Elucidating multiprocessors flow shop scheduling with dependent setup times using a twin particle swarm optimization

• This study examines the multiprocessor flow shop problems with an objective function to minimize total weighted earliness and tardiness and machine idle time.
• We formulate an integer programming for the constrained due window and setups multiprocessor flow shop scheduling problems.
• Introduction to the proposed twin particle swarm optimization (TPSO).
• Various approaches are compared in small and large scale problems.
• Computational test results demonstrate the outstanding performance of the proposed method for the attempted problems.

Particle swarm optimization (PSO) is a novel metaheuristic, which has been applied in a wide variety of production scheduling problems. Two basic characteristics of this algorithm are its efficiency and effectiveness in providing high-quality solutions. In order to improve the traditional PSO, this study proposes the incorporation of a local search heuristic into the basic PSO algorithm. The new, hybrid, metaheuristic is called “twin particle swarm optimization (TPSO)”. The proposed metaheuristic scheme is applied to a flow shop with multiprocessors scheduling problem, which can be considered a real world case regarding the production line. This study, as far as the multiprocessors flow shop production system is concerned, utilizes sequence dependent setup times as constraints. Finally, simulated data confirm the effectiveness and robustness of the proposed algorithm. The data test results indicate that TPSO has potential to replace PSO and become a significant heuristic algorithm for similar problems.

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