Dynamic Management Decision and Stochastic Control Processes
This book treats stochastic control theory and its applications in management. The main numerical techniques necessary for such applications are presented. Several advanced topics leading to optimal processes are dismissed. The book also considers the theory of some stochastic control processes and several applications to illustrate the ideas.
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DERIVATION OF LEVINSONS PROCEDURE
INVARIANT IMBEDDING AND
Comparison between the Two Methods
Inventory System Design
Areas for Futher Research
FUZZY CONTROL PROCESSES I
Optimal Policy for an Adaptive Control Process
OPTIMAL INVENTORY PROCESSES WITH
Conjunctive Surface and Ground Water
STOCHASTIC CONTROL PROCESSES AND
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adaptive control Adaptive Control Processes applications approximation assumptions bang-bang control Bellman capacity expansion chapter computational continuous functions control chart control limit control problem critical number decision problem defined demand denote determine deterministic discuss distribution dynamic programming exceeds the fixed expected cost finite fixed level fk(c functional equation fuzzy constraint fuzzy environment fuzzy goal fuzzy set ground water increasing initial Introduction inventory control inventory problem K-convex function Let us assume linear machine Management Science maximize membership function method minimize the probability monotone multi-stage nonfuzzy nonlinear observation obtained Odanaka open-loop controls optimal control Optimal Inventory Processes optimal policy performance level period prediction theory probability criterion probability density function problem of minimizing procedure pumping quantity random variables recurrence relation sequence stage exceed statistical quality control stochastic bang-bang control stochastic control processes stochastic control theory subinterval termed the grade theorem tion unique solution vector water price water supply