2020
DOI: 10.1007/s10845-020-01697-5
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An effective approach for the dual-resource flexible job shop scheduling problem considering loading and unloading

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 mo… Show more

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Cited by 43 publications
(13 citation statements)
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“…The search performance of the optimization algorithm is enhanced. For the dualresource FJSSP, Wu et al [11] designed a similarity-based set-up time reduction scheduling algorithm and an improved non-dominated sorting genetic algorithm to solve the scheduling model. It can effectively improve production efficiency.…”
Section: Related Workmentioning
confidence: 99%
“…The search performance of the optimization algorithm is enhanced. For the dualresource FJSSP, Wu et al [11] designed a similarity-based set-up time reduction scheduling algorithm and an improved non-dominated sorting genetic algorithm to solve the scheduling model. It can effectively improve production efficiency.…”
Section: Related Workmentioning
confidence: 99%
“…-2-e EA-LS Wu et al [14] WU studied the dual resource constrained FJSP when loading and unloading time of the fixtures was considered. It formulated a multiobjective mathematical model to jointly minimize the Makespan and the total setup time by considering the influence of resource requirement similarity among different operations.…”
Section: The Formulation Of Some Commonmentioning
confidence: 99%
“…Two common strategies are used to study the dual-resource scheduling problem: one is solving the problem of machine allocation and process arrangement first, then scheduling AGV, and the dynamic programming method is mainly adopted to solve these problems. The other is scheduling the machine and AGV simultaneously under the assumption that the AGV path is known without collision, and genetic algorithm, simulated annealing algorithm, particle swarm optimization algorithm, tabu search algorithm, and other intelligent optimization algorithms are used to solve the problem [14][15][16][17][18][19][20][21][22]. Neither of the above two cases pays attention to the uncertainty of AGV transfer time or path planning in the actual production process, and does not realize the real dual-resource integrated scheduling.…”
Section: Introductionmentioning
confidence: 99%