A simulation optimization based control policy for failure prone one-machine, two-product manufacturing systems

2004 ◽  
Vol 46 (2) ◽  
pp. 285-292 ◽  
Author(s):  
J.P. Kenne ◽  
A. Gharbi
Author(s):  
Xi Gu ◽  
Xiaoning Jin ◽  
Jun Ni

Real-time maintenance decision making in large manufacturing system is complex because it requires the integration of different information, including the degradation states of machines, as well as inventories in the intermediate buffers. In this paper, by using a discrete time Markov chain (DTMC) model, we consider the real-time maintenance policies in manufacturing systems consisting of multiple machines and intermediate buffers. The optimal policy is investigated by using a Markov Decision Process (MDP) approach. This policy is compared with a baseline policy, where the maintenance decision on one machine only depends on its degradation state. The result shows how the structures of the policies are affected by the buffer capacities and real-time buffer levels.


Author(s):  
Raed Kontar ◽  
Shiyu Zhou ◽  
John Horst

This paper explores the potential of Gaussian process based Metamodels for simulation optimization with multivariate outputs. Specifically we focus on Multivariate Gaussian process models established through separable and non-separable covariance structures. We discuss the advantages and drawbacks of each approach and their potential applicability in manufacturing systems. The advantageous features of the Multivariate Gaussian process models are then demonstrated in a case study for the optimization of manufacturing performance metrics.


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