We present a scenario decomposition algorithm for stochastic 0-1 programs. The algorithm recovers an optimal solution by iteratively exploring and cutting-off candidate solutions obtained from solving scenario subproblems. The scheme is applicable to quite general problem structures and can be implemented in a distributed framework. We provide a theoretical justification of the effectiveness of the proposed scheme. Illustrative computational results demonstrating near linear parallel speedup on standard test instances are presented.
Biographie : Shabbir Ahmed is the Dean’s Professor and Stewart Faculty Fellow in the H. Milton Stewart School of Industrial & Systems Engineering at the Georgia Institute of Technology. He received his PhD from the University of Illinois at Urbana-Champaign. His research interests are in optimization, specifically stochastic and integer programming. Dr. Ahmed served as the Chair of the Stochastic Programming Society, as a Vice-chair of the INFORMS Optimization Society, and is on the board of directors of the INFORMS Computing Society. He is on the editorial boards of various journals included Mathematical Programming A, Mathematical Programming C, and Operations Research. Dr. Ahmed's honors include the National Science Foundation CAREER award, two IBM Faculty Awards, the Coca-Cola Junior Professorship from ISyE, and the INFORMS Dantzig Dissertation award.
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