of project delays with a genetic algorithm," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 34-44.
    2. Gutjahr, Walter J., 2015. "Bi-Objective Multi-Mode Project Scheduling Under Risk Aversion," European Journal of Operational Research, Elsevier, vol. 246(2), pages 421-434.
    3. Mahsa Parsaei Motamed & Shahrooz Bamdad, 2022. "A multi-objective optimization approach for selecting risk response actions: considering environmental and secondary risks," OPSEARCH, Springer;Operational Research Society of India, vol. 59(1), pages 266-303, March.
    4. Zuo, Fei & Zio, Enrico & Xu, Yue, 2023. "Bi-objective optimization of the scheduling of risk-related resources for risk response," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    5. Nguyen, Trong-Hung & Marmier, François & Gourc, Didier, 2013. "A decision-making tool to maximize chances of meeting project commitments," International Journal of Production Economics, Elsevier, vol. 142(2), pages 214-224.
    6. Pedro Godinho & João Paulo Costa, 2020. "A stochastic model and algorithms for determining efficient time–cost tradeoffs for a project activity," Operational Research, Springer, vol. 20(1), pages 319-348, March.

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