Abstract
Machine Sequencing Flexibility (MSF), a measure of alternate machine sequences in which the operations of a job can be performed, is very important especially in the context of today’s competitive manufacturing environment where responsiveness to the market determines the survival of the industries. In a scheduling scenario where N numbers of jobs are to be scheduled on M number of machines and each job has to visit each machine, the problem becomes an Open Shop problem if MSF is 100%.
In this research, a scheduling methodology, based on GA, is proposed which integrates planning (machine sequencing) with shop floor scheduling (job sequencing) assuming 100% MSF. The methodology works for both Traditional Manufacturing (TM) and FMS. It takes the machine sequences for N number of jobs as chromosomes (process plan). The chromosomes of the given number of population size are evaluated for shop floor scheduling using a heuristic to sequence the jobs for the machine loading. A solution space of the chromosomes is searched through the use of GA for the best/optimal chromosome (machines sequence) for minimization of the Makespan. Various benchmark problems have been solved through the proposed scheduling methodology in TM and FMS environments and the results have been compared with other research results. These comparisons indicate that the presented GA-based scheduling methodology has performed extremely well.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Rachamadugu R., Nandkeolyar U., 1993, Scheduling with Sequencing Flexibility, Decision Sciences, Volume 24, Number 2, P315–341.
Chan F.T.S, Chan H. K. and Lau H. C. W., 2002, The state of the Art in Simulation Study on FMS Scheduling: A comprehensive Survey, the International Journal of Advanced Manufacturing Technology, 19, P830–849.
Gonzalez M.J. and Sahni S., 1976, Open Shop Scheduling to minimize finish time, Journal of the Association for Computing Machinery, 23, P655–679.
Bitran G.R., Dada M., Sisan L.O., 1982, Simulation Model for Job Shop Scheduling (working paper). A.P. Sloen School of Management, Massachusetts Institute of Technology.
Noor S, Khan M.K., Hussain I, Ullah Irfan, 2006, Scheduling Tool for the Flexible Manufacturing Systems using Hybrid Genetic Algorithm, 2nd International Conference on Emerging Technologies (IEEE), Pakistan 13–14 November.
Jain A. S. and Meeran S., 1999, Deterministic Job-Shop Scheduling: Past, Present and Future, European Journal of Operational Research 113, P390–434.
Geyik Faruk., Cedimoglu Ismail Hakki, 2004, A Review of the Production Scheduling Approaches Based on Artificial Intelligence and the Integration of Process Planning and Scheduling “,http://www1.gantep.edu.tr/~fgeyik/SwissCadCam99.pdf, August.
Kumar M., Rajotia S., 2003, Integration of Scheduling with Computer Aided Process Planning”, Journal of Materials Processing Technology, 138, P297–300.
Chaperfield A., Flemming P., Pohlhein H., Fonseca C., 2001, Genetic Algorithm MATLAB Tool Box-User’s Guide” Version 1.2, Department of Automatic Control and Systems Engineering, University of Sheffield.
Morshed M. Sarwar, 2006, A Hybrid Model for Job Shop Scheduling, PhD Thesis, University of Birmingham, UK.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag London Limited
About this paper
Cite this paper
Noor, S., Khan, M.K., Hussain, I. (2007). Investigation of Machines Sequencing Flexibility in Traditional and Flexible Manufacturing Systems (FMS) Using Genetic Algorithms (GA). In: Hinduja, S., Fan, KC. (eds) Proceedings of the 35th International MATADOR Conference. Springer, London. https://doi.org/10.1007/978-1-84628-988-0_40
Download citation
DOI: https://doi.org/10.1007/978-1-84628-988-0_40
Publisher Name: Springer, London
Print ISBN: 978-1-84628-987-3
Online ISBN: 978-1-84628-988-0
eBook Packages: EngineeringEngineering (R0)