A multi-objective optimization model for the design of an effective decarbonized supply chain in mining

In this work, a three-objective optimization model for the design of an effective decarbonized supply chain in mining is proposed. It considers the installation and transport costs as the economic objective, the environmental objective through emissions from transport and operations, and the efficiency of the processing plants as the technological objective. We suggested a Pareto-based algorithm, which is a Multi-objective Hybrid Particle Swarm Optimization metaheuristic, for solving the problem. Using six metrics, many categorized instances are given to compare the performances of our algorithm, with those of an epsilon-constraint based algorithm (AUGMECON). The computational experiments show that the proposed metaheuristic is better than AUGMECON when it comes to metrics related to diversity and the distribution of solutions in the Pareto front.