Simulation of sequencing rules in a five-similar-machine job shop

Simulation of sequencing rules in a five-similar-machine job shop

Nowadays, simulation is essential when researching manufacturing process or designing production system. Line performance and equipment utilization are two major points of interest for every manufacturing company in order to increase competitiveness in the global market. The job shop scheduling is t...

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Main Author: Ang, Liqi
Format: Thesis
Language:English
Published: 2010
Subjects:
T Technology (General)
Online Access:http://eprints.utm.my/id/eprint/12686/6/AngLiqiMFKM2010.pdf
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id my-utm-ep.12686
record_format uketd_dc
institution Universiti Teknologi Malaysia
collection UTM Institutional Repository
language English
topic T Technology (General)
spellingShingle T Technology (General)
Ang, Liqi
Simulation of sequencing rules in a five-similar-machine job shop
description Nowadays, simulation is essential when researching manufacturing process or designing production system. Line performance and equipment utilization are two major points of interest for every manufacturing company in order to increase competitiveness in the global market. The job shop scheduling is the allocation of a number of machines to perform a set of jobs. Job shop scheduling problem exists in most of the manufacturing systems in various form. Due to its high mix and low volume manufacturing environment, priority selecting among the jobs is challenging. This project is a real case study which involving a job shop with five similar CNC milling machines. A total of six jobs were performed and each of them consists of a different set of operation. The sequence of the six jobs to enter the system was determined by the sequencing rules including shortest setup time (SST), shortest processing time (SPT), shortest processing time + setup time (SPST), lowest volume (LV), least process (LP) and earliest due date (EDD). The setup time was taken into consideration to make the results more realistic. Due to the complexity of the model, WITNESS was used to simulate all the sequencing rules and the results are obtained. The best rules approach for the company in this study can be determined by comparing the results for each rule. By doing this, the company will be able to make faster and better decision about which job should be processed first instead of pick it randomly among the jobs. The results indicate that no single rule is excellent in all criteria. SPST rule was recommended to the company as it performed the best in terms of total completion time.
format Thesis
qualification_level Master's degree
author Ang, Liqi
author_facet Ang, Liqi
author_sort Ang, Liqi
title Simulation of sequencing rules in a five-similar-machine job shop
title_short Simulation of sequencing rules in a five-similar-machine job shop
title_full Simulation of sequencing rules in a five-similar-machine job shop
title_fullStr Simulation of sequencing rules in a five-similar-machine job shop
title_full_unstemmed Simulation of sequencing rules in a five-similar-machine job shop
title_sort simulation of sequencing rules in a five-similar-machine job shop
granting_institution Universiti Teknologi Malaysia, Faculty of Mechanical Engineering
granting_department Faculty of Mechanical Engineering
publishDate 2010
url http://eprints.utm.my/id/eprint/12686/6/AngLiqiMFKM2010.pdf
_version_ 1747814946503131136
spelling my-utm-ep.126862017-09-18T06:13:22Z Simulation of sequencing rules in a five-similar-machine job shop 2010 Ang, Liqi T Technology (General) Nowadays, simulation is essential when researching manufacturing process or designing production system. Line performance and equipment utilization are two major points of interest for every manufacturing company in order to increase competitiveness in the global market. The job shop scheduling is the allocation of a number of machines to perform a set of jobs. Job shop scheduling problem exists in most of the manufacturing systems in various form. Due to its high mix and low volume manufacturing environment, priority selecting among the jobs is challenging. This project is a real case study which involving a job shop with five similar CNC milling machines. A total of six jobs were performed and each of them consists of a different set of operation. The sequence of the six jobs to enter the system was determined by the sequencing rules including shortest setup time (SST), shortest processing time (SPT), shortest processing time + setup time (SPST), lowest volume (LV), least process (LP) and earliest due date (EDD). The setup time was taken into consideration to make the results more realistic. Due to the complexity of the model, WITNESS was used to simulate all the sequencing rules and the results are obtained. The best rules approach for the company in this study can be determined by comparing the results for each rule. By doing this, the company will be able to make faster and better decision about which job should be processed first instead of pick it randomly among the jobs. The results indicate that no single rule is excellent in all criteria. SPST rule was recommended to the company as it performed the best in terms of total completion time. 2010 Thesis http://eprints.utm.my/id/eprint/12686/ http://eprints.utm.my/id/eprint/12686/6/AngLiqiMFKM2010.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Mechanical Engineering Faculty of Mechanical Engineering Alessandro Mascis , Dario Pacciarelli (2002). Job-shop scheduling with blocking and no-wait constraints. European Journal of Operational Research 143, 498-517. Attila Lengyela, Itsuo Hatonob, Kanji Uedac (2003) . Scheduling for on-time completion in job shops using feasibility function. Computers & Industrial Engineering 45, 215-229. Averill M. Law (2008). How to build a valid and credible simulation models Averill M. Law & Associates, Inc.6601 East Grant Road, Suite 110 Tucson, AZ 85715, U.S.A. Chandrasekharan Rajendran, Oliver Holthaus (1999) ,A comparative study of dispatching rules in dynamic flow shops and job shops. European Journal of Operational Research 116, 156-170. Daeyoung Chunga, Kichang Leeb, Kitae Shinc, Jinwoo Parkd (2005). A new approach to jobshop scheduling problems with due date constraints considering operation subcontracts Int. J. Production Economics 98, 238-250. H. Allaouia,_, S. Lamourib, A. Artibab, E. Aghezzaf (2008). Simultaneously scheduling n jobs and the preventive maintenance on the two-machine flow shop to minimize the makespan. Int. J. Production Economics 112, 161-167. Heizer, J. and Render, B. (2008). Operations Management. (9th Ed.). Upper Saddle River, New Jersey: Pearson Education. Joc Cing Tay , Nhu Binh Ho (2008). Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems. Computers & Industrial Engineering 54,453-473. Klaus Jansen , Monaldo Mastrolilli, Roberto Solis-Oba (2005). Approximation schemes for job shop scheduling problems with controllable processing times. 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Lagrangian bounds for just-in-time job-shop scheduling. Computers & Operations Research 35, 906 - 915. Shengxiang Yang ,DingweiWang ,Tianyou Chai ,Graham Kendall (2009). An improved constraint satisfaction adaptive neural network for job-shop scheduling. T.C.E. Cheng , B.M.T. Lin b (2009). Johnson’s rule, composite jobs and the relocation problem. European Journal of Operational Research 192, 1008-1013. V. Vinod, R. Sridharan (2008). Scheduling a dynamic job shop production system with sequence-dependent setups: An experimental study. Robotics and Computer- Integrated Manufacturing 24, 435-449. Wen-Chiung Lee, Yau-RenShiau , Shiuan-KangChen , Chin-ChiaWua (2010). A two-machine flow shop scheduling problem with deteriorating jobs and blocking Int. J. Production Economics 124, 188-197. Wiem Mouelhi-Chibani, Henri Pierreval (2010).Training a neural network to select dispatching rules in real time. Computers & Industrial Engineering 58, 249-256. Zhixin Liu (2010). Single machine scheduling to minimize maximum lateness subject to release dates and precedence constraints. Computers & Operations Research 37, 1537-1543.

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