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

Wu, Xiuli, Peng, Junjian, Xiao, Xiao, Wu, Shaomin (2021) An effective approach for the dual-resource flexible job shop scheduling problem considering loading and unloading. Journal of Intelligent Manufacturing, 32 . pp. 707-728. ISSN 0956-5515. E-ISSN 1572-8145. (doi:10.1007/s10845-020-01697-5) (KAR id:84129)


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.

Item Type: Article
DOI/Identification number: 10.1007/s10845-020-01697-5
Uncontrolled keywords: flexible job shop scheduling problem; fixture; resource requirement similarity; set-up time; improved NSGA-II
Subjects: H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
Divisions: Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems
Depositing User: Shaomin Wu
Date Deposited: 13 Nov 2020 09:43 UTC
Last Modified: 04 Mar 2024 16:08 UTC
Resource URI: (The current URI for this page, for reference purposes)

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