Staff View: Solving a hybrid jobshop scheduling problem with space constraints and reentrant processesby using an improved hybrid genetic algorithm: a case study

Solving a hybrid jobshop scheduling problem with space constraints and reentrant processesby using an improved hybrid genetic algorithm: a case study

In this paper, several concepts and techniques of a job shop manufacturing system are adjusted and utilized in a real-world pipe spool fabrication shop scheduling problem. In the present study, an improved hybrid genetic algorithm is developed to create a feasible and active schedule for the operati...

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Main Authors:	Moghadam, A. M., Wong, K. Y., Piroozfard, H.
Format:	Article
Published:	University of Cincinnati 2017
Subjects:	HD28 Management. Industrial Management
Online Access:	http://eprints.utm.my/id/eprint/77148/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041473194&partnerID=40&md5=99033bee3207deea45264a3f4eb7a0bb
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id	my.utm.77148
record_format	eprints
spelling	my.utm.771482018-04-30T14:45:06Z http://eprints.utm.my/id/eprint/77148/ Solving a hybrid jobshop scheduling problem with space constraints and reentrant processesby using an improved hybrid genetic algorithm: a case study Moghadam, A. M. Wong, K. Y. Piroozfard, H. HD28 Management. Industrial Management In this paper, several concepts and techniques of a job shop manufacturing system are adjusted and utilized in a real-world pipe spool fabrication shop scheduling problem. In the present study, an improved hybrid genetic algorithm is developed to create a feasible and active schedule for the operational level of pipe spool fabrication shop with the aims of minimizing maximum completion time of all jobs. In the proposed algorithm, an enhanced solution encoding and decoding are used to represent a schedule for the fabrication shop. In order to generate high-quality initial solutions, an operation order-based global selection is designed, where this operator considers operations processing time and workload of machines while assigning machines to each of the proper operations. In the reproduction phase of this algorithm, the precedence preserving order-based crossover, uniform crossover, swapping operator, and intelligent mutation operator are used to produce the offspring and mutants for the sequencing and job routing sub-problems. In addition, a local search operator is utilized to explore the neighborhood of the solution in order to improve the solution quality further. Stepwise delineation of the proposed approach is presented and a set of benchmark problem instances that is collected from the literature, are solved. Computational outcomes demonstrate the efficacy and effectiveness of the proposed algorithm as compared to other approaches from the literature. Finally, the proposed algorithm is implemented with the collected data from an industrial fabrication shop. The principal results indicate that the productivity of pipe spool fabrication shop, with particular constraints, is increased 85.27% by applying the proposed metaheuristic approach. University of Cincinnati 2017 Article PeerReviewed Moghadam, A. M. and Wong, K. Y. and Piroozfard, H. (2017) Solving a hybrid jobshop scheduling problem with space constraints and reentrant processesby using an improved hybrid genetic algorithm: a case study. International Journal of Industrial Engineering : Theory Applications and Practice, 24 (5). pp. 483-504. ISSN 1072-4761 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041473194&partnerID=40&md5=99033bee3207deea45264a3f4eb7a0bb
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	HD28 Management. Industrial Management
spellingShingle	HD28 Management. Industrial Management Moghadam, A. M. Wong, K. Y. Piroozfard, H. Solving a hybrid jobshop scheduling problem with space constraints and reentrant processesby using an improved hybrid genetic algorithm: a case study
description	In this paper, several concepts and techniques of a job shop manufacturing system are adjusted and utilized in a real-world pipe spool fabrication shop scheduling problem. In the present study, an improved hybrid genetic algorithm is developed to create a feasible and active schedule for the operational level of pipe spool fabrication shop with the aims of minimizing maximum completion time of all jobs. In the proposed algorithm, an enhanced solution encoding and decoding are used to represent a schedule for the fabrication shop. In order to generate high-quality initial solutions, an operation order-based global selection is designed, where this operator considers operations processing time and workload of machines while assigning machines to each of the proper operations. In the reproduction phase of this algorithm, the precedence preserving order-based crossover, uniform crossover, swapping operator, and intelligent mutation operator are used to produce the offspring and mutants for the sequencing and job routing sub-problems. In addition, a local search operator is utilized to explore the neighborhood of the solution in order to improve the solution quality further. Stepwise delineation of the proposed approach is presented and a set of benchmark problem instances that is collected from the literature, are solved. Computational outcomes demonstrate the efficacy and effectiveness of the proposed algorithm as compared to other approaches from the literature. Finally, the proposed algorithm is implemented with the collected data from an industrial fabrication shop. The principal results indicate that the productivity of pipe spool fabrication shop, with particular constraints, is increased 85.27% by applying the proposed metaheuristic approach.
format	Article
author	Moghadam, A. M. Wong, K. Y. Piroozfard, H.
author_facet	Moghadam, A. M. Wong, K. Y. Piroozfard, H.
author_sort	Moghadam, A. M.
title	Solving a hybrid jobshop scheduling problem with space constraints and reentrant processesby using an improved hybrid genetic algorithm: a case study
title_short	Solving a hybrid jobshop scheduling problem with space constraints and reentrant processesby using an improved hybrid genetic algorithm: a case study
title_full	Solving a hybrid jobshop scheduling problem with space constraints and reentrant processesby using an improved hybrid genetic algorithm: a case study
title_fullStr	Solving a hybrid jobshop scheduling problem with space constraints and reentrant processesby using an improved hybrid genetic algorithm: a case study
title_full_unstemmed	Solving a hybrid jobshop scheduling problem with space constraints and reentrant processesby using an improved hybrid genetic algorithm: a case study
title_sort	solving a hybrid jobshop scheduling problem with space constraints and reentrant processesby using an improved hybrid genetic algorithm: a case study
publisher	University of Cincinnati
publishDate	2017
url	http://eprints.utm.my/id/eprint/77148/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041473194&partnerID=40&md5=99033bee3207deea45264a3f4eb7a0bb
_version_	1643657511851524096
score	13.244367

Solving a hybrid jobshop scheduling problem with space constraints and reentrant processesby using an improved hybrid genetic algorithm: a case study

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