Guaranteed cost control for multi-inventory systems with uncertain demand

In this paper we consider the problem of controlling a multi-inventory system in the presence of uncertain demand. The demand is unknown but bounded in an assigned compact set. The control input is assumed to be also constrained in a compact set. We consider an integral cost function of the buffer levels and we face the problem of minimizing the worst-case cost. We show that the optimal cost of a suitable auxiliary problem with no uncertainties is always an upper bound for the original problem. In the special case of minimum-time control, this upper bound is tight, namely its optimal cost is equal to the worst-case cost for the original system. Furthermore, the result is constructive, since the optimal control law can be explicitly computed.

[1]  Franco Blanchini,et al.  Control of production-distribution systems with unknown inputs and system failures , 1998, Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171).

[2]  Dimitri P. Bertsekas,et al.  Dynamic Programming and Optimal Control, Two Volume Set , 1995 .

[3]  Franco Blanchini,et al.  A new class of universal Lyapunov functions for the control of uncertain linear systems , 1999, IEEE Trans. Autom. Control..

[4]  E.K. Boukas,et al.  Minimax production planning in failure prone manufacturing systems , 1995, Proceedings of 1995 34th IEEE Conference on Decision and Control.

[5]  Francesco Martinelli,et al.  On the optimality of myopic production controls for single-server, continuous-flow manufacturing systems , 2001, IEEE Trans. Autom. Control..

[6]  R.E. Larson,et al.  Applications of dynamic programming to the control of water resource systems , 1969, Autom..

[7]  Qing Zhang,et al.  Minimax production planning in failure-prone manufacturing systems , 1995 .

[8]  A. Segall,et al.  An optimal control approach to dynamic routing in networks , 1982 .

[9]  Chang Shu,et al.  Optimal myopic production controls for manufacturing systems , 2000, Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334).

[10]  Konstantin Kogan,et al.  Scheduling: Control-Based Theory and Polynomial-Time Algorithms , 2000 .

[11]  Franco Blanchini,et al.  Least inventory control of multi-storage systems with non-stochastic unknown inputs , 1997, Proceedings of the 1997 American Control Conference (Cat. No.97CH36041).

[12]  Anthony Ephremides,et al.  Control and optimization methods in communication network problems , 1989 .

[13]  M. Papageorgiou,et al.  A linear programming approach to large-scale linear optimal control problems , 1995, Proceedings of 1995 34th IEEE Conference on Decision and Control.

[14]  D. Bertsekas,et al.  On the minimax reachability of target sets and target tubes , 1971 .

[15]  Stanley B. Gershwin,et al.  An algorithm for the computer control of a flexible manufacturing system , 1983 .

[16]  Y. Narahari,et al.  Lead time modeling and acceleration of product design and development , 1999, IEEE Trans. Robotics Autom..

[17]  A. Barrett Network Flows and Monotropic Optimization. , 1984 .

[18]  Edward J. Davison,et al.  Decentralized Robust Control for Dynamic Routing of Large Scale Networks , 1990, 1990 American Control Conference.