Robust scenario-based modeling for green-resilient supply chain considering supporting supplier and hub location and allocation (case study: automotive industry)

Document Type : Original Article

Authors

1 Ph.D. Student, Department of Industrial Management, Sa.C., Islamic Azad University, Sanandaj, Iran.

2 Assistant Professor, Department of Industrial Engineering, Sa.C., Islamic Azad University, Sanandaj, Iran.

3 Associate Professor, Department of Industrial Engineering, Sa.C., Islamic Azad University, Sanandaj, Iran.

10.48308/jimp.15.2.120

Abstract

Introduction and Purpose: Implementing environmental requirements along with reducing logistics costs, selecting sustainable suppliers, and appropriately managing disruptions are among the critical and challenging issues in optimal supply chain management. These issues are especially important in sensitive and complex industries such as the automotive industry, because these industries require precise, coherent, and practical planning throughout their supply chains to maintain their survival, compete in global markets, and grow sustainably. The main objective of this study is to present a multi-objective, robust, scenario-based optimization model that is capable of designing a green, resilient, and flexible network with multi-product, multi-cycle, multi-material, and multi-level characteristics, while effectively managing uncertainties in supply and demand.
Methods: The issues of location and allocation of hubs and the greenness of the primary and backup suppliers were considered in the network design. Efforts have also been made to reduce costs, meet environmental standards, and increase resilience in network objectives and limitations. The scenario-based robust optimization approach was used in the mathematical model to deal with supply and demand uncertainties. In order to solve the multi-objective problem, 10 small and medium problems were solved by the augmented -constraint method and 10 large problems were solved by the NSGAII and MOPSO algorithms, and the results were evaluated and analyzed from different perspectives. The parameters of the metaheuristic algorithms were determined based on the Taguchi method. Also, the metaheuristic algorithms used were compared with each other based on 5 criteria: error, time, diversity, spacing, and MID.  
Results and discussion: The findings show that the NSGAII algorithm achieves the best results according to the evaluation criteria. The computational results reveal that the proposed network configuration could respond to customer demand green and resiliently. A case study in the automotive industry was presented to determine how the proposed model can meet actual requirements. The location of the hubs, the allocation amount, and their associated distribution centers were considered for Iran Khodro Company. Finally, sensitivity analysis was performed based on several important parameters, and the prediction of the results was described.
Conclusions: The results showed that the proposed model minimizes the pollution emitted by the chain members and the fixed and variable costs associated with the members, which include the cost of contracting with the main and supporting suppliers, the cost of establishing the hub, the cost of product production, and the costs of transferring between the chain members. Also, the use of the supporting supplier has been able to greatly improve the resilience of the chain in dealing with disruptions. The designed network has increased the resilience of Iran Khodro Company against these disruptions and can help the company's senior managers to improve the performance of the automotive industry chain. Based on our findings, the capacity of suppliers and hubs is very important, and their disruption will cause changes in network strategies. Our proposed model can help Iran Khodro company achieve some goals such as timely supply of products according to customer needs, optimal use of resources, reducing costs, and increasing flexibility.

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Main Subjects

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