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Robustness Analysis in Decision Aiding, Optimization, and Analytics 1st ed. 2016 [Kõva köide]

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  • Formaat: Hardback, 321 pages, kõrgus x laius: 235x155 mm, kaal: 6387 g, 27 Illustrations, color; 38 Illustrations, black and white; XXI, 321 p. 65 illus., 27 illus. in color., 1 Hardback
  • Sari: International Series in Operations Research & Management Science 241
  • Ilmumisaeg: 21-Jul-2016
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319331191
  • ISBN-13: 9783319331195
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Robustness Analysis in Decision Aiding, Optimization, and Analytics 1st ed. 2016Suurem pilt
  • Formaat: Hardback, 321 pages, kõrgus x laius: 235x155 mm, kaal: 6387 g, 27 Illustrations, color; 38 Illustrations, black and white; XXI, 321 p. 65 illus., 27 illus. in color., 1 Hardback
  • Sari: International Series in Operations Research & Management Science 241
  • Ilmumisaeg: 21-Jul-2016
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319331191
  • ISBN-13: 9783319331195
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783319331195.html
Märksõnad:
This book provides a broad coverage of the recent advances in robustness analysis in decision aiding, optimization, and analytics. It offers a comprehensive illustration of the challenges that robustness raises in different operations research and management science (OR/MS) contexts and the methodologies proposed from multiple perspectives. Aside from covering recent methodological developments, this volume also features applications of robust techniques in engineering and management, thus illustrating the robustness issues raised in real-world problems and their resolution within advances in OR/MS methodologies. Robustness analysis seeks to address issues by promoting solutions, which are acceptable under a wide set of hypotheses, assumptions and estimates. In OR/MS, robustness has been mostly viewed in the context of optimization under uncertainty. Several scholars, however, have emphasized the multiple facets of robustness analysis in a broader OR/MS perspective that goes beyond the traditional framework, seeking to cover the decision support nature of OR/MS methodologies as well. As new challenges emerge in a big-data' era, where the information volume, speed of flow, and complexity increase rapidly, and analytics play a fundamental role for strategic and operational decision-making at a global level, robustness issues such as the ones covered in this book become more relevant than ever for providing sound decision support through more powerful analytic tools.
1 SMAA in Robustness Analysis
1(20)
Risto Lahdelma
Pekka Salminen
1.1 Introduction
1(2)
1.2 Problem Representation in SMAA
3(6)
1.2.1 Stochastic MCDA Problem
3(1)
1.2.2 Generic SMAA Simulation
4(1)
1.2.3 Decision Model
5(1)
1.2.4 Preference Information
6(1)
1.2.5 Cardinal Criteria
7(2)
1.2.6 Ordinal Criteria
9(1)
1.3 Robustness with Imprecise Criteria and Weights
9(6)
1.3.1 Rank Acceptability Indices
10(2)
1.3.2 Pairwise Winning Indices
12(1)
1.3.3 Central Weight Vectors
13(1)
1.3.4 Confidence Factors
13(2)
1.4 Robustness with Respect to Model Structure
15(1)
1.5 Recent Developments of SMAA
16(2)
1.6 Discussion
18(3)
References
19(2)
2 Data-Driven Robustness Analysis for Multicriteria Classification Problems Using Preference Disaggregation Approaches
21(18)
Michael Doumpos
Constantin Zopounidis
2.1 Introduction
21(2)
2.2 Preference Disaggregation for Multicriteria Classification
23(5)
2.2.1 General Framework
23(2)
2.2.2 Robust Approaches
25(3)
2.3 Data-Driven Robustness Indicators for Multicriteria Classification Problems
28(2)
2.4 Illustrative Results
30(5)
2.5 Conclusions and Future Research
35(4)
References
35(4)
3 Robustness for Adversarial Risk Analysis
39(20)
David Rios Insua
Fabrizio Ruggeri
Cesar Alfaro
Javier Gomez
3.1 Introduction
39(2)
3.2 Bayesian Robustness
41(1)
3.3 Sequential Games
42(5)
3.3.1 Game Theoretic Solution and Robustness
42(2)
3.3.2 ARA Solution and Robustness
44(3)
3.3.3 A Full Robust Solution
47(1)
3.4 Simultaneous Games
47(4)
3.4.1 Game Theoretic Solution
48(1)
3.4.2 ARA Solution and Robustness
49(2)
3.5 An Example
51(4)
3.5.1 Game Theoretic Approach
52(1)
3.5.2 Robustness of the Game Theoretic Solution
53(1)
3.5.3 ARA Approach
54(1)
3.5.4 Robustness of the ARA Solution
55(1)
3.6 Discussion
55(4)
References
57(2)
4 From Statistical Decision Theory to Robust Optimization: A Maximin Perspective on Robust Decision-Making
59(30)
Moshe Sniedovich
4.1 Introduction
59(1)
4.2 The Fundamental Decision Problem
60(2)
4.3 Wald's Maximin Paradigm
62(1)
4.4 Maximin Models at a Glance
63(3)
4.4.1 Security Levels
64(1)
4.4.2 Optimal Solutions
65(1)
4.4.3 A Constrained Optimization Perspective
65(1)
4.5 The Wald Factor
66(4)
4.6 Robustness
70(7)
4.6.1 Worst-Case-Based Robustness
71(1)
