Robustness Metrics: How Are They Calculated, When Should They Be 1
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A Practical Guide to Robust Optimization
A Practical Guide to Robust Optimization Bram L. Gorissen, Ihsan Yanıkoğlu, Dick den Hertog Tilburg University, Department of Econometrics and Operations Research, 5000 LE Tilburg, Netherlands {b.l.gorissen,d.denhertog}@tilburguniversity.edu, [email protected] Abstract Robust optimization is a young and active research field that has been mainly developed in the last 15 years. Robust optimization is very useful for practice, since it is tailored to the information at hand, and it leads to computationally tractable formulations. It is therefore remarkable that real-life applications of robust optimization are still lagging behind; there is much more potential for real-life applications than has been exploited hitherto. The aim of this paper is to help practitioners to understand robust optimization and to successfully apply it in practice. We provide a brief introduction to robust optimization, and also describe important do’s and don’ts for using it in practice. We use many small examples to illustrate our discussions. 1 Introduction Real-life optimization problems often contain uncertain data. Data can be inherently stochas- tic/random or it can be uncertain due to errors. The reasons for data errors could be measure- ment/estimation errors that come from the lack of knowledge of the parameters of the mathematical model (e.g., the uncertain demand in an inventory model) or could be implementation errors that come from the physical impossibility to exactly implement a computed solution in a real-life set- ting. There are two approaches to deal with data uncertainty in optimization, namely robust and stochastic optimization. Stochastic optimization (SO) has an important assumption, i.e., the true probability distribution of uncertain data has to be known or estimated. -
Distributionally Robust Optimization for Engineering Design Under Uncertainty
Distributionally Robust Optimization for Engineering Design under Uncertainty Michael G. Kapteyn∗ Massachusetts Institute of Technology, Cambridge, MA 02139, USA Karen E. Willcoxy The University of Texas at Austin, Austin, TX 78712, USA Andy B. Philpottz The University of Auckland, Auckland 1010, New Zealand This paper addresses the challenge of design optimization under uncertainty when the de- signer only has limited data to characterize uncertain variables. We demonstrate that the error incurred when estimating a probability distribution from limited data affects the out- of-sample performance (i.e., performance under the true distribution) of optimized designs. We demonstrate how this can be mitigated by reformulating the engineering design prob- lem as a distributionally robust optimization (DRO) problem. We present computationally efficient algorithms for solving the resulting DRO problem. The performance of the DRO approach is explored in a practical setting by applying it to an acoustic horn design prob- lem. The DRO approach is compared against traditional approaches to optimization under uncertainty, namely, sample-average approximation and a multi-objective optimization in- corporating a risk reduction objective. In contrast with the multi-objective approach, the proposed DRO approach does not use an explicit risk reduction objective but rather speci- fies a so-called ambiguity set of possible distributions, and optimizes against the worst-case distribution in this set. Our results show that the DRO designs in some cases significantly outperform those designs found using the sample-average or multi-objective approaches. I. Introduction The importance of considering the nature of the uncertain parameters in engineering design has long been established,1{3 the primary reason being that simply considering all parameters to be deterministic tends to over-fit designs to the chosen parameter values. -
A Theory of Regret Regulation 1.0
