poster

A preliminary approach to evolutionary multitasking for dynamic flexible job shop scheduling via genetic programming

Authors:

Fangfang Zhang,

Yi Mei,

Su Nguyen,

Mengjie ZhangAuthors Info & Claims

GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

Pages 107 - 108

https://doi.org/10.1145/3377929.3389934

Published: 08 July 2020 Publication History

Get Access

Abstract

Genetic programming, as a hyper-heuristic approach, has been successfully used to evolve scheduling heuristics for job shop scheduling. However, the environments of job shops vary in configurations, and the scheduling heuristic for each job shop is normally trained independently, which leads to low efficiency for solving multiple job shop scheduling problems. This paper introduces the idea of multitasking to genetic programming to improve the efficiency of solving multiple dynamic flexible job shop scheduling problems with scheduling heuristics. It is realised by the proposed evolutionary framework and knowledge transfer mechanism for genetic programming to train scheduling heuristics for different tasks simultaneously. The results show that the proposed algorithm can dramatically reduce the training time for solving multiple dynamic flexible job shop tasks.

References

[1]

Juergen Branke, Su Nguyen, Christoph W Pickardt, and Mengjie Zhang. 2016. Automated design of production scheduling heuristics: A review. IEEE Transactions on Evolutionary Computation 20, 1 (2016), 110--124.

Digital Library

Google Scholar

[2]

Abhishek Gupta, Yew-Soon Ong, Liang Feng, and Kay Chen Tan. 2017. Multiobjective Multifactorial Optimization in Evolutionary Multitasking. IEEE Transactions on Cybernetics 47, 7 (2017), 1652--1665.

Crossref

Google Scholar

[3]

John R Koza and Riccardo Poli. 2005. Genetic programming. In Search Methodologies. Springer, 127--164.

Google Scholar

[4]

Fangfang Zhang, Yi Mei, and Mengjie Zhang. 2018. Genetic programming with multi-tree representation for dynamic flexible job shop scheduling. In Proceedings of the Australasian Joint Conference on Artificial Intelligence. Springer, 472--484.

Crossref

Google Scholar

[5]

Fangfang Zhang, Yi Mei, and Mengjie Zhang. 2019. A Two-Stage Genetic Programming Hyper-heuristic Approach with Feature Selection for Dynamic Flexible Job Shop Scheduling. In Proceedings of the Genetic and Evolutionary Computation Conference. IEEE, 347--355.

Digital Library

Google Scholar

Cited By

View all

Zhang FShi GMei YZhang M(2025)Multiobjective Dynamic Flexible Job Shop Scheduling With Biased Objectives via Multitask Genetic ProgrammingIEEE Transactions on Artificial Intelligence10.1109/TAI.2024.34560866:1(169-183)Online publication date: Jan-2025
https://doi.org/10.1109/TAI.2024.3456086
Vela AValencia-Rivera GCruz-Duarte JOrtiz-Bayliss JAmaya I(2025)Hyper-Heuristics and Scheduling Problems: Strategies, Application Areas, and Performance MetricsIEEE Access10.1109/ACCESS.2025.353220113(14983-14997)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3532201
Huang YZhou WWang YLi MFeng LTan K(2024)Evolutionary Multitasking With Centralized Learning for Large-Scale Combinatorial Multiobjective OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2023.332387728:5(1499-1513)Online publication date: Oct-2024
https://doi.org/10.1109/TEVC.2023.3323877
Show More Cited By

Index Terms

A preliminary approach to evolutionary multitasking for dynamic flexible job shop scheduling via genetic programming
1. Computing methodologies
  1. Artificial intelligence
    1. Planning and scheduling
      1. Planning under uncertainty

Recommendations

Genetic programming with diverse partner selection for dynamic flexible job shop scheduling
GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Dynamic flexible job shop scheduling (DFJSS) aims to make decisions for machine assignment and operation sequencing simultaneously to get an effective schedule under dynamic environments. Genetic programming hyper-heuristic (GPHH) has been successfully ...
Sample-Aware Surrogate-Assisted Genetic Programming for Scheduling Heuristics Learning in Dynamic Flexible Job Shop Scheduling
GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference

Genetic programming (GP) has been successfully introduced to learn scheduling heuristics for dynamic flexible job shop scheduling (DFJSS) automatically. However, the evaluations of GP individuals are normally time-consuming, especially with long DFJSS ...
A two-stage genetic programming hyper-heuristic approach with feature selection for dynamic flexible job shop scheduling
GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference

Dynamic flexible job shop scheduling (DFJSS) is an important and a challenging combinatorial optimisation problem. Genetic programming hyper-heuristic (GPHH) has been widely used for automatically evolving the routing and sequencing rules for DFJSS. The ...

