@inbook{e2ab2bc4cf6e429d84644b758f470ab8,
title = "Dynamic multi-objective job shop scheduling: A genetic programming approach",
abstract = "Handling multiple conflicting objectives in dynamic job shop scheduling is challenging because many aspects of the problem need to be considered when designing dispatching rules. A multi-objective genetic programming based hyperheuristic (MO-GPHH) method is investigated here to facilitate the designing task. The goal of this method is to evolve a Pareto front of non-dominated dispatching rules which can be used to support the decision makers by providing them with potential trade-offs among different objectives. The experimental results under different shop conditions suggest that the evolved Pareto front contains very effective rules. Some extensive analyses are also presented to help confirm the quality of the evolved rules. The Pareto front obtained can cover a much wider ranges of rules as compared to a large number of dispatching rules reported in the literature.Moreover, it is also shown that the evolved rules are robust across different shop conditions. {\textcopyright} 2013 Springer-Verlag Berlin Heidelberg.",
author = "Su Nguyen and Mengjie Zhang and Mark Johnston and Tan, {Kay Chen}",
year = "2013",
doi = "10.1007/978-3-642-39304-4_10",
language = "English",
isbn = "978-3-642-39303-7",
series = "Studies in Computational Intelligence",
publisher = "Springer Berlin Heidelberg",
pages = "251--282",
editor = "Uyar, {A. Sima } and Ozcan, {Ender } and Urquhart, {Neil }",
booktitle = "Automated Scheduling and Planning",
}