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Joint management of capacity and inventory in make-to-stock production systems with multi-class demand

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  • Mayorga, Maria E.
  • Ahn, Hyun-Soo

Abstract

We evaluate the benefits of coordinating capacity and inventory decisions in a make-to-stock production environment. We consider a firm that faces multi-class demand and has additional capacity options that are temporary and randomly available. We formulate the model as a Markov decision process (MDP) and prove that a solution to the optimal joint control problem exists. For several special cases we characterize the structure of the optimal policy. For the general case, however, we show that the optimal policy is state-dependent, and in many instances non-monotone and difficult to implement. Therefore, we consider three pragmatic heuristic policies and assess their performance. We show that the majority of the savings originate from the ability to dynamically adjust capacity, and that a simple heuristic that can adjust production capacity (based on workload fluctuation) but uses a static production/rationing policy can result in significant savings.

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  • Mayorga, Maria E. & Ahn, Hyun-Soo, 2011. "Joint management of capacity and inventory in make-to-stock production systems with multi-class demand," European Journal of Operational Research, Elsevier, vol. 212(2), pages 312-324, July.
  • Handle: RePEc:eee:ejores:v:212:y:2011:i:2:p:312-324
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    References listed on IDEAS

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    Cited by:

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    2. Kim, Hyunjung & Kim, Eungab, 2022. "A hybrid manufacturing system with demand for intermediate goods and controllable make-to-stock production rate," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1244-1257.
    3. Bin Hu & Zhankun Sun, 2022. "Managing Self-Replicating Innovative Goods," Management Science, INFORMS, vol. 68(1), pages 399-419, January.
    4. Mohammad Najjartabar Bisheh & G. Reza Nasiri & Esmaeil Esmaeili & Hamid Davoudpour & Shing I. Chang, 2022. "A new supply chain distribution network design for two classes of customers using transfer recurrent neural network," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(5), pages 2604-2618, October.

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