Optimization of machine tool processing scheduling based on differential evolution algorithm

Yuehong Zhang and Mianhao Zhang

PLOS ONE, 2025, vol. 20, issue 10, 1-19

Abstract: Machine tool processing scheduling plays a pivotal role in modern manufacturing systems, significantly influencing production efficiency, resource utilization, and timely delivery. Due to its combinatorial and NP-hard characteristics, traditional optimization techniques often face challenges when dealing with large-scale and complex scheduling problems. In this paper, we present an optimization approach for machine tool scheduling that leverages the Differential Evolution (DE) algorithm. By tailoring DE for discrete scheduling environments through specialized encoding and decoding techniques, the algorithm is able to effectively explore the solution space while ensuring the generation of feasible schedules. The results from our experiments reveal that the proposed approach outperforms conventional heuristic methods, particularly in minimizing makespan and achieving a balanced workload distribution across machines. This study underscores the potential of DE as a robust, adaptive, and efficient optimization tool for tackling complex scheduling problems in the context of intelligent manufacturing systems.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0333691 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 33691&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0333691

DOI: 10.1371/journal.pone.0333691

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().