Automatic Design of Scheduling Policies for Dynamic Multi-objective Job Shop Scheduling via Cooperative Coevolution Genetic Programming

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

49 Scopus Citations
View graph of relations

Author(s)

Detail(s)

Original languageEnglish
Article number6468087
Pages (from-to)193-208
Journal / PublicationIEEE Transactions on Evolutionary Computation
Volume18
Issue number2
StatePublished - Apr 2014
Externally publishedYes

Abstract

A scheduling policy strongly influences the performance of a manufacturing system. However, the design of an effective scheduling policy is complicated and time consuming due to the complexity of each scheduling decision, as well as the interactions among these decisions. This paper develops four new multi-objective genetic programming-based hyperheuristic (MO-GPHH) methods for automatic design of scheduling policies, including dispatching rules and due-date assignment rules in job shop environments. In addition to using three existing search strategies, nondominated sorting genetic algorithm II, strength Pareto evolutionary algorithm 2, and harmonic distance-based multi-objective evolutionary algorithm, to develop new MO-GPHH methods, a new approach called diversified multi-objective cooperative evolution (DMOCC) is also proposed. The novelty of these MO-GPHH methods is that they are able to handle multiple scheduling decisions simultaneously. The experimental results show that the evolved Pareto fronts represent effective scheduling policies that can dominate scheduling policies from combinations of existing dispatching rules with dynamic/regression-based due-date assignment rules. The evolved scheduling policies also show dominating performance on unseen simulation scenarios with different shop settings. In addition, the uniformity of the scheduling policies obtained from the proposed method of DMOCC is better than those evolved by other evolutionary approaches. © 1997-2012 IEEE.

Research Area(s)

  • Dispatching rule (DR), genetic programming (GP), hyperheuristic, job shop scheduling (JSS)

Citation Format(s)

Automatic Design of Scheduling Policies for Dynamic Multi-objective Job Shop Scheduling via Cooperative Coevolution Genetic Programming. / Nguyen, Su; Zhang, Mengjie; Johnston, Mark; Tan, Kay Chen.

In: IEEE Transactions on Evolutionary Computation, Vol. 18, No. 2, 6468087, 04.2014, p. 193-208.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal