Multi-Level Subpopulation-Based Particle Swarm Optimization Algorithm for Hybrid Flow Shop Scheduling Problem with Limited Buffers

The shop scheduling problem with limited buffers has broad applications in real-world production scenarios, so this research direction is of great practical significance. However, there is currently little research on the hybrid flow shop scheduling problem with limited buffers (LBHFSP). This paper...

Full description

Saved in:
Bibliographic Details
Published in:Computers, materials & continua Vol. 84; no. 2; pp. 2305 - 2330
Main Authors: Zou, Yuan, Lu, Chao, Yin, Lvjiang, Wen, Xiaoyu
Format: Journal Article
Language:English
Published: Henderson Tech Science Press 2025
Subjects:
Algorithms
Buffers
Completion time
Decoding
Job shop scheduling
Optimization algorithms
Particle swarm optimization
Search methods
ISSN:1546-2226, 1546-2218, 1546-2226
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The shop scheduling problem with limited buffers has broad applications in real-world production scenarios, so this research direction is of great practical significance. However, there is currently little research on the hybrid flow shop scheduling problem with limited buffers (LBHFSP). This paper deeply investigates the LBHFSP to optimize the goal of the total completion time. To better solve the LBHFSP, a multi-level subpopulation-based particle swarm optimization algorithm (MLPSO) is proposed, which is founded on the attributes of the LBHFSP and the shortcomings of the basic PSO (particle swarm optimization) algorithm. In MLPSO, firstly, considering the impact of the limited buffers on the process of subsequent operations, a specific circular decoding strategy is developed to accommodate the characteristics of limited buffers. Secondly, an initialization strategy based on blocking time is designed to enhance the quality and diversity of the initial population. Afterward, a multi-level subpopulation collaborative search is developed to prevent being trapped in a local optimum and improve the global exploration capability. Additionally, a local search strategy based on the first blocked job is designed to enhance the MLPSO algorithm’s exploitation capability. Lastly, numerous experiments are carried out to test the performance of the proposed MLPSO by comparing it with classical intelligent optimization and popular algorithms in recent years. The results confirm that the proposed MLPSO has an outstanding performance when compared to other algorithms when solving LBHFSP.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1546-2226
1546-2218
1546-2226
DOI:10.32604/cmc.2025.065972