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

TL;DR

This paper develops a multi-objective genetic algorithm to optimize a sustainable closed-loop supply chain, balancing costs, environmental impact, and reliability under demand uncertainty.

Contribution

It introduces a novel multi-objective model for sustainable supply chain design considering demand uncertainty and uses genetic algorithms to find optimal solutions.

Findings

The approach effectively balances costs, emissions, and reliability.

Pareto front analysis clearly identifies optimal trade-offs.

Model verification confirms efficiency and robustness.

Abstract

Supply chain management has been concentrated on productive ways to manage flows through a sophisticated vendor, manufacturer, and consumer networks for decades. Recently, energy and material rates have been greatly consumed to improve the sector, making sustainable development the core problem for advanced and developing countries. A new approach of supply chain management is proposed to maintain the economy along with the environment issue for the design of supply chain as well as the highest reliability in the planning horizon to fulfill customers demand as much as possible. This paper aims to optimize a new sustainable closed-loop supply chain network to maintain the financial 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 reliability. The situation has been considered under demand uncertainty with warehouse reliability. This approach has been suggested the multi-objective mathematical model minimizing the total costs and total CO2 emissions and maximize the reliability in handling for establishing the closed-loop supply chain. Two optimization methods are used namely Multi-Objective Genetic Algorithm Optimization Method and Weighted Sum Method. Two results have shown the optimality of this approach. This paper also showed the optimal point using Pareto front for clear identification of optima. The results are approved to verify the efficiency of the model and the methods to maintain the financial, environmental, and reliability issues.