000			a
999			_c30986 _d30986
008			220528b xxu||||| |||| 00| 0 eng d
020			_a9781138712164
082			_a658.403 _bMIR
100			_aMiranda, Joao Luis de
245			_aIntroduction to optimization-based decision making
260			_bCRC Press, _c2022 _aBoca Raton :
300			_axxi, 241 p. ; _bill., _c24 cm
365			_b74.99 _cGBP _d102.80
490			_aSeries in operations research
504			_aIncludes bibliographical references and index.
520			_aThe large and complex challenges the world is facing, the growing prevalence of huge data sets, and new and developing ways for addressing them (artificial intelligence, data science, machine learning etc.), means that it is increasingly vital that academics and professionals from across disciplines have a basic understanding of the mathematical underpinnings of effective, optimized decision making. Without it, decision makers risk being overtaken by those who better understand the models and methods, which can best inform strategic and tactical decisions. "Introduction to Optimization-Based Decision Making" provides an elementary and self-contained introduction to the basic concepts involved in making decisions in an optimization-based environment. The mathematical level of the text is directed to the post-secondary reader, or university students in the initial years. The pre-requisites are therefore minimal, and necessary mathematical tools are provided as needed. This lean approach is complemented with a problem-based orientation and a methodology of generalization/reduction. In this way, the book can be useful for students from STEM fields, economics and enterprise sciences, social sciences and humanities, as well as for the general reader interested in multi/trans-disciplinary approaches. Features Collects and discusses the ideas underpinning decision making through optimization tools in a simple and straightforward manner. Suitable for an undergraduate course in optimization-based decision making, or as a supplementary resource for courses in operations research and management science. Self-contained coverage of traditional and more modern optimization models, while not requiring a previous background in decision theory.
650			_aOperations research
650			_aMathematical optimization
650			_aMathematical models
650			_aBusiness and Economics
650			_aNumber Systems
942			_2ddc _cBK