Hybrid Multi-Objective Particle Swarm Optimization for Flexible Job Shop Scheduling Problem

Hybrid Multi-Objective Particle Swarm Optimization for Flexible Job Shop Scheduling Problem

Автор: S. V. Kamble, S. U. Mane, A. J. Umbarkar

Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa

Статья в выпуске: 4 vol.7, 2015 года.

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Hybrid algorithm based on Particle Swarm Optimization (PSO) and Simulated annealing (SA) is proposed, to solve Flexible Job Shop Scheduling with five objectives to be minimized simultaneously: makespan, maximal machine workload, total workload, machine idle time & total tardiness. Rescheduling strategy used to shuffle workload once the machine breakdown takes place in proposed algorithm. The hybrid algorithm combines the high global search efficiency of PSO with the powerful ability to avoid being trapped in local minimum of SA. A hybrid multi-objective PSO (MPSO) and SA algorithm is proposed to identify an approximation of the pareto front for Flexible job shop scheduling (FJSSP). Pareto front and crowding distance is used for identify the fitness of particle. MPSO is significant to global search and SA used to local search. The proposed MPSO algorithm is experimentally applied on two benchmark data set. The result shows that the proposed algorithm is better in term quality of non-dominated solution compared to the other algorithms in the literature.

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Particle Swarm Optimization, Simulated Annealing, Multi Objective Optimization, Job Shop Scheduling, Metaheurestic

Короткий адрес: https://sciup.org/15010679

IDR: 15010679

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