Metaheuristic Solutions for Energy-Efficient Scheduling in Green Manufacturing

Abstract

This study focuses on simultaneous scheduling, a crucial approach in modern manufacturing where multiple jobs are scheduled concurrently to optimize resource utilization and energy efficiency. In recent years, the focus on green manufacturing has intensified, yet challenges in energy efficiency within manufacturing workshops remain significant. This study explores the efficacy of the Particle Swarm Optimization (PSO) model, particularly the novel variant NvPSO and Walrus Optimisation Algorithm (WaOA) in addressing complex scheduling and energy minimization problems. By integrating both scheduling and energy optimization, the NvPSO model achieved a remarkable 9% reduction in total energy costs and a complete elimination of tardiness penalties.The study evaluates NvPSO's and WaOA performance using 13 diverse jobsets and benchmarks it against traditional algorithms, including the Artificial Immune System (AIS) and the Modified Genetic Tabu Algorithm (MGTA). Performance metrics such as Makespan, energy consumption, and processing time were assessed. NvPSO and WaOA demonstrated superior capability in reducing Makespan and energy consumption, particularly in larger and more complex instances. This variant of PSO, with its parameters adjusted using a logistic map, significantly outperforms the Walrus Optimization Algorithm (WaOA), which, while also effective, benefits from increased iterations and computational simplicity. Overall, the study highlights the effectiveness of NvPSO in optimizing scheduling and energy management, offering substantial cost savings and enhanced performance over traditional and alternative algorithms. The results underscore NvPSO's potential as a robust solution for green manufacturing challenges.