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A dynamic mobile production capacity and inventory control problem

Author

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  • Satya S. Malladi
  • Alan L. Erera
  • Chelsea C. White

Abstract

We analyze a problem of dynamic logistics planning given uncertain demands for a multi-location production-inventory system with transportable modular production capacity. In such systems, production modules provide capacity, and can be moved from one location to another to produce stock and satisfy demand. We formulate a dynamic programming model for a planning problem that considers production and inventory decisions, and develop suboptimal lookahead and rollout policies that use value function approximations based on geographic decomposition. Mixed-integer programming formulations are provided for several single-period optimization problems that define these policies. These models generalize a formulation for the single-period newsvendor problem, and in some cases the feasible region polyhedra contain only integer extreme points allowing efficient solution computation. A computational study with stationary demand distributions, which should benefit least from mobile capacity, provides an analysis of the effectiveness of these policies and the value that mobile production capacity provides. For problems with 20 production locations, the best suboptimal policies produce on average 13% savings over fixed capacity allocation policies when the costs of module movement, holding, and backordering are accounted for. Greater savings result when the number of locations increases.

Suggested Citation

  • Satya S. Malladi & Alan L. Erera & Chelsea C. White, 2020. "A dynamic mobile production capacity and inventory control problem," IISE Transactions, Taylor & Francis Journals, vol. 52(8), pages 926-943, August.
  • Handle: RePEc:taf:uiiexx:v:52:y:2020:i:8:p:926-943
    DOI: 10.1080/24725854.2019.1693709
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    Citations

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    Cited by:

    1. Junxuan Li & Chelsea C. White, 2023. "Capacity planning in a decentralized autologous cell therapy manufacturing network for low-cost resilience," Flexible Services and Manufacturing Journal, Springer, vol. 35(2), pages 295-319, June.
    2. Allen, R. Cory & Avraamidou, Styliani & Butenko, Sergiy & Pistikopoulos, Efstratios N., 2024. "Solution strategies for integrated distribution, production, and relocation problems arising in modular manufacturing," European Journal of Operational Research, Elsevier, vol. 314(3), pages 963-979.
    3. Faugère, Louis & Klibi, Walid & White, Chelsea & Montreuil, Benoit, 2022. "Dynamic pooled capacity deployment for urban parcel logistics," European Journal of Operational Research, Elsevier, vol. 303(2), pages 650-667.
    4. Satya S. Malladi & Alan L. Erera & Chelsea C. White, 2021. "Managing mobile production-inventory systems influenced by a modulation process," Annals of Operations Research, Springer, vol. 304(1), pages 299-330, September.
    5. Katoozian, Hoora & Zanjani, Masoumeh Kazemi, 2022. "Supply network design for mass personalization in Industry 4.0 era," International Journal of Production Economics, Elsevier, vol. 244(C).
    6. Fathi, Mahdi & Khakifirooz, Marzieh & Diabat, Ali & Chen, Huangen, 2021. "An integrated queuing-stochastic optimization hybrid Genetic Algorithm for a location-inventory supply chain network," International Journal of Production Economics, Elsevier, vol. 237(C).
    7. Diri Kenger, Zülal & Koç, Çağrı & Özceylan, Eren, 2021. "Integrated disassembly line balancing and routing problem with mobile additive manufacturing," International Journal of Production Economics, Elsevier, vol. 235(C).
    8. Wang, Yu & Ropke, Stefan & Wen, Min & Bergh, Simon, 2023. "The mobile production vehicle routing problem: Using 3D printing in last mile distribution," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1407-1423.

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