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Bi-Objective Multi-Mode Project Scheduling Under Risk Aversion

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  • Gutjahr, Walter J.

Abstract

The paper proposes a model for stochastic multi-mode resource-constrained project scheduling under risk aversion with the two objectives makespan and cost. Activity durations and costs are assumed as uncertain and modeled as random variables. For the scheduling part of the decision problem, the class of early-start policies is considered. In addition to the schedule, the assignment of execution modes to activities has to be selected. To take risk aversion into account, the approach of optimization under multivariate stochastic dominance constraints, recently developed in other fields, is adopted. For the resulting bi-objective stochastic integer programming problem, the Pareto frontier is determined by means of an exact solution method, incorporating a branch-and-bound technique based on the forbidden set branching scheme from stochastic project scheduling. Randomly generated test instances, partially derived from a test case from the PSPLIB, are used to show the computational feasibility of the approach.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:246:y:2015:i:2:p:421-434
    DOI: 10.1016/j.ejor.2015.05.004
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    2. Osman Hürol Türkakın & David Arditi & Ekrem Manisalı, 2021. "Comparison of Heuristic Priority Rules in the Solution of the Resource-Constrained Project Scheduling Problem," Sustainability, MDPI, vol. 13(17), pages 1-17, September.
    3. 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.
    4. Hazır, Öncü & Ulusoy, Gündüz, 2020. "A classification and review of approaches and methods for modeling uncertainty in projects," International Journal of Production Economics, Elsevier, vol. 223(C).
    5. Ripon K. Chakrabortty & Ruhul A. Sarker & Daryl L. Essam, 2020. "Single mode resource constrained project scheduling with unreliable resources," Operational Research, Springer, vol. 20(3), pages 1369-1403, September.
    6. Öncü Hazir & Gündüz Ulusoy, 2020. "A classification and review of approaches and methods for modeling uncertainty in projects," Post-Print hal-02898162, HAL.
    7. Thomas Felberbauer & Walter J. Gutjahr & Karl F. Doerner, 2019. "Stochastic project management: multiple projects with multi-skilled human resources," Journal of Scheduling, Springer, vol. 22(3), pages 271-288, June.
    8. Saad Muslet Albogami & Mohd Khairol Anuar Bin Mohd Ariffin & Eris Elianddy Bin Supeni & Kamarul Arifin Ahmad, 2021. "A New Hybrid AHP and Dempster—Shafer Theory of Evidence Method for Project Risk Assessment Problem," Mathematics, MDPI, vol. 9(24), pages 1-30, December.
    9. Mohammad Rostami & Morteza Bagherpour, 2020. "A lagrangian relaxation algorithm for facility location of resource-constrained decentralized multi-project scheduling problems," Operational Research, Springer, vol. 20(2), pages 857-897, June.
    10. Paraskevopoulos, Dimitris C. & Laporte, Gilbert & Repoussis, Panagiotis P. & Tarantilis, Christos D., 2017. "Resource constrained routing and scheduling: Review and research prospects," European Journal of Operational Research, Elsevier, vol. 263(3), pages 737-754.
    11. Liesiö, Juuso & Xu, Peng & Kuosmanen, Timo, 2020. "Portfolio diversification based on stochastic dominance under incomplete probability information," European Journal of Operational Research, Elsevier, vol. 286(2), pages 755-768.
    12. Karl F. Doerner & Vittorio Maniezzo, 2018. "Metaheuristic search techniques for multi-objective and stochastic problems: a history of the inventions of Walter J. Gutjahr in the past 22 years," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(2), pages 331-356, June.

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