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E-raamat: Business Analytics for Decision Making

(The Wharton School, University of Pennsylvania, Philadelphia, USA), (School of Information Systems, Singapore Management University, Singapore)
  • Formaat: 330 pages
  • Ilmumisaeg: 03-Sep-2018
  • Kirjastus: Chapman & Hall/CRC
  • Keel: eng
  • ISBN-13: 9781315360249
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Business Analytics for Decision MakingSuurem pilt
  • Formaat: 330 pages
  • Ilmumisaeg: 03-Sep-2018
  • Kirjastus: Chapman & Hall/CRC
  • Keel: eng
  • ISBN-13: 9781315360249
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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.
Preface xi
List of Figures xvii
List of Tables xxi
I Starters 1(58)
1 Introduction
3(22)
1.1 The Computational Problem Solving Cycle
3(3)
1.2 Example: Simple Knapsack Models
6(3)
1.3 An Example: The Eilon Simple Knapsack Model
9(2)
1.4 Scoping Out Post-Solution Analysis
11(7)
1.4.1 Sensitivity
11(2)
1.4.2 Policy
13(1)
1.4.3 Outcome Reach
14(1)
1.4.4 Opportunity
14(1)
1.4.5 Robustness
15(1)
1.4.6 Explanation
15(1)
1.4.7 Resilience
16(2)
1.5 Parameter Sweeping: A Method for Post-Solution Analysis
18(1)
1.6 Decision Sweeping
19(1)
1.7 Summary of Vocabulary and Main Points
20(1)
1.8 For Exploration
21(2)
1.9 For More Information
23(2)
2 Constrained Optimization Models: Introduction and Concepts
25(18)
2.1 Constrained Optimization
25(4)
2.2 Classification of Models
29(4)
2.2.1 (1) Linear Program (LP)
30(1)
2.2.2 (2) Integer Linear Program (ILP)
31(1)
2.2.3 (3) Mixed Integer Linear Program (MILP)
31(1)
2.2.4 (4) Nonlinear Program (NLP)
32(1)
2.2.5 (5) Nonlinear Integer Program (NLIP)
33(1)
2.2.6 (6) Mixed Integer Nonlinear Program (MINLP)
33(1)
2.3 Solution Concepts
33(2)
2.4 Computational Complexity and Solution Methods
35(2)
2.5 Metaheuristics
37(3)
2.5.1 Greedy Hill Climbing
37(2)
2.5.2 Local Search Metaheuristics: Simulated Annealing
39(1)
2.5.3 Population Based Metaheuristics: Evolutionary Algorithms
39(1)
2.6 Discussion
40(1)
2.7 For Exploration
40(1)
2.8 For More Information
41(2)
3 Linear Programming
43(16)
3.1 Introduction
43(1)
3.2 Wagner Diet Problem
43(2)
3.3 Solving an LP
45(3)
3.4 Post-Solution Analysis of LPs
48(5)
3.5 More than One at a Time: The 100% Rule
53(4)
3.6 For Exploration
57(1)
3.7 For More Information
58(1)
II Optimization Modeling 59(102)
4 Simple Knapsack Problems
61(20)
4.1 Introduction
61(1)
4.2 Solving a Simple Knapsack in Excel
61(1)
4.3 The Bang-for-Buck Heuristic
62(2)
4.4 Post-Solution Analytics with the Simple Knapsack
64(8)
4.4.1 Sensitivity Analysis
64(7)
4.4.2 Candle Lighting Analysis
71(1)
4.5 Creating Simple Knapsack Test Models
72(2)
4.6 Discussion
74(1)
4.7 For Exploration
74(4)
4.8 For More Information
78(3)
5 Assignment Problems
81(16)
5.1 Introduction
81(1)
5.2 The Generalized Assignment Problem
82(3)
5.3 Case Example: GAP 1-c5-15-1
85(1)
5.4 Using Decisions from Evolutionary Computation
