research-article

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

Authors:

Fangfang Zhang,

Mengjie ZhangAuthors Info & Claims

GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference

Pages 347 - 355

https://doi.org/10.1145/3321707.3321790

Published: 13 July 2019 Publication History

Abstract

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 terminal set is the key to the success of GPHH. There are a wide range of features in DFJSS that reflect different characteristics of the job shop state. However, the importance of a feature can vary from one scenario to another, and some features may be redundant or irrelevant under the considered scenario. Feature selection is a promising strategy to remove the unimportant features and reduce the search space of GPHH. However, no work has considered feature selection in GPHH for DFJSS so far. In addition, it is necessary to do feature selection for the two terminal sets simultaneously. In this paper, we propose a new two-stage GPHH approach with feature selection for evolving routing and sequencing rules for DFJSS. The experimental studies show that the best solutions achieved by the proposed approach are better than that of the baseline method in most scenarios. Furthermore, the rules evolved by the proposed approach involve a smaller number of unique features, which are easier to interpret.

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Cited By

Xu BXu KFei BHuang DTao LWang Y(2024)Automatic Design of Energy-Efficient Dispatching Rules for Multi-Objective Dynamic Flexible Job Shop Scheduling Based on Dual Feature Weight SetsMathematics10.3390/math1210146312:10(1463)Online publication date: 9-May-2024
https://doi.org/10.3390/math12101463
Đurasević MJakobović D(2024)Heuristic Ensemble Construction Methods of Automatically Designed Dispatching Rules for the Unrelated Machines EnvironmentAxioms10.3390/axioms1301003713:1(37)Online publication date: 5-Jan-2024
https://doi.org/10.3390/axioms13010037
Đurasević MJakobović DPicek SMariot L(2024)Assessing the Ability of Genetic Programming for Feature Selection in Constructing Dispatching Rules for Unrelated Machine EnvironmentsAlgorithms10.3390/a1702006717:2(67)Online publication date: 4-Feb-2024
https://doi.org/10.3390/a17020067
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Index Terms

A two-stage genetic programming hyper-heuristic approach with feature selection for dynamic flexible job shop scheduling
1. Computing methodologies
  1. Artificial intelligence
    1. Planning and scheduling
      1. Planning under uncertainty

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Published In

cover image ACM Conferences

GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference

July 2019

1545 pages

ISBN:9781450361118

DOI:10.1145/3321707

Editor:
Manuel López-Ibáñez
University of Manchester, UK
,
General Chairs:
Anne Auger
Inria and Ecole Polytechnique, France
,
Thomas Stützle
IRIDIA, Université libre de Bruxelles Belgium

Copyright © 2019 ACM.

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Published: 13 July 2019

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GECCO '19: Genetic and Evolutionary Computation Conference

July 13 - 17, 2019

Prague, Czech Republic

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

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Cited By

Xu BXu KFei BHuang DTao LWang Y(2024)Automatic Design of Energy-Efficient Dispatching Rules for Multi-Objective Dynamic Flexible Job Shop Scheduling Based on Dual Feature Weight SetsMathematics10.3390/math1210146312:10(1463)Online publication date: 9-May-2024
https://doi.org/10.3390/math12101463
Đurasević MJakobović D(2024)Heuristic Ensemble Construction Methods of Automatically Designed Dispatching Rules for the Unrelated Machines EnvironmentAxioms10.3390/axioms1301003713:1(37)Online publication date: 5-Jan-2024
https://doi.org/10.3390/axioms13010037
Đurasević MJakobović DPicek SMariot L(2024)Assessing the Ability of Genetic Programming for Feature Selection in Constructing Dispatching Rules for Unrelated Machine EnvironmentsAlgorithms10.3390/a1702006717:2(67)Online publication date: 4-Feb-2024
https://doi.org/10.3390/a17020067
Nguyen SThiruvady DSun YZhang MLi XHandl J(2024)Genetic-based Constraint Programming for Resource Constrained Job SchedulingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654046(942-951)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654046
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
Xu MMei YZhang FZhang M(2024)Genetic Programming With Lexicase Selection for Large-Scale Dynamic Flexible Job Shop SchedulingIEEE Transactions on Evolutionary Computation10.1109/TEVC.2023.324460728:5(1235-1249)Online publication date: Oct-2024
https://doi.org/10.1109/TEVC.2023.3244607
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
Yu RLadwig RXu XZhu PHanson PXie YJia X(2024)Evolution-Based Feature Selection for Predicting Dissolved Oxygen Concentrations in LakesParallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70085-9_25(398-415)Online publication date: 7-Sep-2024
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Oueslati IHammami MNouaouri IAzzouz ASaid LAllaoui H(2024)A Honey Bee Mating Optimization HyperHeuristic for Patient Admission Scheduling ProblemMetaheuristics and Nature Inspired Computing10.1007/978-3-031-69257-4_7(89-104)Online publication date: 15-Sep-2024
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\DJurasević MGil-Gala FJakobović D(2023)Constructing ensembles of dispatching rules for multi-objective tasks in the unrelated machines environmentIntegrated Computer-Aided Engineering10.3233/ICA-23070430:3(275-292)Online publication date: 10-May-2023
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