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A robust fuzzy stochastic model for the responsive-resilient inventory-location problem: comparison of metaheuristic algorithms

  • S.I. : Business Analytics and Operations Research
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Abstract

This study proposes a scenario-based mixed-integer programming model to investigate the responsive-resilient inventory-location problem under uncertainty. The proposed model minimizes the total costs and makes decisions about the location, allocation, and inventory problems. The literature showed that simultaneous consideration of responsiveness and resilience measures has been ignored by the researchers. Hence, to fill this gap, this study considers responsiveness and resilience measures in the proposed model. Also, since the uncertainty exists in the nature of the research problem due to changes in the business environment, this paper applies the queuing theory and robust fuzzy stochastic optimization to cope with uncertainty. At first, the existed uncertainty in lead time and demand is tackled by employing the queuing theory, and some performance measures of the system are calculated. Then, the achieved results are incorporated into the fuzzy robust stochastic model. As the problem is an NP-Hard, this study develops several metaheuristic algorithms to solve the proposed model in a reasonable time. Then, the applicability of the proposed model and efficiency of the developed algorithms are shown by several numerical examples. Eventually, several sensitivity analyses are conducted on some important parameters of the model, and useful managerial insights are provided.

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Authors and Affiliations

  1. School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Sina Nayeri, Mahdieh Tavakoli, Mehrab Tanhaeean & Fariborz Jolai

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  1. Sina Nayeri
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  2. Mahdieh Tavakoli
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  3. Mehrab Tanhaeean
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Correspondence to Fariborz Jolai.

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Nayeri, S., Tavakoli, M., Tanhaeean, M. et al. A robust fuzzy stochastic model for the responsive-resilient inventory-location problem: comparison of metaheuristic algorithms. Ann Oper Res 315, 1895–1935 (2022). https://doi.org/10.1007/s10479-021-03977-6

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