Automatic Design of Scheduling Policies for Dynamic Multi-objective Job Shop Scheduling via Cooperative Coevolution Genetic Programming .pdf (6.18 MB)

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

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journal contribution
posted on 27.10.2020, 21:46 by S Nguyen, Mengjie Zhang, M Johnston, K Chen Tan
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.

Funding

Genetic Programming for Job Shop Scheduling | Funder: ROYAL SOCIETY OF NEW ZEALAND | Grant ID: 12-VUW-134

History

Preferred citation

Nguyen, S., Zhang, M., Johnston, M. & Chen Tan, K. (2014). Automatic Design of Scheduling Policies for Dynamic Multi-objective Job Shop Scheduling via Cooperative Coevolution Genetic Programming. IEEE Transactions on Evolutionary Computation, 18(2), 193-208. https://doi.org/10.1109/TEVC.2013.2248159

Journal title

IEEE Transactions on Evolutionary Computation

Volume

18

Issue

2

Publication date

01/04/2014

Pagination

193-208

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Publication status

Published

Contribution type

Article

Online publication date

21/02/2013

ISSN

1089-778X

eISSN

1941-0026

Exports