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An integrated queuing-stochastic optimization hybrid Genetic Algorithm for a location-inventory supply chain network

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  • Fathi, Mahdi
  • Khakifirooz, Marzieh
  • Diabat, Ali
  • Chen, Huangen

Abstract

We consider a location-inventory optimization model for supply chain (SC) configuration. It includes a supplier, multiple distribution centers (DCs), and multiple retailers. Customer demand and replenishment lead time are considered to be stochastic. Two classes of customer orders, priority and ordinary, are assumed based on their demand. The goal is to find the optimal locations for DCs and their inventory policy simultaneously. For this purpose, a two-phase approach based on queuing theory and stochastic optimization was developed. In the first phase, the stock level of DCs is modeled as a Markov chain process and is analyzed, while in the second phase, a mathematical program is used to determine the optimal number and locations of DCs, the assignment of retailers to DCs, and the order quantity and safety stock level at DCs. As solving this problem is NP-hard, a hybrid Genetic Algorithm (GA) was developed to make the problem computationally tractable.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:proeco:v:237:y:2021:i:c:s0925527321001158
    DOI: 10.1016/j.ijpe.2021.108139
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    4. Farheen Naz & Rohit Agrawal & Anil Kumar & Angappa Gunasekaran & Abhijit Majumdar & Sunil Luthra, 2022. "Reviewing the applications of artificial intelligence in sustainable supply chains: Exploring research propositions for future directions," Business Strategy and the Environment, Wiley Blackwell, vol. 31(5), pages 2400-2423, July.

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