Minimizing total flow time on a batch processing machine using a hybrid max–min ant system

•The problem of minimizing total flow time on a single batch processing machine with non-identical job sizes is addressed.•A binary mixed integer programming model is formulated.•An effective hybrid max–min ant system is proposed.•The effectiveness of the proposed approach is evaluated using randoml...

Full description

Saved in:
Bibliographic Details
Published in:Computers & industrial engineering Vol. 99; pp. 372 - 381
Main Authors: Rafiee Parsa, N., Karimi, B., Moattar Husseini, S.M.
Format: Journal Article
Language:English
Published: New York Elsevier Ltd 01.09.2016
Pergamon Press Inc
Subjects:
Algorithms
Ant colony optimization
Batch processing
Batch processing machines
Computer simulation
Dynamic programming
Dynamical systems
Heuristic
Integer programming
Job shops
Mathematical models
Max–min ant system
Optimization algorithms
Polymethyl methacrylates
Production scheduling
Searching
Semiconductor manufacturing
Studies
Total flow time
Total flow time
Max–min ant system
Dynamic programming
Semiconductor manufacturing
Batch processing machines
ISSN:0360-8352, 1879-0550
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•The problem of minimizing total flow time on a single batch processing machine with non-identical job sizes is addressed.•A binary mixed integer programming model is formulated.•An effective hybrid max–min ant system is proposed.•The effectiveness of the proposed approach is evaluated using randomly generated test instances. A single batch processing machine scheduling problem with non-identical job sizes to minimize the total flow time is investigated. The problem is formulated as a binary mixed integer programming model. Since the research problem is shown to be NP-hard, a hybrid metaheuristic algorithm based on the max–min ant system (MMAS) is proposed. MMAS is an ant colony optimization algorithm derived from ant system. In the proposed algorithm, first, a sequence of jobs is constructed based on the MMAS algorithm. Then, a dynamic programming algorithm is applied to obtain the optimal batching for the given job sequence. At last, an effective local search procedure is embedded in the algorithm for finding higher quality solutions. The performance of the proposed algorithm is compared with CPLEX and available heuristics in the literature. Computational results demonstrate the efficacy of the proposed algorithm in terms of the solution quality.
Bibliography:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-1
ObjectType-Feature-2
content type line 23
ISSN:0360-8352
1879-0550
DOI:10.1016/j.cie.2016.06.008