A Biobjective Model for Integrated Inventory and Transportation at Tactical and Operational Levels With Green Constraints

Carbon emission policies should be considered in supply chain modeling. Rather than regarding the cost of carbon emissions like other costs in the supply chain, it should be considered as a separate objective function using multiobjective modeling. This function can then be minimized using a multiobjective optimization method. This study presents a biobjective model for a green supply chain that incorporates learning effects and involves suppliers, manufacturers, distribution centers, and retailers with different capacities. The model aims to minimize costs associated with inventory, manpower, fixed and variable costs, and purchase costs while also reducing carbon dioxide emissions by optimizing vehicle allocation to drivers and destinations based on human factors in different scenarios. The proposed model is responsive to retailer demand and addresses real-life constraints for transportation and inventory decisions within a green supply chain framework. To tackle the complexity of large-scale problems, this study employs Nondominated Sorting Genetic Algorithm-II to solve the model.