A resilient-green model for multi-echelon meat supply chain planning. (February 2021)

• Record Type: Journal Article
• Authors: Gholami-Zanjani, Seyed Mohammad; Jabalameli, Mohammad Saeed; Pishvaee, Mir Saman
• Is Part Of: Computers & industrial engineering. Volume 152(2021)
• Publication Date: 2021-02
• Subjects: Meat supply chain -- Inventory management -- Resilience -- Green -- Stochastic programming; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2020.107018
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: A novel mathematical model is developed for meat inventory planning in a three-echelon supply chain. Physical impact of disruptions on capacity and inventory levels are profiled. Greenness and two resiliency strategies are jointly incorporated into the core model. Lexicographic Weighted Tchebycheff method is used to deal with the bi-objective two-stage stochastic model. The numerical results are derived based on several extensions of the core model and different indictors. Abstract: Inevitable disruptions and undesired environmental effects perturb food supply chains and meat supply chains. Academics and practitioners need to simultaneously incorporate resilience and greenness perspectives in planning approaches since this context has not been investigated in the existing literature. At the tactical level, it requires advanced modelling and solution approaches. In this research, a novel bi-objective stochastic model is developed to cover specific characteristics of meat inventory planning in a three-echelon network. Two resiliency strategies are embedded in the model to hedge against the disruptions. Disruptions behavior in the food context is characterized to illustrate real-world situations as stochastic processes using the Monte-Carlo method. To solve the model, sample average approximation and Lexicographic Weighted Tchebycheff methods are derived. Numerous problem instances are conducted to validate the applicability of the proposed model and solution approach. The results reiterate that resilient solutions could retain the network performance and even increase it by 6%. It is also concluded that solutions are sensitive to the throughput capacity and lead-time by 13% and 41%, respectively. Moreover, trade-off interactions between the two objectives are perceived to provide managerial insights.