Multi-objective optimization of integrated lot-sizing and scheduling problem in flexible job shops

This paper investigates a particular integrated lotsizing and scheduling problem in a multi-level multi-product, multi-machine and flexible routes environment that is called flexible job shop problem (FJSP). The considered problem involves making simultaneous decision in sequencing operations, sizing lots and assigning machines to operations in order to optimize a multi-objective function including minimizing sum of the system costs, total machines workload and makespan, while a given demand is fulfilled without backlogging. Due to the complexity of the problem, a hybrid meta-heuristic based on a combination of Genetic algorithm and particle swarm optimization algorithm is developed to solve it. Additionally, the Taguchi method is employed to calibrate the influential parameters of the meta-heuristic and boost its capabilities. Finally, the performance of the proposed algorithm is compared with some well-known multi-objective algorithms such as NSGAII, SPEA2 and VEGA. Regarding to the computational results, the hybrid algorithm surpasses the other algorithms in the closeness of solutions to pareto optimal front and diversity criteria.

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