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Guided genetic algorithm for solving unrelated parallel machine scheduling problem with additional resources

This paper solved the unrelated parallel machine scheduling with additional resources (UPMR) problem. The processing time and the number of required resources for each job rely on the machine that does the processing. Each job j needed units of resources (rjm) during its time of processing on a mach...

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Main Authors: Abed, Munther Hameed, Mohd Nizam Mohmad, Kahar
Format: Article
Language:English
Published: Institute of Advanced Engineering and Science 2022
Subjects:
QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
Online Access:http://umpir.ump.edu.my/id/eprint/34633/1/Guided%20genetic%20algorithm%20for%20solving%20unrelated%20parallel%20machine%20scheduling.pdf
http://umpir.ump.edu.my/id/eprint/34633/
https://doi.org/10.11591/ijeecs.v26.i2.pp1036-1049
https://doi.org/10.11591/ijeecs.v26.i2.pp1036-1049
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spelling my.ump.umpir.346332023-03-14T06:46:42Z http://umpir.ump.edu.my/id/eprint/34633/ Guided genetic algorithm for solving unrelated parallel machine scheduling problem with additional resources Abed, Munther Hameed Mohd Nizam Mohmad, Kahar QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) This paper solved the unrelated parallel machine scheduling with additional resources (UPMR) problem. The processing time and the number of required resources for each job rely on the machine that does the processing. Each job j needed units of resources (rjm) during its time of processing on a machine m. These additional resources are limited, and this made the UPMR a difficult problem to solve. In this study, the maximum completion time of jobs makespan must be minimized. Here, we proposed genetic algorithm (GA) to solve the UPMR problem because of the robustness and the success of GA in solving many optimization problems. An enhancement of GA was also proposed in this work. Generally, the experiment involves tuning the parameters of GA. Additionally, an appropriate selection of GA operators was also experimented. The guide genetic algorithm (GGA) is not used to solve the unspecified dynamic UPMR. Besides, the utilization of parameters tuning and operators gave a balance between exploration and exploitation and thus help the search escape the local optimum. Results show that the GGA outperforms the simple genetic algorithm (SGA), but it still didn't match the results in the literature. On the other hand, GGA significantly outperforms all methods in terms of CPU time. Institute of Advanced Engineering and Science 2022-05 Article PeerReviewed pdf en cc_by_sa_4 http://umpir.ump.edu.my/id/eprint/34633/1/Guided%20genetic%20algorithm%20for%20solving%20unrelated%20parallel%20machine%20scheduling.pdf Abed, Munther Hameed and Mohd Nizam Mohmad, Kahar (2022) Guided genetic algorithm for solving unrelated parallel machine scheduling problem with additional resources. Indonesian Journal of Electrical Engineering and Computer Science, 26 (2). pp. 1036-1049. ISSN 2502-4752 https://doi.org/10.11591/ijeecs.v26.i2.pp1036-1049 https://doi.org/10.11591/ijeecs.v26.i2.pp1036-1049
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
spellingShingle QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
Abed, Munther Hameed
Mohd Nizam Mohmad, Kahar
Guided genetic algorithm for solving unrelated parallel machine scheduling problem with additional resources
description This paper solved the unrelated parallel machine scheduling with additional resources (UPMR) problem. The processing time and the number of required resources for each job rely on the machine that does the processing. Each job j needed units of resources (rjm) during its time of processing on a machine m. These additional resources are limited, and this made the UPMR a difficult problem to solve. In this study, the maximum completion time of jobs makespan must be minimized. Here, we proposed genetic algorithm (GA) to solve the UPMR problem because of the robustness and the success of GA in solving many optimization problems. An enhancement of GA was also proposed in this work. Generally, the experiment involves tuning the parameters of GA. Additionally, an appropriate selection of GA operators was also experimented. The guide genetic algorithm (GGA) is not used to solve the unspecified dynamic UPMR. Besides, the utilization of parameters tuning and operators gave a balance between exploration and exploitation and thus help the search escape the local optimum. Results show that the GGA outperforms the simple genetic algorithm (SGA), but it still didn't match the results in the literature. On the other hand, GGA significantly outperforms all methods in terms of CPU time.
format Article
author Abed, Munther Hameed
Mohd Nizam Mohmad, Kahar
author_facet Abed, Munther Hameed
Mohd Nizam Mohmad, Kahar
author_sort Abed, Munther Hameed
title Guided genetic algorithm for solving unrelated parallel machine scheduling problem with additional resources
title_short Guided genetic algorithm for solving unrelated parallel machine scheduling problem with additional resources
title_full Guided genetic algorithm for solving unrelated parallel machine scheduling problem with additional resources
title_fullStr Guided genetic algorithm for solving unrelated parallel machine scheduling problem with additional resources
title_full_unstemmed Guided genetic algorithm for solving unrelated parallel machine scheduling problem with additional resources
title_sort guided genetic algorithm for solving unrelated parallel machine scheduling problem with additional resources
publisher Institute of Advanced Engineering and Science
publishDate 2022
url http://umpir.ump.edu.my/id/eprint/34633/1/Guided%20genetic%20algorithm%20for%20solving%20unrelated%20parallel%20machine%20scheduling.pdf
http://umpir.ump.edu.my/id/eprint/34633/
https://doi.org/10.11591/ijeecs.v26.i2.pp1036-1049
https://doi.org/10.11591/ijeecs.v26.i2.pp1036-1049
_version_ 1761616581281972224
score 13.211869

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