An effective approach for the dual-resource flexible job shop scheduling problem considering loading and unloading

Xiuli Wu, Junjian Peng, Xiao Xiao and Shaomin Wu ()
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Xiuli Wu: University of Science and Technology Beijing
Junjian Peng: University of Science and Technology Beijing
Xiao Xiao: University of Science and Technology Beijing
Shaomin Wu: University of Kent

Journal of Intelligent Manufacturing, 2021, vol. 32, issue 3, No 5, 707-728

Abstract: Abstract Many manufacturing systems need more than one type of resource to co-work with. Commonly studied flexible job shop scheduling problems merely consider the main resource such as machines and ignore the impact of other types of resource. As a result, scheduling solutions may not put into practice. This paper therefore studies the dual resource constrained flexible job shop scheduling problem when loading and unloading time (DRFJSP-LU) of the fixtures is considered. It formulates a multi-objective mathematical model to jointly minimize the makespan and the total setup time. Considering the influence of resource requirement similarity among different operations, we propose a similarity-based scheduling algorithm for setup-time reduction (SSA4STR) and then an improved non-dominated sorting genetic algorithm II (NSGA-II) to optimize the DRFJSP-LU. Experimental results show that the SSA4STR can effectively reduce the loading and unloading time of fixtures while ensuring a level of makespan. The experiments also verify that the scheduling solution with multiple resources has a greater guiding effect on production than the scheduling result with a single resource.

Keywords: Flexible job shop scheduling problem; Fixture; Resource requirement similarity; Set-up time; Improved NSGA-II (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s10845-020-01697-5

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