Optimal production delivery policies for supplier and manufacturer in a constrained closed-loop supply chain for returnable transport packaging through metaheuristic approach

Abstract

Recent advances in the product packaging materials have enabled the supply chain management systems to adopt returnable transport packaging policies to achieve economic and environmental sustainability. The application of the advanced self-healing polymers in packaging material has enabled the packaging to withstand fatigue associated failures with the increased mechanical strength. In this perspective, this paper develops a multi-attribute closed-loop supply chain model for self-healing polymers based returnable transport packaging with single supplier, single manufacturer, and multi-retailers under budget and storage constraints. A single-setup-multi-delivery (SSMD) policy is recommended for the centralized decision making of the supplier and manufacturer in a proposed supply chain management to improve the economic sustainability. To depict the real world situations for environmental protection, the effect of the variable aspects of transportation and carbon emissions are minimized through the optimal production delivery strategies. Multi-objectives of the proposed supply chain model include profit maximization and carbon emissions minimization of the system. A weighted goal programming technique along with three distinct metaheuristic approaches are applied to obtain the efficient trade-off among model objectives. The experimental analysis is carried out to illustrate the practical implication of the proposed supply chain management model and numerical results are analyzed for their robustness. The experimental outcomes for the application of SSMD policy are compared with single-setup-single-delivery (SSSD) policy, which proves that the SSMD policy improves the total profit of the whole system by devising the optimal number of shipments. The sensitivity analysis is carried out to study the behavior of the key parameters involved in the proposed supply chain management for varying decision maker preferences and significant managerial insights are obtained.