000			01900nam a22002417a 4500
005			20250328081412.0
008			250313b |||||||| |||| 00| 0 eng d
020			_a9783031545498
040			_aMAIN
082			_a658.4034 _bKIN-24
100			_aKing, Alan J.
245			_aModeling with Stochastic Programming / _cAlan J. King
250			_a2nd ed.
260			_aSwitzerland : _bSpringer, _c2024
300			_axviii,202p.
440			_aSpringer Series in operations research and Financial Engineering
500			_aSummary:This is an updated version of what is still the only text to address basic questions about how to model uncertainty in mathematical programming, including how to reformulate a deterministic model so that it can be analyzed in a stochastic setting. This second edition has important extensions regarding how to represent random phenomena in the models (also called scenario generation) as well as a new chapter on multi-stage models. This text would be suitable as a stand-alone or supplement for a second course in OR/MS or in optimization-oriented engineering disciplines where the instructor wants to explain where models come from and what the fundamental modeling issues are. The book is easy-to-read, highly illustrated with lots of examples and discussions. It will be suitable for graduate students and researchers working in operations research, mathematics, engineering and related departments where there is interest in learning how to model uncertainty. Alan King is a Research Staff Member at IBM's Thomas J. Watson Research Center in New York. Stein W. Wallace is a Professor of Operational Research and head of Center for Shipping and Logistics at NHH Norwegian School of Economics, Bergen, Norway
650			_aOperation Research
650			_aStochastic processes
650			_aStochastic Programming
700			_aWallace, Stein W.
942			_cBK
999			_c22168 _d22168