A Multi-Objective Optimization Model for Green Supply Chain Network Design Using Metaheuristic Algorithms

Today, green supply chain managers in leading companies are trying to benefit from green logistics and improve their environmental performance throughout the supply chain as a strategic tool to gain sustainable competitive advantage by creating environmental desirability and satisfaction throughout the supply chain. The green supply chain has emerged as a new approach for companies to achieve profit and target market share by reducing risk and environmental impacts. Supplier selection is a complex decision-making problem in which a variety of evaluation criteria have different degrees of importance according to the supply chain strategy. In recent years, the relationship between supplier and consumer has received serious attention in manufacturing companies. When there is a long-term relationship between the two, the company's supply chain will be a serious and strong obstacle to competitors. Considering that a major part of the materials and components of products are supplied from external suppliers, paying attention to environmental criteria in the supply process is important. In this study, an integrated approach to distribution networks is presented to simultaneously address the location and allocation issues in the green supply chain. In this approach, decisions related to facility location, allocation, and customer demand satisfaction are made in a way that minimizes pollution. In this regard, a new mathematical model of multi-facility location-allocation is presented by considering several types of transportation to minimize the total environmental pollution, costs related to facility location and transportation costs and maximize the population coverage of demand. Also, non-dominated sorting genetic algorithms and non-dominated ranking genetic algorithms are used to solve the proposed model, and finally, the results of solving the model through each algorithm are compared with each other, and the necessary analyses are presented.