scholarly journals Genetic programming for job shop scheduling

2020 ◽  
Author(s):  
S Nguyen ◽  
Mengjie Zhang ◽  
M Johnston ◽  
KC Tan
Keyword(s):  
Genetic Programming ◽  
Manufacturing System ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Early Stage ◽  
Dispatching Rules ◽  
Job Shops ◽  
Future Studies ◽  
Shop Scheduling ◽  
International Publishing

© 2019, Springer International Publishing AG, part of Springer Nature. Designing effective scheduling rules or heuristics for a manufacturing system such as job shops is not a trivial task. In the early stage, scheduling experts rely on their experiences to develop dispatching rules and further improve them through trials-and-errors, sometimes with the help of computer simulations. In recent years, automated design approaches have been applied to develop effective dispatching rules for job shop scheduling (JSS). Genetic programming (GP) is currently the most popular approach for this task. The goal of this chapter is to summarise existing studies in this field to provide an overall picture to interested researchers. Then, we demonstrate some recent ideas to enhance the effectiveness of GP for JSS and discuss interesting research topics for future studies.

scholarly journals Genetic programming for job shop scheduling

2020 ◽  
Author(s):  
S Nguyen ◽  
Mengjie Zhang ◽  
M Johnston ◽  
KC Tan
Keyword(s):  
Genetic Programming ◽  
Manufacturing System ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Early Stage ◽  
Dispatching Rules ◽  
Job Shops ◽  
Future Studies ◽  
Shop Scheduling ◽  
International Publishing

© 2019, Springer International Publishing AG, part of Springer Nature. Designing effective scheduling rules or heuristics for a manufacturing system such as job shops is not a trivial task. In the early stage, scheduling experts rely on their experiences to develop dispatching rules and further improve them through trials-and-errors, sometimes with the help of computer simulations. In recent years, automated design approaches have been applied to develop effective dispatching rules for job shop scheduling (JSS). Genetic programming (GP) is currently the most popular approach for this task. The goal of this chapter is to summarise existing studies in this field to provide an overall picture to interested researchers. Then, we demonstrate some recent ideas to enhance the effectiveness of GP for JSS and discuss interesting research topics for future studies.

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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