Genetic Programming for Evolving Due-Date AssignmentModels in Job Shop Environments

Su Nguyen; Mengjie Zhang; Mark JohnstonKay Chen Tan

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Genetic Programming for Evolving Due-Date AssignmentModels in Job Shop Environments

机译：Genetic Programming for Evolving Due-Date AssignmentModels in Job Shop Environments

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摘要

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.

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来源
《Evolutionary computation》 |2014年第1期|105-138|共34页
作者
Su Nguyen; Mengjie Zhang; Mark JohnstonKay Chen Tan;
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作者单位

Evolutionary Computation Research Group, Victoria University of Wellington, Wellington, New Zealand;

Department of Electrical and Computer Engineering, National University of Singapore, Singapore;

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原文格式 PDF
正文语种英语
中图分类计算技术、计算机技术;
关键词
Genetic programming; job shop; due-date assignment; hyper-heuristics;

Genetic Programming for Evolving Due-Date AssignmentModels in Job Shop Environments

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