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Integrated supply, production and distribution scheduling under disruption risks

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  • Sawik, Tadeusz

Abstract

This paper presents a bi-objective stochastic mixed integer programming approach for a joint selection of suppliers and scheduling of production and distribution in a multi-echelon supply chain subject to local and regional disruption risks. Two conflicting problem objectives are minimization of cost and maximization of service level. The three shipping methods are considered for distribution of products: batch shipping with a single shipment of different customer orders, batch shipping with multiple shipments of different customer orders and individual shipping of each customer order immediately after its completion. The stochastic combinatorial optimization problem is formulated as a time-indexed mixed integer program with the weighted-sum aggregation of the two objective functions. The supply portfolio is determined by binary selection and fractional allocation variables while time-indexed assignment variables determine the production and distribution schedules. The problem formulation incorporates supply–production, production–distribution and supply–distribution coordinating constraints to efficiently coordinate supply, production and distribution schedules. Numerical examples modelled after an electronics supply chain and computational results are presented and some managerial insights are reported. The findings indicate that for all shipping methods, the service-oriented supply portfolio is more diversified than the cost-oriented portfolio and the more cost-oriented decision-making, the more delayed the expected supply, production and distribution schedules.

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  • Sawik, Tadeusz, 2016. "Integrated supply, production and distribution scheduling under disruption risks," Omega, Elsevier, vol. 62(C), pages 131-144.
    • Handle: RePEc:eee:jomega:v:62:y:2016:i:c:p:131-144
    DOI: 10.1016/j.omega.2015.09.005
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    7. Sun, X.T. & Chung, S.H. & Chan, Felix T.S. & Wang, Zheng, 2018. "The impact of liner shipping unreliability on the production–distribution scheduling of a decentralized manufacturing system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 242-269.
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    11. Gupta, Varun & Ivanov, Dmitry & Choi, Tsan-Ming, 2021. "Competitive pricing of substitute products under supply disruption," Omega, Elsevier, vol. 101(C).
    12. PrasannaVenkatesan, S. & Goh, M., 2016. "Multi-objective supplier selection and order allocation under disruption risk," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 95(C), pages 124-142.
    13. Nezamoddini, Nasim & Gholami, Amirhosein & Aqlan, Faisal, 2020. "A risk-based optimization framework for integrated supply chains using genetic algorithm and artificial neural networks," International Journal of Production Economics, Elsevier, vol. 225(C).
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    15. Behzadi, Golnar & O’Sullivan, Michael Justin & Olsen, Tava Lennon & Zhang, Abraham, 2018. "Agribusiness supply chain risk management: A review of quantitative decision models," Omega, Elsevier, vol. 79(C), pages 21-42.
    16. Shahed Mahmud & Ripon K. Chakrabortty & Alireza Abbasi & Michael J. Ryan, 2022. "Switching strategy-based hybrid evolutionary algorithms for job shop scheduling problems," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 1939-1966, October.
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    20. Cheng, Bayi & Leung, Joseph Y.-T. & Li, Kai & Yang, Shanlin, 2019. "Integrated optimization of material supplying, manufacturing, and product distribution: Models and fast algorithms," European Journal of Operational Research, Elsevier, vol. 277(1), pages 100-111.
    21. Konur, Dinçer & Campbell, James F. & Monfared, Sepideh A., 2017. "Economic and environmental considerations in a stochastic inventory control model with order splitting under different delivery schedules among suppliers," Omega, Elsevier, vol. 71(C), pages 46-65.
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