Three Hybrid Scatter Search Algorithms for Multi-Objective Job Shop Scheduling Problem

Three Hybrid Scatter Search Algorithms for Multi-Objective Job Shop Scheduling Problem

Blog Article

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 Graviola 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 Rolling reach the optimal Pareto front.