A Genetic Algorithm for Solving Flexible Flow Shop Scheduling Problem with Autonomous Guided Vehicles

Miao Wang; Bin Xin

doi:10.1109/ICCA.2019.8899914

A Genetic Algorithm for Solving Flexible Flow Shop Scheduling Problem with Autonomous Guided Vehicles

Miao Wang, Bin Xin^*

^*Corresponding author for this work

School of Automation

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

The flexible flow shop (FFS) is defined as a multistage flow shop with multiple parallel machines. FFS scheduling problem is a complex combinatorial problem which has been intensively studied in many real world industries. In the FFS scheduling problem, each job has to be processed on any machine at each stage, but it is not considered that how the jobs are transported from one machine to another. In this study, materials are transported from one machine to another through autonomous guided vehicles (AGV) system. In this paper, we propose a genetic algorithm (GA) for solving FFS scheduling problem in which AGVs are used to transport materials. We design effective coding and decoding scheme and genetic operators including crossover and mutation. The effectiveness of the algorithm is verified by simulation experiments.

Original language	English
Title of host publication	2019 IEEE 15th International Conference on Control and Automation, ICCA 2019
Publisher	IEEE Computer Society
Pages	922-927
Number of pages	6
ISBN (Electronic)	9781728111643
DOIs	https://doi.org/10.1109/ICCA.2019.8899914
Publication status	Published - Jul 2019
Event	15th IEEE International Conference on Control and Automation, ICCA 2019 - Edinburgh, United Kingdom Duration: 16 Jul 2019 → 19 Jul 2019

Publication series

Name	IEEE International Conference on Control and Automation, ICCA
Volume	2019-July
ISSN (Print)	1948-3449
ISSN (Electronic)	1948-3457

Conference

Conference	15th IEEE International Conference on Control and Automation, ICCA 2019
Country/Territory	United Kingdom
City	Edinburgh
Period	16/07/19 → 19/07/19

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10.1109/ICCA.2019.8899914

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Wang, M., & Xin, B. (2019). A Genetic Algorithm for Solving Flexible Flow Shop Scheduling Problem with Autonomous Guided Vehicles. In 2019 IEEE 15th International Conference on Control and Automation, ICCA 2019 (pp. 922-927). Article 8899914 (IEEE International Conference on Control and Automation, ICCA; Vol. 2019-July). IEEE Computer Society. https://doi.org/10.1109/ICCA.2019.8899914

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title = "A Genetic Algorithm for Solving Flexible Flow Shop Scheduling Problem with Autonomous Guided Vehicles",

abstract = "The flexible flow shop (FFS) is defined as a multistage flow shop with multiple parallel machines. FFS scheduling problem is a complex combinatorial problem which has been intensively studied in many real world industries. In the FFS scheduling problem, each job has to be processed on any machine at each stage, but it is not considered that how the jobs are transported from one machine to another. In this study, materials are transported from one machine to another through autonomous guided vehicles (AGV) system. In this paper, we propose a genetic algorithm (GA) for solving FFS scheduling problem in which AGVs are used to transport materials. We design effective coding and decoding scheme and genetic operators including crossover and mutation. The effectiveness of the algorithm is verified by simulation experiments.",

author = "Miao Wang and Bin Xin",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 15th IEEE International Conference on Control and Automation, ICCA 2019 ; Conference date: 16-07-2019 Through 19-07-2019",

year = "2019",

month = jul,

doi = "10.1109/ICCA.2019.8899914",

language = "English",

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Wang, M & Xin, B 2019, A Genetic Algorithm for Solving Flexible Flow Shop Scheduling Problem with Autonomous Guided Vehicles. in 2019 IEEE 15th International Conference on Control and Automation, ICCA 2019., 8899914, IEEE International Conference on Control and Automation, ICCA, vol. 2019-July, IEEE Computer Society, pp. 922-927, 15th IEEE International Conference on Control and Automation, ICCA 2019, Edinburgh, United Kingdom, 16/07/19. https://doi.org/10.1109/ICCA.2019.8899914

A Genetic Algorithm for Solving Flexible Flow Shop Scheduling Problem with Autonomous Guided Vehicles. / Wang, Miao; Xin, Bin.
2019 IEEE 15th International Conference on Control and Automation, ICCA 2019. IEEE Computer Society, 2019. p. 922-927 8899914 (IEEE International Conference on Control and Automation, ICCA; Vol. 2019-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - The flexible flow shop (FFS) is defined as a multistage flow shop with multiple parallel machines. FFS scheduling problem is a complex combinatorial problem which has been intensively studied in many real world industries. In the FFS scheduling problem, each job has to be processed on any machine at each stage, but it is not considered that how the jobs are transported from one machine to another. In this study, materials are transported from one machine to another through autonomous guided vehicles (AGV) system. In this paper, we propose a genetic algorithm (GA) for solving FFS scheduling problem in which AGVs are used to transport materials. We design effective coding and decoding scheme and genetic operators including crossover and mutation. The effectiveness of the algorithm is verified by simulation experiments.

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A Genetic Algorithm for Solving Flexible Flow Shop Scheduling Problem with Autonomous Guided Vehicles

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