4.6.2 How Bad Should Worst Be?
72(1)
4.6.3 Global vs Local Robustness
73(4)
4.7 A Robust Decision-Making Perspective
77(6)
4.7.1 Robust Optimization
77(2)
4.7.2 Conservatism
79(1)
4.7.3 Irresponsible Decision-Making
80(1)
4.7.4 A Probabilistic Perspective on Worst-Case Analysis
81(2)
4.8 Can Wald's Maximin Paradigm Save the World?
83(6)
References
85(4)
5 The State of Robust Optimization
89(24)
Secil Sozuer
Aurelie C. Thiele
5.1 Introduction
89(2)
5.2 Theory of Robust Optimization
91(7)
5.2.1 Connection with Stochastic Optimization
91(3)
5.2.2 Nonlinear Optimization
94(1)
5.2.3 Multiple Objectives and Pareto Optimization
95(1)
5.2.4 Multi-Stage Decision-Making
96(2)
5.3 Application Areas of Robust Optimization
98(9)
5.3.1 Classical Logistics Problems
98(2)
5.3.2 Facility Location
100(1)
5.3.3 Supply Chain Management
101(1)
5.3.4 Industry-Specific Applications
102(1)
5.3.5 Finance
103(1)
5.3.6 Machine Learning and Statistics
104(1)
5.3.7 Energy Systems
104(1)
5.3.8 Public Good
105(2)
5.4 Conclusions and Guidelines for Implementation
107(6)
References
108(5)
6 Robust Discrete Optimization Under Discrete and Interval Uncertainty: A Survey
113(32)
Adam Kasperski
Pawel Zielinski
6.1 Introduction
113(4)
6.2 Robust Min-Max (Regret) Problems
117(10)
6.2.1 Using the Minmax Criterion
117(3)
6.2.2 Using the Minmax Regret Criterion
120(7)
6.3 Extensions of the Minmax Approach
127(5)
6.3.1 Using the OWA Criterion
127(3)
6.3.2 Using the WOWA Criterion
130(2)
6.4 Robust Optimization with Incremental Recourse
132(4)
6.4.1 Discrete Uncertainty Representation
134(1)
6.4.2 Interval Uncertainty Representation
135(1)
6.5 Robust Two-Stage Problems
136(3)
6.5.1 Discrete Uncertainty Representation
137(1)
6.5.2 Interval Uncertainty Representation
138(1)
6.6 Conclusions
139(6)
References
140(5)
7 Performance Analysis in Robust Optimization
145(26)
Andre Chassein
Marc Goerigk
7.1 Introduction
145(1)
7.2 Notations and Definitions
146(3)
7.2.1 General Notation
146(1)
7.2.2 The Uncertain Assignment Problem
147(1)
7.2.3 The Uncertain Knapsack Problem
148(1)
7.3 Approaches to Robust Optimization
149(10)
7.3.1 Strict Robustness
149(2)
7.3.2 Bounded Uncertainty
151(1)
7.3.3 Ellipsoidal Uncertainty
152(1)
7.3.4 Regret Robustness
153(4)
7.3.5 Recoverable Robustness
157(1)
7.3.6 Summary
158(1)
7.4 Frameworks to Evaluate Robust Solutions
159(2)
7.4.1 The Price of Robustness
159(1)
7.4.2 The AC-WC Curve
160(1)
7.4.3 The Scenario Curve
160(1)
7.4.4 The Sampled Scenario Curve
160(1)
7.4.5 The Scenario Curve with Recovery
161(1)
7.5 Experiments
161(10)
7.5.1 Assignment Problem
162(2)
7.5.2 Knapsack Problem
164(5)
References
169(2)
8 Robust-Soft Solutions in Linear Optimization Problems with Fuzzy Parameters
171(20)
Masahiro Inuiguchi
8.1 Introduction
171(1)
8.2 Blind Spots in Fuzzy Programming Approaches
172(6)
8.2.1 Linear Program with Fuzzy Objective Function Coefficients
172(1)
8.2.2 Solution Comparison by Objective Function Values
173(3)
8.2.3 Necessity and Possibility Measure Optimization
176(2)
8.3 Optimization Approaches
178(1)
8.3.1 Possible and Necessary Optimal Solutions
178(1)
8.3.2 Robust-Soft Optimal Solutions
178(1)
8.4 Solution Algorithms Under Given Fuzzy Goals
179(5)
8.5 Solving the Subproblem
184(3)
8.6 Solution Algorithms Under Unknown Goals
187(2)
8.7 Concluding Remarks
189(2)
References
189(2)
9 Robust Machine Scheduling Based on Group of Permutable Jobs
191(30)
Christian Artigues
Jean-Charles Billaut
Azzedine Cheref
Nasser Mebarki
Zakaria Yahouni
9.1 Introduction to Scheduling and Robust Scheduling
193(8)
9.1.1 Scheduling Problems
193(3)
9.1.2 Robustness in Scheduling
196(2)
9.1.3 Feasible Schedules and the Absolute Robustness Problem
198(1)
9.1.4 The Standard Solution Representation for (Robust) Disjunctive Scheduling
199(2)
9.2 Groups of Permutable Jobs: A Solution Structure for Robust Scheduling
201(8)
9.2.1 Groups of Permutable Jobs: A Flexible Solution Representation
202(2)
9.2.2 Combinatorial Optimization Problems on Group Sequences
204(5)
9.3 Solution Methods: A Recoverable Robust Approach Based on Groups of Permutable Operations
209(5)