JOURNAL OF CONSUMER PSYCHOLOGY, 17(1), 3–18 Copyright © 2007, Lawrence Erlbaum Associates, Inc. HJCP A Theory of Regret Regulation 1.0 Regret Regulation Theory Marcel Zeelenberg and Rik Pieters Tilburg University, The Netherlands We propose a theory of regret regulation that distinguishes regret from related emotions, specifies the conditions under which regret is felt, the aspects of the decision that are regret- ted, and the behavioral implications. The theory incorporates hitherto scattered findings and ideas from psychology, economics, marketing, and related disciplines. By identifying strate- gies that consumers may employ to regulate anticipated and experienced regret, the theory identifies gaps in our current knowledge and thereby outlines opportunities for future research. The average consumer makes a couple of thousands of concerning regret. Regret research originated in basic decisions daily. These include not only the decision of research in economics (Bell, 1982; Loomes & Sugden, 1982), which products and brands to buy and in which quantity, and psychology (Gilovich & Medvec, 1995; Kahneman & but also what to eat for breakfast, what kind of tea to Tversky, 1982; Landman, 1993). Nowadays one can find drink, whether to read the morning newspaper, which spe- examples of regret research in many different domains, such cific articles and ads (and whether to act on them), when to as marketing (Inman & McAlister, 1994; Simonson, 1992), stop reading, which shows to watch on which TV-channels law (Guthrie, 1999; Prentice & Koehler, 2003), organiza- (and whether to zap or zip), or maybe watch a DVD, and tional behavior (Goerke, Moller, & Schulz-Hardt, 2004; for how long, how to commute to work or school, and so Maitlis & Ozcelik, 2004), medicine (Brehaut et al., 2003; forth. -
Robust and Data-Driven Optimization: Modern Decision-Making Under Uncertainty
Robust and Data-Driven Optimization: Modern Decision-Making Under Uncertainty Dimtris Bertsimas¤ Aur¶elieThieley March 2006 Abstract Traditional models of decision-making under uncertainty assume perfect information, i.e., ac- curate values for the system parameters and speci¯c probability distributions for the random variables. However, such precise knowledge is rarely available in practice, and a strategy based on erroneous inputs might be infeasible or exhibit poor performance when implemented. The purpose of this tutorial is to present a mathematical framework that is well-suited to the limited information available in real-life problems and captures the decision-maker's attitude towards uncertainty; the proposed approach builds upon recent developments in robust and data-driven optimization. In robust optimization, random variables are modeled as uncertain parameters be- longing to a convex uncertainty set and the decision-maker protects the system against the worst case within that set. Data-driven optimization uses observations of the random variables as direct inputs to the mathematical programming problems. The ¯rst part of the tutorial describes the robust optimization paradigm in detail in single-stage and multi-stage problems. In the second part, we address the issue of constructing uncertainty sets using historical realizations of the random variables and investigate the connection between convex sets, in particular polyhedra, and a speci¯c class of risk measures. Keywords: optimization under uncertainty; risk preferences; uncertainty sets; linear program- ming. 1 Introduction The ¯eld of decision-making under uncertainty was pioneered in the 1950s by Dantzig [25] and Charnes and Cooper [23], who set the foundation for, respectively, stochastic programming and ¤Boeing Professor of Operations Research, Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02139, [email protected] yP.C. -
A New Robust Optimization Approach for Scheduling Under Uncertainty II
Computers and Chemical Engineering 31 (2007) 171–195 A new robust optimization approach for scheduling under uncertainty II. Uncertainty with known probability distribution Stacy L. Janak, Xiaoxia Lin, Christodoulos A. Floudas∗ Department of Chemical Engineering, Princeton University, Princeton, NJ 08544-5263, USA Received 1 April 2005; received in revised form 3 May 2006; accepted 22 May 2006 Available online 1 August 2006 Abstract In this work, we consider the problem of scheduling under uncertainty where the uncertain problem parameters can be described by a known probability distribution function. A novel robust optimization methodology, originally proposed by Lin, Janak, and Floudas [Lin, X., Janak, S. L., & Floudas, C. A. (2004). A new robust optimization approach for scheduling under uncertainty: I. Bounded uncertainty. Computers and Chemical Engineering, 28, 1069–1085], is extended in order to consider uncertainty described by a known probability distribution. This robust optimization formulation is based on a min–max framework and when applied to mixed-integer linear programming (MILP) problems, produces “robust” solutions that are immune against data uncertainty. Uncertainty is considered in the coefficients of the objective function, as well