Abstract
Manufacturing systems have to be able to cope with variability, such as job variety, machine failures and repair, operation time variability and the variability between workers. The chapter shows how models can be used to support the design of systems that cope with variability. In job shops variability leads to high inventories. Only with highly variable operation times does random routing of jobs improve performance, otherwise uniform routing is preferable. Scheduling jobs based on their operation times improves performance. While flow lines can offer improved productivity, quality problems occur in moving belt systems, and with limited storage space for in process jobs productivity is reduced. FMS were developed to overcome job shop and flow line problems. Models are used to show why FMS have not met their initial promise. Central storage of in process jobs uses limited storage space better. Cells and teams should achieve substantial productivity improvement, but only if there are incentives for faster workers do more work.
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Acknowledgements
I would like to acknowledge the contribution to my understanding of manufacturing systems by Bill Brady and Leo Hanifin and by my former PhD students John Callahan, George Shanthikumar, David Yao, Ken McKay, Beth Jewkes, Diwakar Gupta, Seyed Iravani, Xiao-Gao Liu.
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Buzacott, J.A. (2013). The Design of Manufacturing Systems to Cope with Variability. In: Smith, J., Tan, B. (eds) Handbook of Stochastic Models and Analysis of Manufacturing System Operations. International Series in Operations Research & Management Science, vol 192. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6777-9_1
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DOI: https://doi.org/10.1007/978-1-4614-6777-9_1
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