9.3.1 MILP Formulation
210(1)
9.3.2 Tabu Search Algorithms
211(1)
9.3.3 Solution Algorithms for the Standard Robust Scheduling Method
212(1)
9.3.4 Computational Experiments
213(1)
9.4 Using Groups of Permutable Operations in an Industrial Context
214(7)
9.4.1 Heuristics for the Reactive Phase of Groups of Permutable Operations
215(1)
9.4.2 A Multi-Criteria Decision Support System (DSS) for Groups of Permutable Operations
216(3)
References
219(2)
10 How Robust is a Robust Policy? Comparing Alternative Robustness Metrics for Robust Decision-Making
221(18)
Jan H. Kwakkel
Sibel Eker
Erik Pruyt
10.1 Introduction
222(1)
10.2 Measuring Robustness
223(2)
10.3 Case
225(4)
10.3.1 Model
226(2)
10.3.2 Formulating the Problem
228(1)
10.4 Results
229(4)
10.5 Discussion
233(6)
References
236(3)
11 Developing Robust Climate Policies: A Fuzzy Cognitive Map Approach
239(26)
Alexandros Nikas
Haris Doukas
11.1 Introduction
240(2)
11.2 Fuzzy Cognitive Maps
242(3)
11.3 The Methodological Framework
245(11)
11.3.1 Determining the Group of Stakeholders
246(1)
11.3.2 Designing the Cognitive Map
247(3)
11.3.3 Inferring Causal Relation Weights
250(1)
11.3.4 Exploring the Time Dimension
251(1)
11.3.5 Quantifying Concepts
252(2)
11.3.6 Selecting Configuration Parameters
254(2)
11.3.7 Running Simulations
256(1)
11.4 Assessing Results
256(2)
11.5 Conclusions
258(7)
References
260(5)
12 Robust Optimization Approaches to Single Period Portfolio Allocation Problem
265(20)
Nalan Gulpinar
Zhezhi Hu
12.1 Introduction
265(2)
12.2 Robust Portfolio Management Model
267(2)
12.3 Defining Uncertainty Sets
269(1)
12.4 Derivation of Robust Counterpart
269(4)
12.5 Data-Driven Robust Optimization
273(2)
12.6 Distributionally Robust Optimization
275(2)
12.7 Robust Risk Measures
277(3)
12.8 Concluding Remarks
280(5)
References
280(5)
13 Portfolio Optimization with Second-Order Stochastic Dominance Constraints and Portfolios Dominating Indices
285(14)
Neslihan Fidan Kececi
Viktor Kuzmenko
Stan Uryasev
13.1 Introduction
286(1)
13.2 Second Order Stochastic Dominance (SSD)
286(2)
13.2.1 SSD Constraints for a Discrete Set of Scenarios
287(1)
13.2.2 Portfolio Optimization Problem with SSD Constraints
287(1)
13.3 Algorithm for Portfolio Optimization Problem with SSD Constraints
288(2)
13.3.1 Removing Redundant Constraints
288(1)
13.3.2 Cutting Plane Algorithm
288(1)
13.3.3 PSG Code for Optimization with SSD Constraints
289(1)
13.4 Case Study
290(7)
13.4.1 Estimation of Time-Varying Covariance Matrix
291(1)
13.4.2 Comparing Numerical Performance of Various Portfolio Settings
291(1)
13.4.3 Out-of-Sample Simulation
292(5)
13.5 Conclusions
297(2)
References
298(1)
14 Robust DEA Approaches to Performance Evaluation of Olive Oil Production Under Uncertainty
299(20)
Kazim Bans Atici
Nalan Gulpinar
14.1 Introduction
299(2)
14.2 DEA Modeling
301(3)
14.2.1 Deterministic DEA Model
301(1)
14.2.2 Imprecise DEA Model
302(2)
14.3 Robust DEA Approach
304(3)
14.3.1 Robust Linear Optimization
304(2)
14.3.2 Robust DEA Model
306(1)
14.4 Case Study: Performance of Olive Oil Growing Farms
307(1)
14.5 Computational Results
308(9)
14.5.1 Performance Comparison of Imprecise and Robust DEA Approaches
309(5)
14.5.2 Impact of Model Parameters
314(3)
14.6 Conclusions
317(2)
References
318(1)
Index 319
Michael Doumpos is associate professor of operations research at the School of Production Engineering and Management of the Technical University of Crete, Greece. His research interests include multiple criteria decision making, decision support systems, business intelligence, and financial risk management. He has published over 70 research papers in premier journals in operations research, management science, and finance. He has also authored and edited several books on intelligent decision support, multicriteria analysis, and financial decision making, and he has guest-edited more than 10 special issues on these areas for leading international journals. He serves as managing editor of the International Journal of Multicriteria Decision Making, and associate editor of Omega and the International Journal of Financial Engineering and Risk Management. He has been involved in a number of research projects funded by public organizations and the private sector, regarding the implementation of decision support systems and data analytic techniques in several areas such as finance, banking, energy management, and shipping.

