TY - GEN
T1 - Production control in a network-failure prone manufacturing system with stochastic demand using improved response surface methodology
AU - Sajadi, Seyed Mojtaba
AU - Seyedesfahani, Mir Mehdi
AU - Sörensen, Kenneth
PY - 2010
Y1 - 2010
N2 - In this paper we consider the production control of a failure prone manufacturing network using the Hedging Point Policy (HPP). This system consist of a network of machines with relationship constraints that can be in one of four states: operational, in repair, starved and blocked. Broken machines are subject to a repair process, and up time and repair time in each phase for each machines is assumed to be exponentially distributed. The demand for the product produced by the final machine is assumed to be a Poisson process. Unmet demand is either backlogged or lost. The objective of this paper is to find the optimal production rates of each machine so as to minimize the long run average inventory and backlog cost. In order to solve this problem we use a simulation based optimization method that combines stochastic optimal control theory, discrete event simulation, experimental design and Automated Response Surface Methodology (RSM). We include a numerical example to illustrate the effectiveness of the proposed methodology.
AB - In this paper we consider the production control of a failure prone manufacturing network using the Hedging Point Policy (HPP). This system consist of a network of machines with relationship constraints that can be in one of four states: operational, in repair, starved and blocked. Broken machines are subject to a repair process, and up time and repair time in each phase for each machines is assumed to be exponentially distributed. The demand for the product produced by the final machine is assumed to be a Poisson process. Unmet demand is either backlogged or lost. The objective of this paper is to find the optimal production rates of each machine so as to minimize the long run average inventory and backlog cost. In order to solve this problem we use a simulation based optimization method that combines stochastic optimal control theory, discrete event simulation, experimental design and Automated Response Surface Methodology (RSM). We include a numerical example to illustrate the effectiveness of the proposed methodology.
KW - Automated response surface methodology
KW - Experimental design
KW - Failure prone manufacturing network
KW - Simulation based optimization
UR - https://ieeexplore.ieee.org/document/5668204
UR - http://www.scopus.com/inward/record.url?scp=78651428413&partnerID=8YFLogxK
U2 - 10.1109/ICCIE.2010.5668204
DO - 10.1109/ICCIE.2010.5668204
M3 - Conference publication
AN - SCOPUS:78651428413
SN - 9781424472956
T3 - 40th International Conference on Computers and Industrial Engineering: Soft Computing Techniques for Advanced Manufacturing and Service Systems, CIE40 2010
BT - 40th International Conference on Computers and Industrial Engineering
T2 - 40th International Conference on Computers and Industrial Engineering, CIE40 2010
Y2 - 25 July 2010 through 28 July 2010
ER -