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Title:Genetic algorithm-based approach for makespan minimization in a flow shop with queue time limits and skip-ping jobs
Authors:ID Han, J. H. (Author)
ID Lee, J. Y. (Author)
Files:.pdf APEM18-2_152-162.pdf (670,82 KB)
MD5: 775340C25ADDD23AD67F8548A909412C
 
URL https://apem-journal.org/Archives/2023/Abstract-APEM18-2_152-162.html
 
Language:English
Work type:Article
Typology:1.01 - Original Scientific Article
Organization:FS - Faculty of Mechanical Engineering
Abstract:This study investigates a flow shop scheduling problem with queue time limits and skipping jobs, which are common scheduling requirements for semiconductor and printed circuit board manufacturing systems. These manufacturing systems involve the most complex processes, which are strictly controlled and constrained to manufacture high-quality products and satisfy dynamic customer orders. Further, queue times between consecutive stages are limited. Given that the queue times are limited, jobs must begin the next step within the maximum queue time after the jobs in the previous step are completed. In the considered flow shop, several jobs can skip the first step, referred to as skipping jobs. Skipping jobs exist because of multiple types of products processed in the same flow shop. For the considered flow shop, this paper proposes a mathematical programming formulation and a genetic algorithm to minimize the makespan. The GA demonstrated its strengths through comprehensive computational experiments, demonstrating its effectiveness and efficiency. As the problem size increased, the GA's performance improved noticeably, while maintaining acceptable computation times for real-world fab facilities. We also validated its performance in various scenarios involving queue time limits and skipping jobs, to further emphasize its capabilities.
Keywords:scheduling, flow shop, makespan, queue time limits, skipping jobs, optimization, modelling, genetic algorithm
Publication status:Published
Publication version:Version of Record
Submitted for review:09.10.2022
Article acceptance date:06.07.2023
Publication date:23.07.2023
Publisher:Chair of Production Engineering (CPE), University of Maribor Faculty of Mechanical Engineering
Year of publishing:2023
Number of pages:str. 152-162
Numbering:Vol. 18, no. 2
PID:20.500.12556/DKUM-97040 New window
UDC:004.8
ISSN on article:1854-6250
COBISS.SI-ID:268223747 New window
DOI:10.14743/apem2023.2.463 New window
Copyright:Content form this work may be used under the terms of the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
Publication date in DKUM:13.02.2026
Views:153
Downloads:1
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Categories:Misc.
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Record is a part of a journal

Title:Advances in production engineering & management
Shortened title:Adv produc engineer manag
Publisher:Fakulteta za strojništvo, Inštitut za proizvodno strojništvo
ISSN:1854-6250
COBISS.SI-ID:229859072 New window

Document is financed by a project

Funder:the Ministry of SMEs and Startups (MSS, Korea)
Funding programme:the Technology Development Program
Project number:RS-2022-00140527

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

Language:Slovenian
Keywords:genetski algoritmi, modeliranje, optimizacija


Collection

This document is a part of these collections:
  1. Advances in production engineering & management

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