Constantin Zopounidis is Professor of financial engineering and operations research at the Technical University of Crete (Greece), Distinguished Research Professor in Audencia Nantes School of Management (France), and Senior Academician of the Royal Academy of Economics and Financial Sciences of Spain, and member of the Royal Academy of Doctors of Spain. His research interests include financial engineering, financial risk management, and multiple criteria decision making. He has published over 300 papers in premier international journals, edited volumes, and conference proceedings. He has also published several books and edited volumes on financial engineering and operations research. Prof. Zopounidis is co-Editor-in-Chief of Operational Research-An International Journal (Springer), and editor-in-chief of the International Journalof Multicriteria Decision Making (Inderscience), and the International Journal of Financial Engineering and Risk Management (Inderscience). He also serves as topical editor for the Wiley Encyclopedia of Operations Research and Management Science, and associate editor and member of the editorial board for several journals such as the European Journal of Operational Research, the EURO Journal on Decision Processes, Optimization Letters, the International Transactions in Operational Research, among others. In recognition of his research work, he has received awards and honorary distinctions from several international research organizations. In 2013 he received the Edgeworth-Pareto Award from the International Society on Multiple Criteria Decision Making. Prof. Zopounidis is also the founder and elected president of the Financial Engineering and Banking Society.





Evangelos Grigoroudis is Associate Professor on management of quality processes in the School of Production Engineering and Management of the Technical University of Crete, Greece (2002-). He followed postgraduate studies in Technical University of Crete, Greece from where he received his Ph.D. degree in 1999. He has received distinctions from the Hellenic Operational Research Society, the Academy of Business and Administrative Sciences, the World Automation Congress, the Foundation of Ioannis and Vasileia Karayianni, the Technical University of Crete, and the State Scholarships Foundation of Greece. He acts as reviewer for more than 50 scientific journals, and he is associate editor of the Operational Research: An International Journal, International Journal of Decision Support Systems, Journal of Knowledge Economy, International Journal of Social Ecology and Sustainable Development, Journal of Innovation and Entrepreneurship, Journal of Technology, Innovation and Education, and International Journal of Food and Beverage Manufacturing and Business Models and member of the Editorial Boardof the scientific journals: International Journal of Information and Decision Sciences, International Journal of Information Systems in the Service Sector, International Journal of Multicriteria Decision Making. He is author/editor of 17 books on the measurement of service quality, the business strategy and management, and the multicriteria decision aid approaches, as well as of a significant number of research reports and papers in scientific journals and conference proceedings. He has participated in the organisation of many scientific conferences and he has numerous presentations in scientific conferences. His research interests include service quality measurement processes, customer and employee satisfaction, performance evaluation, business excellence, operational research (evaluation methodologies and techniques), multicriteria decision analysis, data analysis (qualitative data analysis methods), and marketing (market and customer satisfaction surveys).