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Butz, Florian (2014) Dynamic Process Planning: A Hybrid Modeling Approach for Real Time Production Scheduling. [Dissertation (University of Nottingham only)] (Unpublished)

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Abstract

Background Developments in the manufacturing environment such as shifting markets, emerging technologies and increasing competition require fundamental changes. One aspect is the integration of the activities process planning and process scheduling with the objective to balance resource loading, avoid bottlenecks and lower resource utilization.

Method One approach to integrate process planning and scheduling is computer simulation. A manufacturing process of Supreme Precision Manufacturing Co., Ltd. is analysed by the use of a hybrid simulation model. Agent-based modeling and discrete event simulation are combined to investigate the impact of alternating set ups on the process. Different levels of flexibility, different decision criteria and different queuing principles are introduced into the model with the use of the software AnyLogic.

Results The quantitative results show that a dynamic process is able to produce more output with shorter processing times per unit than a static process in the same period. Additionally, the machines in a dynamic process are more utilized and the waiting times per unit are shorter. Furthermore, it is more effective to base a decision for or against an alternative machine on the current queue length, rather than the average queue length or the machine utilization, as these processes provide better results in terms of output, processing time per unit, machine utilization and waiting time per unit. Last but not least, as soon as some flexibility is intro-duced into the process, a process applying LIFO queuing principles is more efficient than one applying FIFO.

Conclusion The results confirm with theoretical goals and objectives of an efficient schedul-ing activity such as minimizing average flow time or maximizing machine utilization as pointed out by Tan and Khoshnevis (2000). However, the results are very case sensitive, which means it is difficult to make general assumptions or conclusions for other manufactur-ing environments or processes based on these results.

Item Type:	Dissertation (University of Nottingham only)
Depositing User:	EP, Services
Date Deposited:	11 Nov 2014 15:06
Last Modified:	19 Oct 2017 14:01
URI:	https://eprints.nottingham.ac.uk/id/eprint/27298

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