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Heterogeneous Multi-resource Planning and Allocation Under Stochastic Demand

Author

Listed:
  • Arden Baxter

    (H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332; Center for Health and Humanitarian Systems, Georgia Institute of Technology, Atlanta, Georgia 30332)

  • Pinar Keskinocak

    (H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332; Center for Health and Humanitarian Systems, Georgia Institute of Technology, Atlanta, Georgia 30332)

  • Mohit Singh

    (H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332)

Abstract

We study the capacity planning and allocation decisions for multiple heterogeneous resources, considering potential demand scenarios, where each demand requests a subset of the available resource types simultaneously at a specified time, location, and duration ( smRmD ). We model this problem as a two-stage stochastic integer program and consider two variants for the objective function: (a) maximize the expected reward of demands met over all scenarios, subject to a budget B for resources, and (b) maximize the expected reward of demands met over all scenarios minus the cost of resources. Contributions of this work include (i) a thorough complexity analysis of smRmD and its variants, (ii) analysis of structural properties, (iii) development of various approximation algorithms using the unique structural properties of smRmD and its variants, and (iv) an extensive computational study to explore the ease with which exact and approximate solutions may be found.

Suggested Citation

  • Arden Baxter & Pinar Keskinocak & Mohit Singh, 2023. "Heterogeneous Multi-resource Planning and Allocation Under Stochastic Demand," INFORMS Journal on Computing, INFORMS, vol. 35(5), pages 929-951, September.
    • Handle: RePEc:inm:orijoc:v:35:y:2023:i:5:p:929-951
    DOI: 10.1287/ijoc.2023.1298
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    References listed on IDEAS

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    1. Mete, Huseyin Onur & Zabinsky, Zelda B., 2010. "Stochastic optimization of medical supply location and distribution in disaster management," International Journal of Production Economics, Elsevier, vol. 126(1), pages 76-84, July.
    2. Caunhye, Aakil M. & Zhang, Yidong & Li, Mingzhe & Nie, Xiaofeng, 2016. "A location-routing model for prepositioning and distributing emergency supplies," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 90(C), pages 161-176.
    3. Mehmet A. Begen & Retsef Levi & Maurice Queyranne, 2012. "Technical Note---A Sampling-Based Approach to Appointment Scheduling," Operations Research, INFORMS, vol. 60(3), pages 675-681, June.
    4. Wang, Zekai & Ding, Tao & Jia, Wenhao & Huang, Can & Mu, Chenggang & Qu, Ming & Shahidehpour, Mohammad & Yang, Yongheng & Blaabjerg, Frede & Li, Li & Wang, Kang & Chi, Fangde, 2022. "Multi-stage stochastic programming for resilient integrated electricity and natural gas distribution systems against typhoon natural disaster attacks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    5. Soovin Yoon & Laura A. Albert & Veronica M. White, 2021. "A Stochastic Programming Approach for Locating and Dispatching Two Types of Ambulances," Transportation Science, INFORMS, vol. 55(2), pages 275-296, March.
    6. G Barbarosoǧlu & Y Arda, 2004. "A two-stage stochastic programming framework for transportation planning in disaster response," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(1), pages 43-53, January.
    7. Bakker, Hannah & Dunke, Fabian & Nickel, Stefan, 2020. "A structuring review on multi-stage optimization under uncertainty: Aligning concepts from theory and practice," Omega, Elsevier, vol. 96(C).
    8. Yi, Wei & Ozdamar, Linet, 2007. "A dynamic logistics coordination model for evacuation and support in disaster response activities," European Journal of Operational Research, Elsevier, vol. 179(3), pages 1177-1193, June.
    9. Chang, Mei-Shiang & Tseng, Ya-Ling & Chen, Jing-Wen, 2007. "A scenario planning approach for the flood emergency logistics preparation problem under uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 43(6), pages 737-754, November.
    10. Paul, Jomon A. & Zhang, Minjiao, 2019. "Supply location and transportation planning for hurricanes: A two-stage stochastic programming framework," European Journal of Operational Research, Elsevier, vol. 274(1), pages 108-125.
    11. Arden Baxter & Pinar Keskinocak & Mohit Singh, 2022. "Heterogeneous Multi-resource Allocation with Subset Demand Requests," INFORMS Journal on Computing, INFORMS, vol. 34(5), pages 2389-2399, September.
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