Matrix methods in production planning of failure prone manufacturing systems
Matrix methods in production planning of failure prone manufacturing systems
This paper studies optimal hedging policy for a failure prone one-machine system. The machine produces one type of product and its demand has batch arrival. The inter-arrival time of the demand, up time of the machine and processing time for one unit of product are exponentially distributed. When the machine is down, it is subject to a sequence of l repairing phases. In each phase, the repair time is exponentially distributed. The machine states and the inventory levels are modeled as Markov processes and an efficient algorithm is presented to solve the steady state probability distribution. The average running cost for the system can be written in terms of the steady state distribution. The optimal hedging point can then be obtained by varying different values of hedging point.
manufacturing system, hedging point policy, steady state distribution, block gauss-seidel method
9783540760559
1-29
Ching, Wai Ki
cfee9d26-97e3-42ce-a4e9-b91e6d8aeef7
Zhou, Xun Yu
67677e8b-0641-4ba0-af26-508701d8fd33
1996
Ching, Wai Ki
cfee9d26-97e3-42ce-a4e9-b91e6d8aeef7
Zhou, Xun Yu
67677e8b-0641-4ba0-af26-508701d8fd33
Ching, Wai Ki and Zhou, Xun Yu
(1996)
Matrix methods in production planning of failure prone manufacturing systems.
In,
Recent Advances in Control and Optimization of Manufacturing Systems. Part 1: Optimal Production Planning.
(Lecture Notes in Control and Information Sciences, 214)
Springer, .
(doi:10.1007/BFb0015112).
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Abstract
This paper studies optimal hedging policy for a failure prone one-machine system. The machine produces one type of product and its demand has batch arrival. The inter-arrival time of the demand, up time of the machine and processing time for one unit of product are exponentially distributed. When the machine is down, it is subject to a sequence of l repairing phases. In each phase, the repair time is exponentially distributed. The machine states and the inventory levels are modeled as Markov processes and an efficient algorithm is presented to solve the steady state probability distribution. The average running cost for the system can be written in terms of the steady state distribution. The optimal hedging point can then be obtained by varying different values of hedging point.
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Published date: 1996
Additional Information:
ISSN 0170-8643
Keywords:
manufacturing system, hedging point policy, steady state distribution, block gauss-seidel method
Organisations:
Operational Research
Identifiers
Local EPrints ID: 29736
URI: http://eprints.soton.ac.uk/id/eprint/29736
ISBN: 9783540760559
PURE UUID: 40af3050-d4d7-4582-bfb5-6708863fb477
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Date deposited: 01 May 2007
Last modified: 15 Mar 2024 07:34
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Contributors
Author:
Wai Ki Ching
Author:
Xun Yu Zhou
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