as the coefficients and right-hand-side parameters of the inequality constraints in MILP problems. Robust optimization techniques are developed for uncertain data described by several known distributions including a uniform distribution, a normal distribution, the difference of two normal distributions, a general discrete distribution, a binomial distribution, and a poisson distribution. The robust optimization formulation introduces a small number of auxiliary variables and additional constraints into the original MILP problem, generating a deterministic robust counterpart problem which provides the optimal/feasible solution given the (relative) magnitude of the uncertain data, a feasibility tolerance, and a reliability level. -
Algorithms and Concepts for Robust Optimization
Algorithms and Concepts for Robust Optimization Dissertation zur Erlangung des mathematisch-naturwissenschaftlichen Doktorgrades Doctor rerum naturalium der Georg-August Universit¨atG¨ottingen vorgelegt von Marc Goerigk aus Berlin G¨ottingen2012 Referentin: Prof. Dr. Anita Sch¨obel Koreferent: Prof. Dr. Marco L¨ubbecke Tag der m¨undlichen Pr¨ufung 24.9.2012 Preface The more I occupied myself with robust optimization, the more it grew from a mathe- matical discipline to a pattern of thought that influenced my everyday decisions. Un- certainty is all around us - from the flight to Tromsø or the train to Saarbr¨ucken, to the details like lactose in sandwiches (better don't try if you're not sure). Along my way I had plenty of people who helped me and generously overlooked some overconservatism stemming from restrictive robustness concepts. First of all, I thank Anita Sch¨obel for endless support and her impressive ability to not sleep - at least, that's how it seems. Also the great spirit of the optimization working group at the Institute for Numerical and Applied Mathematics made writing this thesis a joy: Thanks to Marco Bender, Ruth H¨ubner,Jonas Ide (\your partner in science"), Mark K¨orner(\der große Mark"), Thorsten Krempasky (\Dr. Lightning"), Michael Schachtebeck, Robert Schieweck, Marie Schmidt (\der kleine Marc"), Daniel Scholz, Morten Tiedemann and Stephan Westphal. Also, the proof-reading of Marie, Ruth, Marco and Robert was a great help during the final phase of the work. I thank Florian Bruns, Emilio Carrizosa, Markus Chimani, Martin Knoth, Sigrid Knust, Matthias M¨uller-Hannemann,Petra Mutzel, and Bernd Zey for the inspira- tional and challenging joint work (see Chapter 8 for an overview). -
Weighted Sets of Probabilities and Minimax Weighted Expected Regret: New Approaches for Representing Uncertainty and Making Decisions∗
Weighted Sets of Probabilities and Minimax Weighted Expected Regret: New Approaches for Representing Uncertainty and Making Decisions∗ Joseph Y. Halpern Samantha Leung Cornell University Cornell University [email protected] [email protected] Abstract 1 broken 10 broken cont 10,000 -10,000 back 0 0 We consider a setting where an agent’s uncer- check 5,001 -4,999 tainty is represented by a set of probability mea- sures, rather than a single measure. Measure- Table 1: Payoffs for the robot delivery problem. Acts are in by-measure updating of such a set of measures the leftmost column. The remaining two columns describe upon acquiring new information is well-known the outcome for the two sets of states that matter. to suffer from problems; agents are not always able to learn appropriately. To deal with these problems, we propose using weighted sets of probabilities: a representation where each mea- baker’s delivery robot, T-800, is delivering 1; 000 cupcakes sure is associated with a weight, which de- from the bakery to a banquet. Along the way, T-800 takes a notes its significance. We describe a natural tumble down a flight of stairs and breaks some of the cup- approach to updating in such a situation and cakes. The robot’s map indicates that this flight of stairs a natural approach to determining the weights. must be either ten feet or fifteen feet high. For simplicity, We then show how this representation can be assume that a fall of ten feet results in one broken cupcake, used in decision-making, by modifying a stan- while a fall of fifteen feet results in ten broken cupcakes. -
A Quantitative Measurement of Regret Theory
