Abstract
The Job Shop Scheduling Problem (JSSP) consists of finding the best scheduling for a set of jobs that should be processed in a specific order using a set of machines. This problem belongs to the NP-hard class problems and has enormous industrial applicability. In the manufacturing area, decision-makers consider several criteria to elaborate their production schedules. These cases are studied in multi-objective optimization. However, few works are addressed from this multi-objective perspective. The literature shows that multi-objective evolutionary algorithms can solve these problems efficiently; nevertheless, multi-objective algorithms have slow convergence to the Pareto optimal front. This paper proposes three multi-objective Scatter Search hybrid algorithms that improve the convergence speed evolving on a reduced set of solutions. These algorithms are: Scatter Search/Local Search (SS/LS), Scatter Search/Chaotic Multi-Objective Threshold Accepting (SS/CMOTA), and Scatter Search/Chaotic Multi-Objective Simulated Annealing (SS/CMOSA). The proposed algorithms are compared with the state-of-the-art algorithms IMOEA/D, CMOSA, and CMOTA, using the MID, Spacing, HV, Spread, and IGD metrics; according to the experimental results, the proposed algorithms achieved the best performance. Notably, they obtained a 47% reduction in the convergence time to reach the optimal Pareto front.
Highlights
Accepted: 21 January 2022Published: 31 January 2022 Publisher’sNote: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.Licensee MDPI, Basel, Switzerland
We observed that our hybrid algorithms Scatter Search/Local Search (SS/Local Search (LS)), Scatter Search (SS)/CMOTA, and SS/Chaotic Multi-Objective Simulated Annealing (CMOSA) obtained the best results
We observed that the SS/LS algorithm obtained a better performance since the value of the MID metric is smaller than the other algorithms analyzed
Summary
Accepted: 21 January 2022Published: 31 January 2022 Publisher’sNote: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.Licensee MDPI, Basel, Switzerland. Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. For a job to be completed, all of its operations must be processed in a given sequence. This problem belongs to the NP-hard class [1], is challenging of solving it, and has significant industrial applicability [2]. In JSSP, we must determine the order or sequence for processing a set of jobs through several machines minimizing one or more objective functions. The Multi-Objective optimization algorithms use the concept of domination where two solutions are compared to determine if one solution dominates the other or not. Pareto Dominance: For any optimization problem, solution A dominates another solution B if the following conditions are met: A is strictly better than B on at least one objective, and A is not worse than B in all objectives [23].
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