86(9)
5.5 Discussion
95(1)
5.6 For Exploration
95(1)
5.7 For More Information
96(1)
6 The Traveling Salesman Problem
97(14)
6.1 Introduction
97(1)
6.2 Problem Definition
98(1)
6.3 Solution Approaches
99(7)
6.3.1 Exact Algorithms
99(2)
6.3.2 Heuristic Algorithms
101(2)
6.3.2.1 Construction Heuristics
101(1)
6.3.2.2 Iterative Improvement or Local Search
102(1)
6.3.3 Putting Everything Together
103(3)
6.4 Discussion
106(1)
6.5 For Exploration
107(2)
6.6 For More Information
109(2)
7 Vehicle Routing Problems
111(8)
7.1 Introduction
111(1)
7.2 Problem Definition
112(1)
7.3 Solution Approaches
113(3)
7.3.1 Exact Algorithms
113(1)
7.3.2 Heuristic Algorithms
114(7)
7.3.2.1 Construction Heuristics
115(1)
7.3.2.2 Iterative Improvement or Local Search
115(1)
7.4 Extensions of VRP
116(1)
7.5 For Exploration
117(1)
7.6 For More Information
117(2)
8 Resource-Constrained Scheduling
119(10)
8.1 Introduction
119(1)
8.2 Formal Definition
120(1)
8.3 Solution Approaches
121(4)
8.3.1 Exact Algorithms
121(1)
8.3.2 Heuristic Algorithms
122(8)
8.3.2.1 Serial Method
123(1)
8.3.2.2 Parallel Method
123(1)
8.3.2.3 Iterative Improvement or Local Search
123(2)
8.4 Extensions of RCPSP
125(2)
8.5 For Exploration
127(1)
8.6 For More Information
127(2)
9 Location Analysis
129(20)
9.1 Introduction
129(1)
9.2 Locating One Service Center
130(2)
9.2.1 Minimizing Total Distance
130(2)
9.2.2 Weighting by Population
132(1)
9.3 A Naive Greedy Heuristic for Locating n Centers
132(4)
9.4 Using a Greedy Hill Climbing Heuristic
136(4)
9.5 Discussion
140(6)
9.6 For Exploration
146(1)
9.7 For More Information
147(2)
10 Two-Sided Matching
149(12)
10.1 Quick Introduction: Two-Sided Matching Problems
149(1)
10.2 Narrative Description of Two-Sided Matching Problems
150(2)
10.3 Representing the Problem
152(2)
10.4 Stable Matches and the Deferred Acceptance Algorithm
154(1)
10.5 Once More, in More Depth
155(1)
10.6 Generalization: Matching in Centralized Markets
156(1)
10.7 Discussion: Complications
157(2)
10.8 For More Information
159(2)
III Metaheuristic Solution Methods 161(42)
11 Local Search Metaheuristics
163(16)
11.1 Introduction
163(1)
11.2 Greedy Hill Climbing
163(7)
11.2.1 Implementation in Python
165(2)
11.2.2 Experimenting with the Greedy Hill Climbing Implementation
167(3)
11.3 Simulated Annealing
170(2)
11.4 Running the Simulated Annealer Code
172(1)
11.5 Threshold Accepting Algorithms
172(3)
11.6 Tabu Search
175(1)
11.7 For Exploration
175(2)
11.8 For More Information
177(2)
12 Evolutionary Algorithms
179(18)
12.1 Introduction
179(2)
12.2 EPs: Evolutionary Programs
181(7)
12.2.1 The EP Procedure
181(3)
12.2.2 Applying the EP Code to the Test Problems
184(1)
12.2.3 EP Discussion
184(4)
12.3 The Basic Genetic Algorithm (GA)
188(7)
12.3.1 The GA Procedure
188(5)
12.3.2 Applying the Basic GA Code to a Test Problem
193(1)
12.3.3 GA Discussion
193(2)
12.4 For Exploration
195(1)
12.5 For More Information
195(2)
13 Identifying and Collecting Decisions of Interest
