Multi-objective Particle Swarm Optimization for Sustainable Industrial Operations: Energy and Cost Perspectives

Industrial Operation (IO) has recently grown under pressure to optimize operating costs and reduce consumption of energy to attain sustainability. These two principal objectives frequently conflict, while industrial activities and advancements are underway. To overcome the constraints, this paper proposes a novel use of Multi-Objective Particle Swarm Optimization (MOPSO), providing an effective architecture for striking a balance between economic performance and energy efficiency in industrial operations. By making use of MOPSO's inherent benefits in managing complex, multi-objective issues. sA Pareto-optimal set of solutions is produced by the method, which not only facilitates decision-making but also helps decision-makers balance trade-offs between energy conservation and cost reduction. In contrast to traditional approaches, the proposed solution achieved a 45.6% reduction in operational cost and a 99.31% increase in energy efficiency, as demonstrated by a case study of a manufacturing process. The potential result of MOPSO as a metaheuristic method for sustainable industrial operations provides actionable insights for achieving greener and more cost-effective production systems. However, by demonstrating the useful implementation of MOPSP in an industrial setting in real time, this work adds to the increasing pace of research in intelligent optimization strategies.