Energy-efficient job shop scheduling using hybrid mean-variance mapping optimization

The purpose of this work is to address a 5-jobs-4-machines energy-efficient job shop scheduling problem using Hybrid Mean- Variance Mapping Optimization algorithm. Two case studies have been used to validate the algorithm effectiveness in minimizing the maximum makespan and the total energy consumption. A job's assignment is constrained to only one machine, and each machine can operate on a single job at a time, with no preemption. The obtained results are compared to previous studies that applied different metaheuristics on the same model problem. Hybrid Mean-Variance Mapping Optimization has demonstrated superior results compared to Genetic Algorithm and matched the results obtained by Particle Swarm Optimization algorithm in solving job shop scheduling problems with energy considerations in the manufacturing environment. The algorithm's simplicity and the fewer adjustable control parameters are distinct advantages over other algorithms.