Flexible job shop scheduling with parallel machines using Genetic Algorithm and Grouping Genetic Algorithm

► This research uses Genetic Algorithm to solve flexible job shop scheduling problem. ► This algorithm includes machine selection module and operation scheduling module. ► A real factory is used as a case study for performance evaluation. ► The proposed algorithm outperforms current methods used in...

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Bibliographic Details
Published in:Expert systems with applications 2012-09, Vol.39 (11), p.10016-10021
Main Authors: Chen, James C., Wu, Cheng-Chun, Chen, Chia-Wen, Chen, Kou-Huang
Format: Article
Language:English
Subjects:
Algorithms
Computer simulation
Financing
Flexible job shop
Genetic Algorithm
Genetic algorithms
Grouping Genetic Algorithm
Job shop scheduling
Makespan
Manufacturing engineering
Modules
Operation scheduling
Parallel machine
Scheduling
Tardiness
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Summary:► This research uses Genetic Algorithm to solve flexible job shop scheduling problem. ► This algorithm includes machine selection module and operation scheduling module. ► A real factory is used as a case study for performance evaluation. ► The proposed algorithm outperforms current methods used in practice. Based on Genetic Algorithm (GA) and Grouping Genetic Algorithm (GGA), this research develops a scheduling algorithm for job shop scheduling problem with parallel machines and reentrant process. This algorithm consists of two major modules: machine selection module (MSM) and operation scheduling module (OSM). MSM helps an operation to select one of the parallel machines to process it. OSM is then used to arrange the sequences of all operations assigned to each machine. A real weapon production factory is used as a case study to evaluate the performance of the proposed algorithm. Due to the high penalty of late delivery in military orders and high cost of equipment investment, total tardiness, total machine idle time and makespan are important performance measures used in this study. Based on the design of experiments, the parameters setting for GA and GGA are identified. Simulation results demonstrate that MSM and OSM respectively using GGA and GA outperform current methods used in practice.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2012.01.211