Multi-Objective Optimization for Sustainable Closed-Loop Supply Chain Network Under Demand Uncertainty: A Genetic Algorithm

Productivity can be increased through sophisticated supply chain management including vendor, manufacturer, and the final consumer. Recently, energy and material consumption have been greatly increased. As a result, the problem has increased for sustainable development for developed and developing countries. Hence, in this paper a novel approach of supply chain management is proposed to maintain the economic and the environment issues for the design of supply chain as well to ensure the customer demand. The objectives of this paper is to optimize a new sustainable closed-loop supply chain network to maintain the economic along with the environmental factor to minimize the negative effect on the environment and maximize the average total number of products dispatched to customers to enhance satisfaction. The demand uncertainty and warehouse reliability has been considered in this model. This multi-objective mathematical model minimizes the total costs and total CO2 emissions and maximize the customer reliability through establishing a closed-loop supply chain network. Multi-Objective Genetic Algorithm Optimization Method and Weighted Sum Method are used to solve the proposed problem. The results indicate the optimality of the proposed problem through Pareto front. The obtained result validates the model to maintain economic, environmental, and reliability aspects.