Genetic programming for evolving due-date assignment models in job shop environments

Authors:
Su Nguyen;Mengjie Zhang;Mark Johnston;Kay Chen Tan
Affiliations:
-;-;-;-
Venue:
Evolutionary Computation
Year:
2014

Citing 19
Cited 0

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Abstract

Due-date assignment plays an important role in scheduling systems and strongly influences the delivery performance of job shops. Because of the stochastic and dynamic nature of job shops, the development of general due-date assignment models DDAMs is complicated. In this study, two genetic programming GP methods are proposed to evolve DDAMs for job shop environments. The experimental results show that the evolved DDAMs can make more accurate estimates than other existing dynamic DDAMs with promising reusability. In addition, the evolved operation-based DDAMs show better performance than the evolved DDAMs employing aggregate information of jobs and machines.