scholarly journals Feature selection in evolving job shop dispatching rules with genetic programming

2021 ◽  
Author(s):  
Yi Mei ◽  
Mengjie Zhang ◽  
Su Nyugen
Keyword(s):  
Genetic Programming ◽  
Domain Knowledge ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Search Space ◽  
Dispatching Rules ◽  
Terminal Set ◽  
Selection Approach ◽  
Personal Use ◽  
Priority Function

Genetic Programming (GP) has been successfully used to automatically design dispatching rules in job shop scheduling. The goal of GP is to evolve a priority function that will be used to order the waiting jobs at each decision point, and decide the next job to be processed. To this end, the proper terminals (i.e. job shop features) have to be decided. When evolving the priority function, various job shop features can be included in the terminal set. However, not all the features are helpful, and some features are irrelevant to the rule. Including irrelevant features into the terminal set enlarges the search space, and makes it harder to achieve promising areas. Thus, it is important to identify the important features and remove the irrelevant ones to improve the GP-evolved rules. This paper proposes a domain-knowledge-free feature ranking and selection approach. As a result, the terminal set is significantly reduced and only the most important features are selected. The experimental results show that using only the selected features can lead to significantly better GP-evolved rules on both training and unseen test instances. © Mei 2016. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in 'GECCO '16: Proceedings of the Genetic and Evolutionary Computation Conference', https://doi.org/10.1145/2908812.2908822.

scholarly journals Feature selection in evolving job shop dispatching rules with genetic programming

2021 ◽  
Author(s):  
Yi Mei ◽  
Mengjie Zhang ◽  
Su Nyugen
Keyword(s):  
Genetic Programming ◽  
Domain Knowledge ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Search Space ◽  
Dispatching Rules ◽  
Terminal Set ◽  
Selection Approach ◽  
Personal Use ◽  
Priority Function

Genetic Programming (GP) has been successfully used to automatically design dispatching rules in job shop scheduling. The goal of GP is to evolve a priority function that will be used to order the waiting jobs at each decision point, and decide the next job to be processed. To this end, the proper terminals (i.e. job shop features) have to be decided. When evolving the priority function, various job shop features can be included in the terminal set. However, not all the features are helpful, and some features are irrelevant to the rule. Including irrelevant features into the terminal set enlarges the search space, and makes it harder to achieve promising areas. Thus, it is important to identify the important features and remove the irrelevant ones to improve the GP-evolved rules. This paper proposes a domain-knowledge-free feature ranking and selection approach. As a result, the terminal set is significantly reduced and only the most important features are selected. The experimental results show that using only the selected features can lead to significantly better GP-evolved rules on both training and unseen test instances. © Mei 2016. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in 'GECCO '16: Proceedings of the Genetic and Evolutionary Computation Conference', https://doi.org/10.1145/2908812.2908822.

scholarly journals A Hybrid Genetic Programming Algorithm for Automated Design of Dispatching Rules

2020 ◽  
Author(s):  
Su Nguyen ◽  
Yi Mei ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Production Systems ◽  
Search Space ◽  
Automated Design ◽  
Dispatching Rules ◽  
Programming Algorithm ◽  
Massachusetts Institute Of Technology ◽  
Institute Of Technology

© 2018 Massachusetts Institute of Technology. Designing effective dispatching rules for production systems is a difficult and timeconsuming task if it is done manually. In the last decade, the growth of computing power, advanced machine learning, and optimisation techniques has made the automated design of dispatching rules possible and automatically discovered rules are competitive or outperform existing rules developed by researchers. Genetic programming is one of the most popular approaches to discovering dispatching rules in the literature, especially for complex production systems. However, the large heuristic search space may restrict genetic programming from finding near optimal dispatching rules. This article develops a new hybrid genetic programming algorithm for dynamic job shop scheduling based on a new representation, a new local search heuristic, and efficient fitness evaluators. Experiments show that the new method is effective regarding the quality of evolved rules. Moreover, evolved rules are also significantly smaller and contain more relevant attributes.

scholarly journals A Hybrid Genetic Programming Algorithm for Automated Design of Dispatching Rules

2020 ◽  
Author(s):  
Su Nguyen ◽  
Yi Mei ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Production Systems ◽  
Search Space ◽  
Automated Design ◽  
Dispatching Rules ◽  
Programming Algorithm ◽  
Massachusetts Institute Of Technology ◽  
Institute Of Technology

