Content area

Abstract

This paper addresses a hybrid processing system in automotive mold casting, which involves single processing machines and parallel batch processing machines. A job shop scheduling problem with parallel batch processing machines (JSP-PBPM) is developed, with the objective of minimizing the maximum completion time. First, a solution decoding strategy combined with the JSP-PBPM problem and a batch job addition algorithm is proposed. This approach addresses the impact of operation precedence relationships on conventional decoding strategies and aims to maximize the utilization of parallel batch processing machines for batch operations. Next, an Improved Scatter Search (ISS) algorithm is introduced to solve the problem. The ISS algorithm finds the optimal solution through several steps, including the construction of the initial population, improvement of the initial solution, creation of a reference set, generation of subsets, and refinement of the final solution. Finally, simulation experiments are conducted to verify the feasibility and effectiveness of the proposed algorithm and decoding strategy in solving such problems.

Details

1009240
Business indexing term
Title
An improved scatter search algorithm for solving job shop scheduling problems with parallel batch processing machine
Volume
15
Issue
1
Pages
11872
Publication year
2025
Publication date
2025
Publisher
Nature Publishing Group
Place of publication
London
Country of publication
United States
Publication subject
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
 
 
Online publication date
2025-04-07
Milestone dates
2025-03-03 (Registration); 2025-01-21 (Received); 2025-03-03 (Accepted)
Publication history
 
 
   First posting date
07 Apr 2025
ProQuest document ID
3188186226
Document URL
https://www.proquest.com/scholarly-journals/improved-scatter-search-algorithm-solving-job/docview/3188186226/se-2?accountid=208611
Copyright
Copyright Nature Publishing Group 2025
Last updated
2025-04-10
Database
ProQuest One Academic