Publication Type

Journal Article

Version

acceptedVersion

Publication Date

6-2026

Abstract

Batch processing machines (BPMs) are widely used in industries such as semiconductors, metal processing, and healthcare, where jobs are processed in batches. As production, inventory, and distribution become increasingly integrated to improve efficiency, research on their joint scheduling in parallel BPM environments remains scarce. This paper addresses the integrated scheduling problem in parallel BPMs, involving production, inventory, and distribution stages, with the objective of minimizing total costs. A unified cost-based model is first formulated, applicable to both in-facility and external distribution scenarios. A hybrid algorithm framework, HyDPN, combining deep reinforcement learning, dynamic programming, and neighborhood operations is proposed. Extensive experiments demonstrate that HyDPN consistently outperforms existing heuristic algorithms and state-of-the-art algorithms. The results indicate that the proposed framework enables integrated scheduling in parallel BPMs, reducing production, inventory, and distribution costs in multi-stage supply chains.

Keywords

Deep reinforcement learning, Hybrid algorithm framework, Integrated scheduling, Parallel batch processing machines

Discipline

Operations Research, Systems Engineering and Industrial Engineering | Software Engineering

Research Areas

Intelligent Systems and Optimization

Publication

Computers & Industrial Engineering

Volume

216

First Page

1

Last Page

22

ISSN

0360-8352

Identifier

10.1016/j.cie.2026.111960

Publisher

Elsevier

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1016/j.cie.2026.111960

Download

Share

COinS