MANAGEMENT SCIENCE informs ® Vol. 56, No. 1, January 2010, pp. 161–175 doi 10.1287/mnsc.1090.1097 issn eissn 0025-1909 1526-5501 10 5601 0161 © 2010 INFORMS A Quantitative Measurement of Regret Theory Han Bleichrodt Erasmus School of Economics, Erasmus University, 3000 DR Rotterdam, The Netherlands, [email protected] Alessandra Cillo IESE Business School, 08034 Barcelona, Spain, [email protected] Enrico Diecidue Decision Sciences Area, INSEAD, 77305 Fontainebleau Cedex, France, [email protected] his paper introduces a method to measure regret theory, a popular theory of decision under uncertainty. TRegret theory allows for violations of transitivity, and it may seem paradoxical to quantitatively measure an intransitive theory. We adopt the trade-off method and show that it is robust to violations of transitivity. Our method makes no assumptions about the shape of the functions reflecting utility and regret. It can be performed at the individual level, taking account of preference heterogeneity. Our data support the main assumption of regret theory, that people are disproportionately averse to large regrets, even when event-splitting effects are controlled for. The findings are robust: similar results were obtained in two measurements using different stimuli. The data support the reliability of the trade-off method: its measurements could be replicated using different stimuli and were not susceptible to strategic responding. Key words: regret theory; utility measurement; decision under uncertainty History: Received May 20, 2008; accepted August 3, 2009, by George Wu, decision analysis. Published online in Articles in Advance November 6, 2009. 1. Introduction Regret theory also has real-world implications and Regret theory (Bell 1982, Loomes and Sugden 1982) can explain field data that are incompatible with is an important theory of decision under uncertainty. -
The Art and Science of Modeling Decision-Making Under Severe Uncertainty
Decision Making in Manufacturing and Services Vol. 1 • 2007 • No. 1{2 • pp. 111{136 The Art and Science of Modeling Decision-Making Under Severe Uncertainty Moshe Sniedovich∗ Abstract. For obvious reasons, models for decision-making under severe uncertainty are austere. Simply put, there is precious little to work with under these conditions. This fact highlights the great importance of utilizing in such cases the ingredients of the mathematical model to the fullest extent, which in turn brings under the spotlight the art of mathematical modeling. In this discussion we examine some of the subtle considerations that are called for in the mathematical modeling of decision-making under severe uncertainty in general, and worst-case analysis in particular. As a case study we discuss the lessons learnt on this front from the Info-Gap experience. Keywords: mathematical modeling, severe uncertainty, maximin, worst-case analysis, robust optimization, info-gap. Mathematics Subject Classification: 90B50, 90C47, 91B06. Received/Revised: 04 April 2007/30 May 2007 1. INTRODUCTION Methodologies designed for decision-making under severe uncertainty are austere in the extreme because one has precious little to work with in such cases. This is vividly manifested in the simple structure of the mathematical models deployed by these methodologies. And so given the simplicity of such models, it is not surprising that these method- ologies are discussed in introductory OR/MS textbooks, eg. Markland and Sweigart (1987), Winston (1994), Ragsdale (2004), Hillier and Lieberman (2005). But from a practical point of view this means that users of such models are called upon to be imaginative and inventive if they are to make the most of them. -
A Quantitative Measurement of Regret Theory1 IESE, Avda. Pearson 21
A Quantitative Measurement of Regret Theory1 HAN BLEICHRODT Department of Economics, H13-27, Erasmus University, P.O. Box 1738, 3000 DR Rotterdam, The Netherlands. ALESSANDRA CILLO IESE, Avda. Pearson 21, 08034 Barcelona, Spain ENRICO DIECIDUE INSEAD, Boulevard de Constance, 77305 Fontainebleau Cedex, France. March 2007 Abstract This paper introduces a choice-based method that for the first time makes it possible to quantitatively measure regret theory, one of the most popular models of decision under uncertainty. Our measurement is parameter-free in the sense that it requires no assumptions about the shape of the functions reflecting utility and regret. The choice of stimuli was such that event-splitting effects could not confound our results. Our findings were largely consistent with the assumptions of regret theory although some deviations were observed. These deviations can be explained by psychological heuristics and reference- dependence of preferences. KEYWORDS: Regret theory, utility measurement, decision under uncertainty. 1 We are grateful to Graham Loomes, Stefan Trautmann, Peter P. Wakker, George Wu, an associate editor, and two referees for their comments on previous drafts of this paper, to Alpika Mishra for writing the computer program used in the experiment, and to Arthur Attema for his help in running the experiment. Han Bleichrodt’s research was supported by a VIDI grant from the Netherlands Organization for Scientific Research (NWO). Alessandra Cillo and Enrico Diecidue’s research was supported by grants from the INSEAD R&D department and the INSEAD Alumni Fund (IAF). 1. Introduction Regret theory, first proposed by Bell (1982) and Loomes and Sugden (1982), is one of the most popular nonexpected utility models. -