197(6)
13.1 Kinds of Decisions of Interest (DoIs)
197(2)
13.2 The FI2-Pop GA
199(2)
13.3 Discussion
201(1)
13.4 For Exploration
202(1)
13.5 For More Information
202(1)
IV Post-Solution Analysis of Optimization Models 203(76)
14 Decision Sweeping
205(14)
14.1 Introduction
205(1)
14.2 Decision Sweeping with the GAP 1-c5-15-1 Model
205(2)
14.3 Deliberating with the Results of a Decision Sweep
207(7)
14.4 Discussion
214(1)
14.5 For Exploration
214(2)
14.6 For More Information
216(3)
15 Parameter Sweeping
219(10)
15.1 Introduction: Reminders on Solution Pluralism and Parameter Sweeping
219(1)
15.2 Parameter Sweeping: Post-Solution Analysis by Model Re-Solution
220(5)
15.2.1 One Parameter at a Time
221(1)
15.2.2 Two Parameters at a Time
222(1)
15.2.3 N Parameters at a Time
222(1)
15.2.4 Sampling
223(2)
15.2.5 Active Nonlinear Tests
225(1)
15.3 Parameter Sweeping with Decision Sweeping
225(1)
15.4 Discussion
226(1)
15.5 For Exploration
226(1)
15.6 For More Information
227(2)
16 Multiattribute Utility Modeling
229(14)
16.1 Introduction
229(1)
16.2 Single Attribute Utility Modeling
230(4)
16.2.1 The Basic Framework
230(1)
16.2.2 Example: Bringing Wine
231(3)
16.3 Multiattribute Utility Models
234(5)
16.3.1 Multiattribute Example: Picking a Restaurant
235(1)
16.3.2 The SMARTER Model Building Methodology
236(48)
16.3.2.1 Step 1: Purpose and Decision Makers
236(1)
16.3.2.2 Step 2: Value Tree
236(1)
16.3.2.3 Step 3: Objects of Evaluation
236(1)
16.3.2.4 Step 4: Objects-by-Attributes Table
237(1)
16.3.2.5 Step 5: Dominated Options
237(1)
16.3.2.6 Step 6: Single-Dimension Utilities
237(1)
16.3.2.7 Step 7: Do Part I of Swing Weighting
238(1)
16.3.2.8 Step 8: Obtain the Rank Weights
238(1)
16.3.2.9 Step 9: Calculate the Choice Utilities and Decide
239(1)
16.4 Discussion
239(1)
16.5 For Exploration
240(1)
16.6 For More Information
240(3)
17 Data Envelopment Analysis
243(10)
17.1 Introduction
243(4)
17.2 Implementation
247(1)
17.3 Demonstration of DEA Concept
247(3)
17.4 Discussion
250(1)
17.5 For Exploration
250(1)
17.6 For More Information
250(3)
18 Redistricting: A Case Study in Zone Design
253(26)
18.1 Introduction
253(1)
18.2 The Basic Redistricting Formulation
254(1)
18.3 Representing and Formulating the Problem
255(3)
18.4 Initial Forays for Discovering Good Districting Plans
258(9)
18.5 Solving a Related Solution Pluralism Problem
267(5)
18.6 Discussion
272(3)
18.7 For Exploration
275(1)
18.8 For More Information
276(3)
V Conclusion 279(10)
19 Conclusion
281(8)
19.1 Looking Back
281(1)
19.2 Revisiting Post-Solution Analysis
281(3)
19.3 Looking Forward
284(5)
19.3.1 Uncertainty
285(2)
19.3.2 Argumentation
287(2)
A Resources 289(2)
A.1 Resources on the Web
289(2)
Bibliography 291(12)
Index 303
Steven Orla Kimbrough, The Wharton School, University of Pennsylvania, Philadelphia, USA



Hoong Chuin Lau, School of Information Systems, Singapore Management University, Singapore
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