Using a Just-In-Time Approach in the Green Supply Chain, Taking Into Account CO2 Emissions, Under Uncertainty in the Pre- and Post-COVID-19 Situation

Abstract

The main objective of this study is to develop a fuzzy-based approach for building a multistage, multiproduct, and multiperiod supply chain network (SCN) after and before the COVID-19 pandemic. The proposed model optimizes production and distribution planning under uncertainty in a multiperiod stochastic process network. The model is designed to help decision-makers manage the green supply chain (GSC) of their organizations. It was developed using the mixed-integer linear programming (MILP) approach. The model aims to maximize customer satisfaction in the pre- and post-COVID-19 era by reducing the total cost and delivery time they face. The model also estimates production, asset locations, order allocation, and inventory levels. Under uncertain conditions, a new probabilistic MILP model addresses the multiproduct, multiperiod SCN design (SCND) problem. The two objectives of this model are to maximize time and cost by using the concepts of total cost of ownership, activity-based costing, and just-in-time (JIT) production. The model’s outputs include the quantity of goods purchased, produced, inventoried, delivered, and transported and the selection of suppliers before and after the COVID situation. A numerical example solved using the above technique is given to evaluate and validate the model and the proposed solution approach. Finally, the results of the study are presented.