Statistics of Robust Optimization: a Generalized Empirical Likelihood Approach
Statistics of Robust Optimization: A Generalized Empirical Likelihood Approach John C. Duchi1 Peter W. Glynn2 Hongseok Namkoong2 1Department of Statistics 2Department of Management Science and Engineering Stanford University fjduchi,glynn,[email protected] Stanford University Abstract We study statistical inference and robust solution methods for stochastic optimization prob- lems. We first develop an empirical likelihood framework for stochastic optimization. We show an empirical likelihood theory for Hadamard differentiable functionals with general f-divergences and give conditions under which T (P ) = infx2X EP [`(x; ξ)] is Hadamard differentiable. Noting that the right endpoint of the generalized empirical likelihood confidence interval is a distribu- tionally robust optimization problem with uncertainty regions given by f-divergences, we show various statistical properties of robust optimization. First, we give a statistically principled method of choosing the size of the uncertainty set to obtain a calibrated one-sided confidence interval. Next, we give general conditions under which the robust solutions are consistent. Fi- nally, we prove an asymptotic expansion for the robust formulation, showing how robustification regularizes the problem. 1 Introduction In this paper, we study the properties of robust solution methods, in particular statistical and inferential guarantees, for the stochastic optimization problem Z minimize EP0 [`(x; ξ)] = `(x; ξ)dP0(ξ): (1) x2X Ξ d In the formulation (1), the feasible region X ⊂ R is a nonempty closed set, ξ is a random vector on the probability space (Ξ; A;P0), where the domain Ξ is a (subset of) a separable metric space, and the function ` : X × Ξ ! R is a lower semi-continuous (loss) functional. -
Data-Driven Robust Optimization
Math. Program., Ser. A DOI 10.1007/s10107-017-1125-8 FULL LENGTH PAPER Data-driven robust optimization Dimitris Bertsimas1 · Vishal Gupta2 · Nathan Kallus3 Received: 13 September 2015 / Accepted: 14 February 2017 © Springer-Verlag Berlin Heidelberg and Mathematical Optimization Society 2017 Abstract The last decade witnessed an explosion in the availability of data for opera- tions research applications. Motivated by this growing availability, we propose a novel schema for utilizing data to design uncertainty sets for robust optimization using sta- tistical hypothesis tests. The approach is flexible and widely applicable, and robust optimization problems built from our new sets are computationally tractable, both theoretically and practically. Furthermore, optimal solutions to these problems enjoy a strong, finite-sample probabilistic guarantee whenever the constraints and objective function are concave in the uncertainty. We describe concrete procedures for choos- ing an appropriate set for a given application and applying our approach to multiple uncertain constraints. Computational evidence in portfolio management and queueing confirm that our data-driven sets significantly outperform traditional robust optimiza- tion techniques whenever data are available. Keywords Robust optimization · Data-driven optimization · Chance-constraints · Hypothesis testing B Vishal Gupta [email protected] Dimitris Bertsimas [email protected] Nathan Kallus [email protected] 1 Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02139, USA 2 Marshall School of Business, University of Southern California, Los Angeles, CA 90029, USA 3 School of Operations Research and Information Engineering, Cornell University and Cornell Tech, New York, NY 10011, USA 123 D. Bertsimas et al. Mathematics Subject Classification 80M50 (Optimization: Operations research, mathematical programming) · 62H15 (Multivariate Analysis: Hypothesis Testing) 1 Introduction Robust optimization is a popular approach to optimization under uncertainty.