Comments

Information & Contributors

Information

Published In

GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

July 2020

1982 pages

ISBN:9781450371278

DOI:10.1145/3377929

General Chair:
Carlos Artemio Coello Coello
CINVESTAV-IPN

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 July 2020

Check for updates

Author Tags

Qualifiers

Poster

Funding Sources

Chinese Government Scholarship

Conference

GECCO '20

Sponsor:

SIGEVO

GECCO '20: Genetic and Evolutionary Computation Conference

July 8 - 12, 2020

Cancún, Mexico

Acceptance Rates

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

28
Total Citations
View Citations
228
Total Downloads

Downloads (Last 12 months)29
Downloads (Last 6 weeks)1

Reflects downloads up to 25 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Zhang FShi GMei YZhang M(2025)Multiobjective Dynamic Flexible Job Shop Scheduling With Biased Objectives via Multitask Genetic ProgrammingIEEE Transactions on Artificial Intelligence10.1109/TAI.2024.34560866:1(169-183)Online publication date: Jan-2025
https://doi.org/10.1109/TAI.2024.3456086
Vela AValencia-Rivera GCruz-Duarte JOrtiz-Bayliss JAmaya I(2025)Hyper-Heuristics and Scheduling Problems: Strategies, Application Areas, and Performance MetricsIEEE Access10.1109/ACCESS.2025.353220113(14983-14997)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3532201
Huang YZhou WWang YLi MFeng LTan K(2024)Evolutionary Multitasking With Centralized Learning for Large-Scale Combinatorial Multiobjective OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2023.332387728:5(1499-1513)Online publication date: Oct-2024
https://doi.org/10.1109/TEVC.2023.3323877
Huang ZMei YZhang FZhang M(2024)Multitask Linear Genetic Programming With Shared Individuals and Its Application to Dynamic Job Shop SchedulingIEEE Transactions on Evolutionary Computation10.1109/TEVC.2023.326387128:6(1546-1560)Online publication date: Dec-2024
https://doi.org/10.1109/TEVC.2023.3263871
Zhang FMei YNguyen SZhang M(2024)Survey on Genetic Programming and Machine Learning Techniques for Heuristic Design in Job Shop SchedulingIEEE Transactions on Evolutionary Computation10.1109/TEVC.2023.325524628:1(147-167)Online publication date: Feb-2024
https://doi.org/10.1109/TEVC.2023.3255246
Chen JJia YBi YChen W(2024)Generate a Single Heuristic for Multiple Dynamic Flexible Job Shop Scheduling Tasks by Genetic Programming2024 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC60901.2024.10611762(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/CEC60901.2024.10611762
Xu WWan Y(2024)A Heuristic-Based Evolutionary Approach for Joint Optimization of Job Shop Scheduling and Facility LayoutIEEE Access10.1109/ACCESS.2024.342714412(97630-97645)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3427144
Guo HLiu JWang YZhuang C(2024)An improved genetic programming hyper-heuristic for the dynamic flexible job shop scheduling problem with reconfigurable manufacturing cellsJournal of Manufacturing Systems10.1016/j.jmsy.2024.03.00974(252-263)Online publication date: Jun-2024
https://doi.org/10.1016/j.jmsy.2024.03.009
Pan XLei MSun JWang HJu TBai L(2024)An evolutionary feature selection method based on probability-based initialized particle swarm optimizationInternational Journal of Machine Learning and Cybernetics10.1007/s13042-024-02107-5Online publication date: 14-Mar-2024
https://doi.org/10.1007/s13042-024-02107-5
Zhang FMei YNguyen STan KZhang M(2023)Task Relatedness-Based Multitask Genetic Programming for Dynamic Flexible Job Shop SchedulingIEEE Transactions on Evolutionary Computation10.1109/TEVC.2022.319978327:6(1705-1719)Online publication date: Dec-2023
https://doi.org/10.1109/TEVC.2022.3199783
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Genetic programming with diverse partner selection for dynamic flexible job shop scheduling

Sample-Aware Surrogate-Assisted Genetic Programming for Scheduling Heuristics Learning in Dynamic Flexible Job Shop Scheduling

A two-stage genetic programming hyper-heuristic approach with feature selection for dynamic flexible job shop scheduling

Comments

Information

Published In

Sponsors

Publisher

Publication History

Check for updates

Author Tags

Qualifiers

Funding Sources

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations