Paper Info
Title
Auction-based approach with improved disjunctive graph model for job shop scheduling problem with parallel batch processing
Abstract
The job-shop scheduling problem (JSSP) is encountered in several industries, including the military where heat treatment is applied prior to the machining process in production. This study aims to minimize the overall make-span of a JSSP with parallel batch processing. The problem is formulated as a mixed-integer linear programming model. Feasible solutions are derived from an auction-based approach for forming batches, allocating operation machines, and scheduling. An improved disjunctive graph model is further developed to search for better solutions. We conduct numerical experiments to test a set of benchmark instances. A comparison of the results with those obtained applying other existing algorithms and CPLEX demonstrates the effectiveness and stability of the proposed auction-based approach and improved graph model. Furthermore, a statistical analysis using IBM SPSS shows that the proposed auction-based approach has an absolute advantage in solving medium-scale and large-scale instances of JSSP with batch processing.
Year
DOI
Venue
2022
10.1016/j.engappai.2022.104735
Engineering Applications of Artificial Intelligence
Keywords
DocType
Volume
Job shop scheduling,Parallel batch processing,Auction-based approach,Improved disjunctive graph model
Journal
110
ISSN
Citations 
PageRank 
0952-1976
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Chengkuan Zeng100.34
Guiqing Qi200.34
Zixuan Liu300.34
Jiafu Tang454149.29
Zhi-Ping Fan5161792.73
Chongjun Yan600.34