Multi-Objective MILP-Based Optimization of Green Supply Chain Networks Under Demand Uncertainty

Abstract

In this paper, the problem of the optimization of the green supply chain networks (GSCNs) under unpredictable demand is addressed using a multi-objective approach of optimization. The growing relevance of sustainability in the supply chain design process is forcing companies to pursue environmentally friendly practices, at the same time making supply chains cost-effective and efficient. The proposed methodology is based on a Mixed-Integer Linear Programming (MILP) approach to decrease the negative impact on the environment, i.e., carbon emission and waste generation, as well as decrease the costs of operations. The parameters that are considered in the optimization model include transportation costs, production capacity, inventory control, and demand uncertainty, which are modeled using scenario planning. The results indicate that the use of green practices by supply chains does not always lead to an increase in costs, even in cases where the demand uncertainty is high. Experience of trade-offs between environmental impact and cost efficiency is seen in case studies in diverse industries. Some of the major statistical results were that operational cost was cut by 20%, and carbon emission was cut by a quarter, in contrast to the conventional models of supply chain. Also, the Efficiency of operations increased by 10 %, and the ability of the system to maintain its operation with the differing demand situation improved by 10 % as well. Such findings highlight the way GSCNs are able to become sustainable and profitable. The paper wraps up by providing some useful advice to the supply chain managers on how to integrate the green practice systematically into decision-making processes. The solution suggested has a powerful decision support system to companies that seek to improve the sustainability whilst addressing the issue of variability in demand.