Case study analysis and genetic algorithm adaptation for job process planning and scheduling in batch production
(2014) In Journal of Design Research 12(1-2). p.52-77- Abstract
- The paper presents an application of job process planning and scheduling into the production of turned parts. Both planning and scheduling are controlled by the genetic algorithm (GA) approach in order to achieve optimum plans. Genetic algorithms are one of the artificial intelligence methods. With GA we are searching in an iterative manner for possible schedules taking into account the limitations of the process. They imitate the Darwin theory of the development of living beings and natural selection. The objective of this paper is to present scheduling model and prove that the genetic algorithm could be applied to various technical problems with some adaptations. The article describes in detail the optimisation process of genetic... (More)
- The paper presents an application of job process planning and scheduling into the production of turned parts. Both planning and scheduling are controlled by the genetic algorithm (GA) approach in order to achieve optimum plans. Genetic algorithms are one of the artificial intelligence methods. With GA we are searching in an iterative manner for possible schedules taking into account the limitations of the process. They imitate the Darwin theory of the development of living beings and natural selection. The objective of this paper is to present scheduling model and prove that the genetic algorithm could be applied to various technical problems with some adaptations. The article describes in detail the optimisation process of genetic algorithm, chromosome representation, selection, genetic operators and parameter settings. Some programming code details in the Visual Basic (VB) language are added for clearer presentation. The orders on the machines are scheduled on the basis of a GA, according to the target function criteria. With the GA throughput time, makespan and costs were reduced. Special attention was put on the integration of the improved scheduling algorithm into existing information system. (Less)
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https://lup.lub.lu.se/record/9b2801a0-25a7-4bd3-ae55-aee6c3cdc69a
- author
- Slak, Ales ; Tavcar, Joze LU and Duhovnik, Joze
- publishing date
- 2014
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Journal of Design Research
- volume
- 12
- issue
- 1-2
- pages
- 26 pages
- publisher
- Inderscience Publishers
- external identifiers
-
- scopus:84900443586
- ISSN
- 1748-3050
- DOI
- 10.1504/JDR.2014.060934
- language
- English
- LU publication?
- no
- id
- 9b2801a0-25a7-4bd3-ae55-aee6c3cdc69a
- date added to LUP
- 2020-10-13 16:42:44
- date last changed
- 2022-02-01 17:00:35
@article{9b2801a0-25a7-4bd3-ae55-aee6c3cdc69a,
abstract = {{The paper presents an application of job process planning and scheduling into the production of turned parts. Both planning and scheduling are controlled by the genetic algorithm (GA) approach in order to achieve optimum plans. Genetic algorithms are one of the artificial intelligence methods. With GA we are searching in an iterative manner for possible schedules taking into account the limitations of the process. They imitate the Darwin theory of the development of living beings and natural selection. The objective of this paper is to present scheduling model and prove that the genetic algorithm could be applied to various technical problems with some adaptations. The article describes in detail the optimisation process of genetic algorithm, chromosome representation, selection, genetic operators and parameter settings. Some programming code details in the Visual Basic (VB) language are added for clearer presentation. The orders on the machines are scheduled on the basis of a GA, according to the target function criteria. With the GA throughput time, makespan and costs were reduced. Special attention was put on the integration of the improved scheduling algorithm into existing information system.}},
author = {{Slak, Ales and Tavcar, Joze and Duhovnik, Joze}},
issn = {{1748-3050}},
language = {{eng}},
number = {{1-2}},
pages = {{52--77}},
publisher = {{Inderscience Publishers}},
series = {{Journal of Design Research}},
title = {{Case study analysis and genetic algorithm adaptation for job process planning and scheduling in batch production}},
url = {{http://dx.doi.org/10.1504/JDR.2014.060934}},
doi = {{10.1504/JDR.2014.060934}},
volume = {{12}},
year = {{2014}},
}