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Logic-based MultiObjective Optimization for Restoration Planning

In: Optimization and Logistics Challenges in the Enterprise

Author

Listed:
  • Jing Gong

    (Rensselaer Polytechnic Institute)

  • Earl E. Lee

    (University of Delaware)

  • John E. Mitchell

    (Rensselaer Polytechnic Institute)

  • William A. Wallace

    (Rensselaer Polytechnic Institute)

Abstract

Summary After a disruption in an interconnected set of systems, it is necessary to restore service. This requires the determination of the tasks that need to be undertaken to restore service, and then scheduling those tasks using the available resources. This chapter discusses combining mathematical programming and constraint programming into multiple objective restoration planning in order to schedule the tasks that need to be performed. There are three classic objectives involved in scheduling problems: the cost, the tardiness, and the make span. Efficient solutions for the multiple objective function problem are determined using convex combinations of the classic objectives. For each combination, a mixed integer program is solved using a Benders decomposition approach. The master problem assigns tasks to work groups, and then subproblems schedule the tasks assigned to each work group. Hooker has proposed using integer programming to solve the master problem and constraint programming to solve the subproblems when using one of the classic objective functions. We show that this approach can be successfully generalized to the multiple objective problem. The speed at which a useful set of points on the efficient frontier can be determined should allow the integration of the determination of the tasks to be performed with the evaluation of the various costs of performing those tasks.

Suggested Citation

  • Jing Gong & Earl E. Lee & John E. Mitchell & William A. Wallace, 2009. "Logic-based MultiObjective Optimization for Restoration Planning," Springer Optimization and Its Applications, in: Wanpracha Chaovalitwongse & Kevin C. Furman & Panos M. Pardalos (ed.), Optimization and Logistics Challenges in the Enterprise, pages 305-324, Springer.
    • Handle: RePEc:spr:spochp:978-0-387-88617-6_11
    DOI: 10.1007/978-0-387-88617-6_11
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    Citations

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    Cited by:

    1. Zou, Qiling & Chen, Suren, 2021. "Resilience-based Recovery Scheduling of Transportation Network in Mixed Traffic Environment: A Deep-Ensemble-Assisted Active Learning Approach," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    2. Aybike Ulusan & Ozlem Ergun, 2018. "Restoration of services in disrupted infrastructure systems: A network science approach," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-28, February.
    3. Yasser Almoghathawi & Andrés D. González & Kash Barker, 2021. "Exploring Recovery Strategies for Optimal Interdependent Infrastructure Network Resilience," Networks and Spatial Economics, Springer, vol. 21(1), pages 229-260, March.
    4. Talebiyan, Hesam & Dueñas-Osorio, Leonardo, 2023. "Auctions for resource allocation and decentralized restoration of interdependent networks," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    5. Almoghathawi, Yasser & Barker, Kash & Albert, Laura A., 2019. "Resilience-driven restoration model for interdependent infrastructure networks," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 12-23.
    6. Almoghathawi, Yasser & Selim, Shokri & Barker, Kash, 2023. "Community structure recovery optimization for partial disruption, functionality, and restoration in interdependent networks," Reliability Engineering and System Safety, Elsevier, vol. 229(C).

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