Hostname: page-component-76fb5796d-dfsvx Total loading time: 0 Render date: 2024-04-26T00:33:23.940Z Has data issue: false hasContentIssue false

Optimal Production Control in Stochastic Manufacturing Systems with Degenerate Demand

Published online by Cambridge University Press:  28 May 2015

Azizul Baten*
Affiliation:
Mathematics Program, School of Distance Education, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia
Anton Abdulbasah Kamil*
Affiliation:
Mathematics Program, School of Distance Education, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia
*
Corresponding author. Email: baten_math@yahoo.com
Corresponding author. Email: anton@usm.my
Get access

Abstract

The paper studies the production inventory problem of minimizing the expected discounted present value of production cost control in manufacturing systems with degenerate stochastic demand. We have developed the optimal inventory production control problem by deriving the dynamics of the inventory-demand ratio that evolves according to a stochastic neoclassical differential equation through Ito's Lemma. We have also established the Riccati based solution of the reduced (one- dimensional) HJB equation corresponding to production inventory control problem through the technique of dynamic programming principle. Finally, the optimal control is shown to exist from the optimality conditions in the HJB equation.

Type
Research Article
Copyright
Copyright © Global-Science Press 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

[1]Baten, M.A. and Morimoto, H., 2005. An extension of the linear regulator for degenerate diffusions, IEEE Transactions on Automatic Control, 50 (11), 18221826.Google Scholar
[2]Baten, M.A. and Sobhan, A., 2007. Optimal consumption in a growth model with the CES production function, Stochastic Analysis and Applications, 25, 10251042.Google Scholar
[3]Bellman, R., 1957. Dynamic Programming, Princeton N.J.: Princeton University Press.Google Scholar
[4]Bensoussan, A., Sethi, S.P., Vickson, R., and Derzko, N.. 1984. Stochastic production planning with production constraints, SIAM J. Control and Optim., 22, 920935.Google Scholar
[5]Fleming, W.H., Sethi, S.P., and Soner, H.M.. 1987. An optimal stochastic production planning with randomly fluctuating demand, SIAM J. Control and Optim., 25, 14941502.Google Scholar
[6]Ikeda, N. and Watanabe, S.. 1981. Stochastic Differential Equations and Diffusion Processes, North-Holland, Amsterdam.Google Scholar
[7]Karatzas, I. and Shreve, S.E., 1991. Brownian Motion and Stochastic Calculus, Springer-Verlag, New York.Google Scholar
[8]Morimoto, H. and Kawaguchi, K., 2002. Optimal Exploitation of Renewable Resources by the Viscosity solution method, Stoc. Ana. and App., vol. 20, 927946.Google Scholar
[9]Morimoto, H. and Okada, M.. 1999. Some results on Bellman equation of ergodic control, SIAM J. Control and Optim., 38(1), 159174.Google Scholar
[10]Prato, G.Da., 1984. Direct Solution of a Riccati Equation Arising in Stochastic Control Theory, Appl. Math. Optim, vol.11, 191208.Google Scholar
[11]Prato, G.Da. and Ichikawa, A.. 1990. Quadratic control for linear time-varying systems, SIAM J. Control and Optim., 28, 359381.Google Scholar
[12]Sethi, S.P., Suo, W., Taksar, M.I., and Zhang, Q.. 1997. Optimal production planning in a stochastic manufacturing systems with long-run average cost, J. Optim. Theory and Appl., 92, 161188.Google Scholar
[13]Sethi, S.P. and Zhang, Q.. 1994. Hierarchical decision making in a stochastic manufacturing systems, Birkhäuser, Boston.Google Scholar