A multi-objective optimization study of energy-saving job shop scheduling

To enhance productivity and conserve resources, this paper proposes a hybrid optimization algorithm, MOTS, to minimize the makespan and total energy consumption. The algorithm integrates the decomposition strategy of the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) with the local search capabilities of Tabu Search. Furthermore, an innovative energy adjustment strategy is introduced, which optimizes scheduling schemes to reduce energy consumption by applying the left-shift or right-shift operations on processes. Experimental comparisons between MOTS and MOEA/D, based on multiple standard benchmark instances, demonstrate that MOTS outperforms MOEA/D in terms of Pareto front coverage and the diversity of the solution set.