Minimising Total Flowtime in a No-Wait Flow Shop (NWFS) using Genetic Algorithms
This paper considers a no-wait flow shop scheduling (NWFS) problem, where the objective is to minimise the total flowtime. We propose a genetic algorithm (GA) that is implemented in a spreadsheet environment. The GA functions as an add-in in the spreadsheet. It is demonstrated that with proposed app...
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Universidad Nacional de Colombia
2018-09-01
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doaj-6c5d059e951a4c37ac84d41a898702482020-11-25T01:17:56ZengUniversidad Nacional de ColombiaIngeniería e Investigación0120-56092248-87232018-09-01383687910.15446/ing.investig.v38n3.7528150071Minimising Total Flowtime in a No-Wait Flow Shop (NWFS) using Genetic AlgorithmsImran Ali Chaudhry0Isam AbdulQader Elbadawi1Muhammad Usman2Muhammad Tajammal Chughtai3University of Hail Kingdom of Saudi ArabiaUniversity of Hail Kingdom of Saudi ArabiaUniversity of Hail Kingdom of Saudi ArabiaUniversity of Hail Kingdom of Saudi ArabiaThis paper considers a no-wait flow shop scheduling (NWFS) problem, where the objective is to minimise the total flowtime. We propose a genetic algorithm (GA) that is implemented in a spreadsheet environment. The GA functions as an add-in in the spreadsheet. It is demonstrated that with proposed approach any criteria can be optimised without modifying the GA routine or spreadsheet model. Furthermore, the proposed method for solving this class of problem is general purpose, as it can be easily customised by adding or removing jobs and machines. Several benchmark problems already published in the literature are used to demonstrate the problem-solving capability of the proposed approach. Benchmark problems set ranges from small (7-jobs, 7 machines) to large (100-jobs, 10-machines). The performance of the GA is compared with different meta-heuristic techniques used in earlier literature. Experimental analysis demonstrate that solutions obtained in this research offer equal quality as compared to algorithms already developed for NWFS problems.https://revistas.unal.edu.co/index.php/ingeinv/article/view/75281Genetic algorithm (GA)SchedulingNo-waitFlow shop |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Imran Ali Chaudhry Isam AbdulQader Elbadawi Muhammad Usman Muhammad Tajammal Chughtai |
spellingShingle |
Imran Ali Chaudhry Isam AbdulQader Elbadawi Muhammad Usman Muhammad Tajammal Chughtai Minimising Total Flowtime in a No-Wait Flow Shop (NWFS) using Genetic Algorithms Ingeniería e Investigación Genetic algorithm (GA) Scheduling No-wait Flow shop |
author_facet |
Imran Ali Chaudhry Isam AbdulQader Elbadawi Muhammad Usman Muhammad Tajammal Chughtai |
author_sort |
Imran Ali Chaudhry |
title |
Minimising Total Flowtime in a No-Wait Flow Shop (NWFS) using Genetic Algorithms |
title_short |
Minimising Total Flowtime in a No-Wait Flow Shop (NWFS) using Genetic Algorithms |
title_full |
Minimising Total Flowtime in a No-Wait Flow Shop (NWFS) using Genetic Algorithms |
title_fullStr |
Minimising Total Flowtime in a No-Wait Flow Shop (NWFS) using Genetic Algorithms |
title_full_unstemmed |
Minimising Total Flowtime in a No-Wait Flow Shop (NWFS) using Genetic Algorithms |
title_sort |
minimising total flowtime in a no-wait flow shop (nwfs) using genetic algorithms |
publisher |
Universidad Nacional de Colombia |
series |
Ingeniería e Investigación |
issn |
0120-5609 2248-8723 |
publishDate |
2018-09-01 |
description |
This paper considers a no-wait flow shop scheduling (NWFS) problem, where the objective is to minimise the total flowtime. We propose a genetic algorithm (GA) that is implemented in a spreadsheet environment. The GA functions as an add-in in the spreadsheet. It is demonstrated that with proposed approach any criteria can be optimised without modifying the GA routine or spreadsheet model. Furthermore, the proposed method for solving this class of problem is general purpose, as it can be easily customised by adding or removing jobs and machines. Several benchmark problems already published in the literature are used to demonstrate the problem-solving capability of the proposed approach. Benchmark problems set ranges from small (7-jobs, 7 machines) to large (100-jobs, 10-machines). The performance of the GA is compared with different meta-heuristic techniques used in earlier literature. Experimental analysis demonstrate that solutions obtained in this research offer equal quality as compared to algorithms already developed for NWFS problems. |
topic |
Genetic algorithm (GA) Scheduling No-wait Flow shop |
url |
https://revistas.unal.edu.co/index.php/ingeinv/article/view/75281 |
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