Random-Direction Optimization Algorithms with Applications to Threshold Controls

G. Yin, Q. Zhang, H. M. Yan and E. K. Boukas
Additional contact information
G. Yin: Wayne State University
Q. Zhang: University of Georgia
H. M. Yan: Chinese University of Hong Kong
E. K. Boukas: École Polytechnique de Montréal

Journal of Optimization Theory and Applications, 2001, vol. 110, issue 1, No 11, 233 pages

Abstract: Abstract This work develops a class of stochastic optimization algorithms. It aims to provide numerical procedures for solving threshold-type optimal control problems. The main motivation stems from applications involving optimal or suboptimal hedging policies, for example, production planning of manufacturing systems including random demand and stochastic machine capacity. The proposed algorithm is a constrained stochastic approximation procedure that uses random-direction finite-difference gradient estimates. Under fairly general conditions, the convergence of the algorithm is established and the rate of convergence is also derived. A numerical example is reported to demonstrate the performance of the algorithm.

Keywords: gradient estimates; random directions; stochastic approximations; manufacturing (search for similar items in EconPapers)
Date: 2001
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DOI: 10.1023/A:1017555931930

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