000			03457 a2200229 4500
999			_c491 _d491
005			20191221115949.0
008			191221b ||||| |||| 00| 0 eng d
020			_a9781482221763
082			_a658.4032 _bKIM
100			_aKimbrough, Steve _91547
245			_aBusiness analytics for decision making
260			_bCRC Press _aBoca Raton _c2016
300			_axxii, 307 p.
365			_aGBP _b74.99
504			_aTable of Contents I: STARTERS Introduction The Computational Problem Solving Cycle Example: Simple Knapsack Models An Example: The Eilon Simple Knapsack Model Scoping Out Post-Solution Analysis Parameter Sweeping: A Method for Post-Solution Analysis Decision Sweeping Summary of Vocabulary and Main Points For Exploration For More Information Constrained Optimization Models: Introduction and Concepts Constrained Optimization Classification of Models Solution Concepts Computational Complexity and Solution Methods Metaheuristics Discussion For Exploration For More Information Linear Programming Introduction Wagner Diet Problem Solving an LP Post-Solution Analysis of LPs More than One at a Time: The 100% Rule For Exploration For More Information II: OPTIMIZATION MODELING Simple Knapsack Problems Introduction Solving a Simple Knapsack in Excel The Bang-for-Buck Heuristic Post-Solution Analytics with the Simple Knapsack Creating Simple Knapsack Test Models
520			_aDescription Business Analytics for Decision Making, the first complete text suitable for use in introductory Business Analytics courses, establishes a national syllabus for an emerging first course at an MBA or upper undergraduate level. This timely text is mainly about model analytics, particularly analytics for constrained optimization. It uses implementations that allow students to explore models and data for the sake of discovery, understanding, and decision making. Business analytics is about using data and models to solve various kinds of decision problems. There are three aspects for those who want to make the most of their analytics: encoding, solution design, and post-solution analysis. This textbook addresses all three. Emphasizing the use of constrained optimization models for decision making, the book concentrates on post-solution analysis of models. The text focuses on computationally challenging problems that commonly arise in business environments. Unique among business analytics texts, it emphasizes using heuristics for solving difficult optimization problems important in business practice by making best use of methods from Computer Science and Operations Research. Furthermore, case studies and examples illustrate the real-world applications of these methods. The authors supply examples in Excel®, GAMS, MATLAB®, and OPL. The metaheuristics code is also made available at the book's website in a documented library of Python modules, along with data and material for homework exercises. From the beginning, the authors emphasize analytics and de-emphasize representation and encoding so students will have plenty to sink their teeth into regardless of their computer programming experience.
650			_aManagement--Statistical methods _91037
650			_aDecision making--Data processing _91548
650			_aDecision making--Statistical methods _91549
700			_aLau, Hoong Chuin _91550
942			_2ddc _cBK