Business analytics for decision making by Steven Orla Kimbrough, Hoong Chuin Lau PDF

By Steven Orla Kimbrough, Hoong Chuin Lau

Business Analytics for determination Making, the 1st entire textual content compatible to be used in introductory company Analytics classes, establishes a countrywide syllabus for an rising first direction at an MBA or top undergraduate point. This well timed textual content is principally approximately version analytics, quite analytics for restricted optimization. It makes use of implementations that permit scholars to discover versions and knowledge for the sake of discovery, figuring out, and selection making.

Business analytics is ready utilizing facts and versions to resolve different types of selection difficulties. There are 3 features in case you intend to make the main in their analytics: encoding, resolution layout, and post-solution research. This textbook addresses all 3. Emphasizing using limited optimization versions for choice making, the publication concentrates on post-solution research of versions.

The textual content makes a speciality of computationally not easy difficulties that more often than not come up in enterprise environments. specific between company analytics texts, it emphasizes utilizing heuristics for fixing tough optimization difficulties vital in enterprise perform via making top use of tools from computing device technology and Operations examine. moreover, case stories and examples illustrate the real-world functions of those tools.

The authors provide examples in Excel®, GAMS, MATLAB®, and OPL. The metaheuristics code can be made to be had on the book's web site in a documented library of Python modules, besides info and fabric for homework workouts. From the start, the authors emphasize analytics and de-emphasize illustration and encoding so scholars could have lots to sink their the teeth into despite their laptop programming experience.

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Extra resources for Business analytics for decision making

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5 (5) Nonlinear Integer Program (NLIP) . . . . . . . . . . . . . . . . 6 (6) Mixed Integer Nonlinear Program (MINLP) . . . . . . . . . . . Solution Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Computational Complexity and Solution Methods . . . . . . . . . . . . . . Metaheuristics . . . . . . . . . . . . . . . . . . . . . . . . .

The xi s, i = 1, 2, . . , n are called the decision variables for the model and they are what the decision maker gets to decide upon. The other mathematical entities in the model—the ci s, the wi s, and b—are parameters; that is, in terms of the model they are assumed to be given and fixed. A decision for a model is constituted by any assignment of (here, 0 or 1) values to each of the n decision variables. A feasible decision is one that satisfies (makes true) the constraints of the problem. Thus, decisions may or may not be feasible.

Solution Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Computational Complexity and Solution Methods . . . . . . . . . . . . . . Metaheuristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Greedy Hill Climbing . . . . . . . . . . . . . . . . . . . . . . . . . 2 Local Search Metaheuristics: Simulated Annealing . . . . . .

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