Abstract
Nowadays, according to the importance of sustainability objectives in supply chain (SC) design and planning, in addition to the economic objective, the social, environmental, and customer-oriented considerations play a vital role in the SCs' performance. On the other hand, SC resilience will cause sustainability to be less affected during disruption. This paper deals with the resilient sustainable supply chain design and planning (RSSCDP) problem under supply disruption risk. We developed a multi-objective robust model to solve the multi-product RSSCDP problem under various supply disruption scenarios. In this problem, the most important decisions related to SC designing and planning, such as facility location and determination of their capacities, supplier selection, inventory management, order allocation and transportation planning, lot sizing, and production planning, are taken into account. In the proposed model, four absorptive and adaptive resilience strategies are simultaneously applied, including (1) multiple sourcing, (2) backup supplier contracting, (3) raw material inventory pre-positioning, and (4) final product inventory pre-positioning. Also, a reasonable trading-off among four objectives, including economic, social, environmental, as well as customer-oriented objectives using the Lp-metric method, is made. In addition, a multi-objective robust scenario-based stochastic programming approach is employed to cope with the operational uncertainty of the parameters. Finally, some numerical studies of the RSSCDP problem are conducted to demonstrate the capabilities and effectiveness of the developed model. The results depict a 10 to 15 percent improvement in the objectives. The findings confirm that the proposed resilience strategies are efficient in mitigating supply disruptions and can maintain the SC sustainability under disruptions.
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Safari, L., Sadjadi, S.J. & Sobhani, F.M. Resilient and sustainable supply chain design and planning under supply disruption risk using a multi-objective scenario-based robust optimization model. Environ Dev Sustain (2023). https://doi.org/10.1007/s10668-023-03769-x
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DOI: https://doi.org/10.1007/s10668-023-03769-x