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Details of Grant 

EPSRC Reference: GR/J90855/01
Title: LARGE SCALE STOCHASTIC PROGRAMMING ALGORITHMS
Principal Investigator: Dempster, Professor MAH
Other Investigators:
Researcher Co-Investigators:
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Pre Nexus Migration
Department: Mathematical Sciences
Organisation: University of Essex
Scheme: Standard Research (Pre-FEC)
Starts: 01 May 1994 Ends: 14 March 1996 Value (£): 45,019
EPSRC Research Topic Classifications:
Mathematical Aspects of OR
EPSRC Industrial Sector Classifications:
Information Technologies
Related Grants:
Panel History:  
Summary on Grant Application Form
The research will develop, code and test algorithms for solving large scale linearly constrained dynamic stochastic programming (DSP) problems which typically arise from strategic, tactical and operational planning problems in a variety of real world applications. In such applications the number of possible alternative problem data paths (scenarios) can become astronomical, requiring that only a representative sample be used in the optimisation process. A new DSP algorithm for the linear case will iterate importance sampling of data paths, solution of the resulting large scale deterministic equivalent linear programme using nested Benders' decomposition (MSLiP) and reduction of the problem using expected value of perfect information techniques (COMPACTER), in order to converge to an unbiased minimum variance estimate of the problem expected value based on a fixed sample size. The algorithm will be tested on an existing extensive set of representative real world test problems and the experience gained used to extend it to the practically important case of (differentiable) nonlinear (convex) objective functions in which at each iteration of the decomposition procedure first order approximations are substituted for true objective values. Tools will be developed in C++ to implement the procedures developed and for DSP model specification and management which augment previously developed software. Parallelisation of the developed algorithms will be pursued concurrently under separate funding.
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Organisation Website: http://www.sx.ac.uk