© 2018 Massachusetts Institute of Technology. Designing effective dispatching rules for production systems is a difficult and timeconsuming task if it is done manually. In the last decade, the growth of computing power, advanced machine learning, and optimisation techniques has made the automated design of dispatching rules possible and automatically discovered rules are competitive or outperform existing rules developed by researchers. Genetic programming is one of the most popular approaches to discovering dispatching rules in the literature, especially for complex production systems. However, the large heuristic search space may restrict genetic programming from finding near optimal dispatching rules. This article develops a new hybrid genetic programming algorithm for dynamic job shop scheduling based on a new representation, a new local search heuristic, and efficient fitness evaluators. Experiments show that the new method is effective regarding the quality of evolved rules. Moreover, evolved rules are also significantly smaller and contain more relevant attributes.

scholarly journals A Hybrid Genetic Programming Algorithm for Automated Design of Dispatching Rules

2019 ◽  
Vol 27 (3) ◽  
pp. 467-496 ◽  
Author(s):  
Su Nguyen ◽  
Yi Mei ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Production Systems ◽  
Search Space ◽  
Automated Design ◽  
Dispatching Rules ◽  
Programming Algorithm ◽  
Local Search Heuristic

Designing effective dispatching rules for production systems is a difficult and time-consuming task if it is done manually. In the last decade, the growth of computing power, advanced machine learning, and optimisation techniques has made the automated design of dispatching rules possible and automatically discovered rules are competitive or outperform existing rules developed by researchers. Genetic programming is one of the most popular approaches to discovering dispatching rules in the literature, especially for complex production systems. However, the large heuristic search space may restrict genetic programming from finding near optimal dispatching rules. This article develops a new hybrid genetic programming algorithm for dynamic job shop scheduling based on a new representation, a new local search heuristic, and efficient fitness evaluators. Experiments show that the new method is effective regarding the quality of evolved rules. Moreover, evolved rules are also significantly smaller and contain more relevant attributes.

scholarly journals A Computational Study of Representations in Genetic Programming to Evolve Dispatching Rules for the Job Shop Scheduling Problem

2020 ◽  
Author(s):  
S Nguyen ◽  
Mengjie Zhang ◽  
M Johnston ◽  
K Chen Tan
Keyword(s):  
Genetic Programming ◽  
Manufacturing Systems ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Computational Study ◽  
Dispatching Rules ◽  
Scheduling Problem ◽  
Learning Approaches ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

Designing effective dispatching rules is an important factor for many manufacturing systems. However, this time-consuming process has been performed manually for a very long time. Recently, some machine learning approaches have been proposed to support this task. In this paper, we investigate the use of genetic programming for automatically discovering new dispatching rules for the single objective job shop scheduling problem (JSP). Different representations of the dispatching rules in the literature are newly proposed in this paper and are compared and analysed. Experimental results show that the representation that integrates system and machine attributes can improve the quality of the evolved rules. Analysis of the evolved rules also provides useful knowledge about how these rules can effectively solve JSP. © 1997-2012 IEEE.

scholarly journals An investigation of ensemble combination schemes for genetic programming based hyper-heuristic approaches to dynamic job shop scheduling

2021 ◽  
Author(s):  
John Park ◽  
Yi Mei ◽  
Su Nguyen ◽  
Gang Chen ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Linear Combination ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Majority Voting ◽  
Dispatching Rules ◽  
Shop Scheduling ◽  
Weighted Majority ◽  
Dynamic Job Shop ◽  
Weighted Majority Voting

Genetic programming based hyper-heuristic (GP-HH) approaches that evolve ensembles of dispatching rules have been effectively applied to dynamic job shop scheduling (JSS) problems. Ensemble GP-HH approaches have been shown to be more robust than existing GP-HH approaches that evolve single dispatching rules for dynamic JSS problems. For ensemble learning in classification, the design of how the members of the ensembles interact with each other, e.g., through various combination schemes, is important for developing effective ensembles for specific problems. In this paper, we investigate and carry out systematic analysis for four popular combination schemes. They are majority voting, which has been applied to dynamic JSS, followed by linear combination, weighted majority voting and weighted linear combination, which have not been applied to dynamic JSS. In addition, we propose several mea-sures for analysing the decision making process in the ensembles evolved by GP. The results show that linear combination is generally better for the dynamic JSS problem than the other combination schemes investigated. In addition, the different combination schemes result in significantly different interactions between the members of the ensembles. Finally, the analysis based on the measures shows that the behaviours of the evolved ensembles are significantly affected by the combination schemes. Weighted majority voting has bias towards single members of the ensembles. © This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/

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