Evolving reusable operation-based due-date assignment models for job shop scheduling with genetic programming

Authors:
Su Nguyen;Mengjie Zhang;Mark Johnston;Kay Chen Tan
Affiliations:
Victoria University of Wellington, Wellington, New Zealand;Victoria University of Wellington, Wellington, New Zealand;Victoria University of Wellington, Wellington, New Zealand;National University of Singapore, Singapore
Venue:
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
Year:
2012

Citing 5
Cited 2

Genetic programming: on the programming of computers by means of natural selection

Genetic programming: on the programming of computers by means of natural selection
A study of due-date assignment rules with constrained tightness in a dynamic job shop

CIE '96 Proceedings of the 19th international conference on Computers and industrial engineering
Job shop scheduling for missed due-date performance

Computers and Industrial Engineering
Computer simulation of due-date setting in multi-machine job shops

Computers and Industrial Engineering
Towards improved dispatching rules for complex shop floor scenarios: a genetic programming approach

Proceedings of the 12th annual conference on Genetic and evolutionary computation

Learning reusable initial solutions for multi-objective order acceptance and scheduling problems with genetic programming

EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
Genetic programming for evolving due-date assignment models in job shop environments

Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

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 features 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.