Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/33757
Title: Multi-level genetic algorithm for the resource-constrained re-entrant scheduling problem in the flow shop
Authors: Lin, D
Lee, CKM 
Ho, W
Issue Date: 2013
Source: Engineering applications of artificial intelligence, 2013, v. 26, no. 4, p. 1282-1290
Abstract: The re-entrant flow shop scheduling problem (RFSP) is regarded as a NP-hard problem and attracted the attention of both researchers and industry. Current approach attempts to minimize the makespan of RFSP without considering the interdependency between the resource constraints and the re-entrant probability. This paper proposed Multi-level genetic algorithm (GA) by including the co-related re-entrant possibility and production mode in multi-level chromosome encoding. Repair operator is incorporated in the Multi-level genetic algorithm so as to revise the infeasible solution by resolving the resource conflict. With the objective of minimizing the makespan, Multi-level genetic algorithm (GA) is proposed and ANOVA is used to fine tune the parameter setting of GA. The experiment shows that the proposed approach is more effective to find the near-optimal schedule than the simulated annealing algorithm for both small-size problem and large-size problem.
Keywords: Genetic algorithm
Multi-level encoding
Re-entrant
Resource-constrained
Publisher: Pergamon Press
Journal: Engineering applications of artificial intelligence 
ISSN: 0952-1976
EISSN: 1873-6769
DOI: 10.1016/j.engappai.2012.10.006
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