Investigating a Machine Breakdown Genetic Programming Approach for Dynamic Job Shop Scheduling

2018 ◽  
pp. 253-270 ◽  
Author(s):  
John Park ◽  
Yi Mei ◽  
Su Nguyen ◽  
Gang Chen ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Programming Approach ◽  
Machine Breakdown ◽  
Shop Scheduling ◽  
Dynamic Job Shop

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/

Adaptive Multitask Genetic Programming for Dynamic Job Shop Scheduling

2021 ◽  
pp. 271-290
Author(s):  
Fangfang Zhang ◽  
Su Nguyen ◽  
Yi Mei ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Shop Scheduling ◽  
Dynamic Job Shop
Sign in / Sign up

Export Citation Format

Share Document