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Modeling of Hybrid Production Systems with Constant WIP and Unreliable Equipment

Modeling of Hybrid Production Systems with Constant WIP and Unreliable Equipment

Mehmet Savsar
Copyright: © 2013 |Pages: 22
ISBN13: 9781466624610|ISBN10: 1466624612|EISBN13: 9781466624627
DOI: 10.4018/978-1-4666-2461-0.ch013
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MLA

Savsar, Mehmet. "Modeling of Hybrid Production Systems with Constant WIP and Unreliable Equipment." Management Innovations for Intelligent Supply Chains, edited by John Wang, IGI Global, 2013, pp. 235-256. https://doi.org/10.4018/978-1-4666-2461-0.ch013

APA

Savsar, M. (2013). Modeling of Hybrid Production Systems with Constant WIP and Unreliable Equipment. In J. Wang (Ed.), Management Innovations for Intelligent Supply Chains (pp. 235-256). IGI Global. https://doi.org/10.4018/978-1-4666-2461-0.ch013

Chicago

Savsar, Mehmet. "Modeling of Hybrid Production Systems with Constant WIP and Unreliable Equipment." In Management Innovations for Intelligent Supply Chains, edited by John Wang, 235-256. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-2461-0.ch013

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Abstract

Material flow in production systems can be controlled by a purely push-pull (just-in-time), or by a hybrid push-pull control mechanism. One type of push-pull production control can be implemented by controlling only the last stage during part withdrawals to trigger the production at the first stage. While the final stage is operated according to a pull mechanism, intermediate stages are operated according to a push system of control in order to keep the work-in-process (WIP) at a constant level. Since the WIP levels are limited in hybrid systems, production output rate is very susceptible to equipment failures. In order to establish suitable WIP capacities between the stages of production, it is essential to analyze the production line using appropriate models and tools. This paper develops a discrete iterative model to study and analyze behavior of a push-pull system with unreliable equipment at any stage. The model is utilized to optimize WIP capacities at intermediate stages and number of kanbans at the last stage. Furthermore, an experimental design is set up to analyze effects of various maintenance policies, line design parameters, and operational factors on line performance measures by using simulation results from the model.

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