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

Introduction and objectives. Implementing environmental requirements, reducing logistics costs, selecting sustainable suppliers and managing related disruptions to maintain business continuity are important issues for supply chain management. All industries, especially the automobile industry, must carry out detailed and practical planning in the supply chains for the continuation of life and growth in order to achieve profitability and customer satisfaction.

Methods. The issues of location and allocation of hubs and the greenness of the main and supporting suppliers were considered in the network design. Efforts have also been made to reduce costs, meet environmental standards, and increase resilience in the goals and limitations of the network. To deal with uncertainties, the scenario-based robust optimization approach was used in the mathematical model. In order to solve the multi-objective problem, the enhanced epsilon constraint approaches, NSGAII and MOPSO were used.

Findings. The results show that NSGAII is the algorithm that achieves the best results according to the evaluation criteria. The calculation results show that the proposed network configuration can respond to the demand of its customers in a green and resilient way.

conclusion Based on our findings, the importance of the capacity of suppliers and hubs is very prominent and their disruption causes changes in network strategies. Our proposed model can help Iran Khodro to achieve some goals, such as timely supply of products that meet customer needs, optimal use of resources, cost reduction, and increased flexibility.ntroduction and objectives. Implementing environmental requirements, reducing logistics costs, selecting sustainable suppliers and managing related disruptions to maintain business continuity are important issues for supply chain management. All industries, especially the automobile industry, must carry out detailed and practical planning in the supply chains for the continuation of life and growth in order to achieve profitability and customer satisfaction.

Methods. The issues of location and allocation of hubs and the greenness of the main and supporting suppliers were considered in the network design. Efforts have also been made to reduce costs, meet environmental standards, and increase resilience in the goals and limitations of the network. To deal with uncertainties, the scenario-based robust optimization approach was used in the mathematical model. In order to solve the multi-objective problem, the enhanced epsilon constraint approaches, NSGAII and MOPSO were used.

Findings. The results show that NSGAII is the algorithm that achieves the best results according to the evaluation criteria. The calculation results show that the proposed network configuration can respond to the demand of its customers in a green and resilient way.

conclusion Based on our findings, the importance of the capacity of suppliers and hubs is very prominent and their disruption causes changes in network strategies. Our proposed model can help Iran Khodro to achieve some goals, such as timely supply of products that meet customer needs, optimal use of resources, cost reduction, and increased flexibility.ntroduction and objectives. Implementing environmental requirements, reducing logistics costs, selecting sustainable suppliers and managing related disruptions to maintain business continuity are important issues for supply chain management. All industries, especially the automobile industry, must carry out detailed and practical planning in the supply chains for the continuation of life and growth in order to achieve profitability and customer satisfaction.

Methods. The issues of location and allocation of hubs and the greenness of the main and supporting suppliers were considered in the network design. Efforts have also been made to reduce costs, meet environmental standards, and increase resilience in the goals and limitations of the network. To deal with uncertainties, the scenario-based robust optimization approach was used in the mathematical model. In order to solve the multi-objective problem, the enhanced epsilon constraint approaches, NSGAII and MOPSO were used.

Findings. The results show that NSGAII is the algorithm that achieves the best results according to the evaluation criteria. The calculation results show thpliers and hubs is very prominent and their disruption causes chad hubs is very prominent and their disruption causes changes in network strategies. Our proposed model can help Iran Khodro to achieve some goals, such as timely supply of products that meet customer needs, optimal use of resources, cost reduction, and increased flexibility.

Methods. The issues of location and allocation of hubs and the greenness of the main and supporting

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