Solving an Industrial Shop Scheduling Problem Using Genetic Algorithm

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Abstract:

Spool fabrication shop is an intermediate phase in the piping process for construction projects. The delivery of pipe spools at the right time in order to be installed in the site is very important. Therefore, effective scheduling and controlling of the fabrication shop has a direct effect on the productivity and successfulness of the whole construction projects. In this paper, a genetic algorithm (GA) is developed to create an active schedule for the operational level of pipe spool fabrication. In the proposed algorithm, an enhanced solution coding is used to suitably represent a schedule for the fabrication shop. The initial population is generated randomly in the initialization stage and precedence preserving order-based crossover (POX) and uniform crossover are used appropriately. In addition, different mutation operators are used. The proposed algorithm is applied with the collected data that consist of operations processing time from an industrial fabrication shop. The results showed that by using GA for scheduling the fabrication processes, the productivity of the spool fabrication shop has increased by 88 percent.

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564-568

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December 2013

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] L. Song, P. Wang and S. AbouRizk, (2006). A virtual shop modeling system for industrial fabrication shops. Simulation Modelling Practice and Theory, 14 (5) 649-662.

DOI: 10.1016/j.simpat.2005.10.012

Google Scholar

[2] N. Sadeghi and A. R. Fayek, (2008). A framework for simulating industrial construction processes. Winter Simulation Conference, Miami, FL, United states. 2396-2401.

DOI: 10.1109/wsc.2008.4736347

Google Scholar

[3] M.L. Pinedo, Scheduling-Theory, Algorithms and Systems, third ed., Springer, NY, (2008).

Google Scholar

[4] V. Sels, F. Steen and M. Vanhoucke, (2011). Applying a hybrid job shop procedure to a Belgian manufacturing company producing industrial wheels and castors in rubber. Computers and Industrial Engineering, 61 (3) 697-708.

DOI: 10.1016/j.cie.2011.04.023

Google Scholar

[5] M. Gen, J. Gao and L. Lin, (2009). Multistage-Based Genetic Algorithm for Flexible Job-Shop Scheduling Problem. Intelligent and Evolutionary Systems, 187, pp.183-196.

DOI: 10.1007/978-3-540-95978-6_13

Google Scholar

[6] J. Gao, M. Gen and L. Sun, (2006). Scheduling jobs and maintenances in flexible job shop with a hybrid genetic algorithm. Journal of Intelligent Manufacturing, 17 (4) 493-507.

DOI: 10.1007/s10845-005-0021-x

Google Scholar

[7] S. Rahnamayan, H.R. Tizhoosh and M.M. A Salama, (2007). A novel population initialization method for accelerating evolutionary algorithms. Comp. & Math. with App., 53 (10) 1605-1614.

DOI: 10.1016/j.camwa.2006.07.013

Google Scholar

[8] I. Kacem, S. Hammadi and P. Borne, (2002).

Google Scholar

[9] F. Pezzella, G. Morganti and G. Ciaschetti, (2008). A genetic algorithm for the Flexible Job-shop Scheduling Problem. Computers and Operations Research, 35 (10) 3202-3212.

DOI: 10.1016/j.cor.2007.02.014

Google Scholar

[10] G. Zhang, L. Gao and Y. Shi, (2011). An effective genetic algorithm for the flexible job-shop scheduling problem. Expert Systems with Applications, 38 (4) 3563-3573.

DOI: 10.1016/j.eswa.2010.08.145

Google Scholar

[11] L. Kyung-Mi, T. Yamakawa and L. Keon-Myung, (1998). A genetic algorithm for general machine scheduling problems, Proceedings at the Second International Conference on Knowledge-Based Intelligent Electronic Systems, 62 60-66.

DOI: 10.1109/kes.1998.725893

Google Scholar

[12] S. Vaghefinezhad and K.Y. Wong, (2012). A genetic algorithm approach for solving a flexible job shop scheduling problem. International J. of Comp. Sci. Issues, 9 (3) 85-90.

Google Scholar

[13] H. Zhou, Y. Feng and L. Han, (2001). The hybrid heuristic genetic algorithm for job shop scheduling. Computers and Industrial Engineering, 40 (3) 191-200.

DOI: 10.1016/s0360-8352(01)00017-1

Google Scholar