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People-Centric Evolutionary System for Dynamic Production Scheduling

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journal contribution
posted on 29.10.2020, 00:54 by Su Nguyen, Mengjie Zhang, Damminda Alahakoon, Kay Chen Tan
Evolving production scheduling heuristics is a challenging task because of the dynamic and complex production environments and the interdependency of multiple scheduling decisions. Different genetic programming (GP) methods have been developed for this task and achieved very encouraging results. However, these methods usually have trouble in discovering powerful and compact heuristics, especially for difficult problems. Moreover, there is no systematic approach for the decision makers to intervene and embed their knowledge and preferences in the evolutionary process. This article develops a novel people-centric evolutionary system for dynamic production scheduling. The two key components of the system are a new mapping technique to incrementally monitor the evolutionary process and a new adaptive surrogate model to improve the efficiency of GP. The experimental results with dynamic flexible job shop scheduling show that the proposed system outperforms the existing algorithms for evolving scheduling heuristics in terms of scheduling performance and heuristic sizes. The new system also allows the decision makers to interact on the fly and guide the evolution toward the desired solutions.

History

Preferred citation

Nguyen, S., Zhang, M., Alahakoon, D. & Tan, K. C. (2019). People-Centric Evolutionary System for Dynamic Production Scheduling. IEEE Transactions on Cybernetics, PP(99), 1-14. https://doi.org/10.1109/tcyb.2019.2936001

Journal title

IEEE Transactions on Cybernetics

Volume

PP

Issue

99

Publication date

01/01/2019

Pagination

1-14

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Publication status

Published

ISSN

2168-2267

eISSN

2168-2275

Language

en

Exports