Markovian approximation for manufacturing systems of unreliable machines in tandem
Markovian approximation for manufacturing systems of unreliable machines in tandem
This paper studies production planning of manufacturing systems of unreliable machines in tandem. The manufacturing system considered here produces one type of product. The demand is assumed to be a Poisson process and the processing time for one unit of product in each machine is exponentially distributed. A broken machine is subject to a sequence of repairing processes. The up time and the repairing time in each phase are assumed to be exponentially distributed. We study the manufacturing system by considering each machine as an individual system with stochastic supply and demand. The Markov Modulated Poisson Process (MMPP) is applied to model the process of supply. Numerical examples are given to demonstrate the accuracy of the proposed method. We employ (s, S) policy as production control. Fast algorithms are presented to solve the average running costs of the machine system for a given (s, S) policy and hence the approximated optimal (s, S) policy.
manufacturing systems, hedging point production (hpp) policies, (s, s) policies, markov modulated poisson process (mmpp), steady state probability distribution
65-78
Ching, Wai Ki
cfee9d26-97e3-42ce-a4e9-b91e6d8aeef7
2001
Ching, Wai Ki
cfee9d26-97e3-42ce-a4e9-b91e6d8aeef7
Abstract
This paper studies production planning of manufacturing systems of unreliable machines in tandem. The manufacturing system considered here produces one type of product. The demand is assumed to be a Poisson process and the processing time for one unit of product in each machine is exponentially distributed. A broken machine is subject to a sequence of repairing processes. The up time and the repairing time in each phase are assumed to be exponentially distributed. We study the manufacturing system by considering each machine as an individual system with stochastic supply and demand. The Markov Modulated Poisson Process (MMPP) is applied to model the process of supply. Numerical examples are given to demonstrate the accuracy of the proposed method. We employ (s, S) policy as production control. Fast algorithms are presented to solve the average running costs of the machine system for a given (s, S) policy and hence the approximated optimal (s, S) policy.
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Published date: 2001
Keywords:
manufacturing systems, hedging point production (hpp) policies, (s, s) policies, markov modulated poisson process (mmpp), steady state probability distribution
Organisations:
Operational Research
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Local EPrints ID: 29750
URI: http://eprints.soton.ac.uk/id/eprint/29750
PURE UUID: 53be0029-2c5b-425a-9eb3-b05c8e897dd8
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Date deposited: 12 May 2006
Last modified: 15 Mar 2024 07:34
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Author:
Wai Ki Ching
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