bluuebunny/crossref_metadata_2025_split huggingface dataset:
Customer Relationship Management: Maximizing Customer Lifetime Value
Authors: Lewis Michael 
Published: February 2011

AbstractAn inherent difficulty in the practice of Customer Relationship Management is that the development of marketing policies that optimize the value of a firm's customer assets requires fairly sophisticated statistical and optimization techniques. This article reviews the existing academic literature focused on methods for optimizing customer lifetime value. The article covers key customer metrics and the use of dynamic programming to maximize customer value and reviews existing applications. The article also discusses limitations of the current body of research and opportunities for developing optimization models that employ rich models of consumer behavior.
D
antzig–
W
olfe Decomposition
Authors: Chung William 
Published: February 2011

Abstract
In this section, the development of the Danztig–Wolfe (DW) decomposition method is presented. The topics include not only the details of the DW decomposition algorithm, but also the discussions of computational efficiency, long‐tail effect, column dropping, parallel implementation, the concept of price‐directive coordination, optimal degree of decentralization, and organizational studies. Moreover, other non‐LP developments of the DW method are briefly introduced. They are the DW method of staircase LP, NLP, MIP, and variational inequality problems.
It should be noted that the DW method may be significantly less efficient than the simplex approach. Hence, the DW method should be the only algorithmic choice when the problem is too large to be solved in one piece. Another motivation of the use of the DW decomposition is to ease model development and maintenance by its solution method that links separately developed submodels only when a joint model solution is desired.
On the other hand, the recent applications of the DW method concern (i) how to generalize the DW method to integer programming's branch‐and‐price procedures and (ii) how to apply the DW method to equilibrium models.
Stochastic Mixed‐Integer Programming Algorithms: Beyond Benders' Decomposition
Authors: Sen Suvrajeet 
Published: February 2011

Abstract
This article is dedicated to a new generation of decomposition algorithms that are designed to solve two‐stage stochastic mixed‐integer programming problems. This class of methods, which constitute extensions of Benders' decomposition, is designed to allow integer variables within subproblems, and such situations occur naturally within stochastic mixed‐integer programming problems. In addition to providing a unified treatment of the algorithms, we also summarize some computational results.
Descriptive Models Of Decision Making
Authors: Baucells Manel , Katsikopoulos Konstantinos V. 
Published: February 2011

AbstractHow should people decide in situations involving risk, time, uncertainty, and multiattribute alternatives? How do people actually decide? In this article, we contrast the rational decision maker with the modal subject observed in experiments and surveys. We use a simple setup to present preference models and decision processes that are descriptively more accurate than the rational model.
DTMCS
with Costs and Rewards
Authors: Xu Susan 
Published: February 2011

Abstract
A discrete‐time Markov chain (DTMC) with an attached reward to a state occupancy or to a state transition is called a DTMC with rewards. DTMC reward models find wide applications in many stochastic systems such as inventory management and portfolio performance. This article reviews several performance measures and solution techniques commonly used in DTMC reward models.
Dynamic Pricing Strategies for Multiproduct Revenue Management Problems
Authors: Maglaras Costis 
Published: February 2011

Abstract
This article reviews multiproduct dynamic pricing models for a revenue maximizing monopolist firm. The baseline model studied in this article is of a seller that owns a fixed capacity of a resource that is consumed in the production or delivery of some type of product. The seller selects a dynamic pricing strategy for the offered product so as to maximize its total expected revenues over a finite time horizon. We then review how this model can be extended to settings where the firm is selling multiple products that consume this firm's capacity, and finally highlight a connection between these dynamic pricing models and the closely related model where prices are fixed, and the seller dynamically controls how to allocate capacity to requests for the different products. Methodologically, this article reviews the dynamic programming formulations of the above problems, as well as their associated deterministic (fluid) analogues. It highlights some of the key insights and pricing heuristics that are known for these problems, and briefly mentions possible extensions and areas of current interest.
Computation and Dynamic Programming
Authors: Topaloglu Huseyin 
Published: February 2011

AbstractDynamic programming provides a structured framework for solving sequential decision‐making problems under uncertainty, but its computational appeal has traditionally been limited when dealing with problems with large state and action spaces. In this article, we describe a variety of methods that are targeted towards alleviating the computational difficulties associated with dynamic programming. The methods that we describe here are simulation based and model free methods, the linear programming approach to approximate dynamic programming, approximate policy iteration, rollout policies, and state aggregation. An important aspect of the methods that we describe in this article is that they use parsimoniously parameterized value function approximations to ensure that the approximations can be stored in a tractable fashion and they utilize simulation to avoid computing expectations explicitly.
Effective Application of Simulated Annealing
Authors: Ohlmann Jeffrey W. , Van Laarhoven Jon W. 
Published: February 2011

Abstract
First adapted as a solution approach for combinatorial optimization problems in the 1980s, simulated annealing and its variants continue to enjoy widespread application. The widespread application of SA is due to the relative ease of implementing its logic, its general nature and flexibility allowing use with little problem‐specific tailoring (at least for alpha versions of an implementation), and its robust performance with respect to solution quality and running time on a wide array of problems. This article describes implementation details for various features of the simulated annealing algorithm, and then illustrates the development of annealing‐based algorithms through the description of several applications.
Eliciting Subjective Probability Distributions from Groups
Authors: Daneshkhah Alireza , Revie Matthew , Bedford Tim , Walls Lesley , Quigley John 
Published: February 2011

AbstractUncertainty analysis has become an increasingly important part of risk assessments and operations research models, and the role of expert judgment in providing information for decision making has become more useful. Decision makers often have access to more than one expert, and it is common to make the decisions on the basis of the expertise of several experts, which leads to the problem of how to combine or aggregate the experts' judgments. A number of approaches have been proposed as to how to elicit, and how to synthesize, the different experts' knowledge. We discuss a variety of models that lead to specific combination methods. The output of these methods is a “combined probability distribution,” which can be viewed as representing a summary of the current state of information regarding the uncertainty of interest. We also briefly review the psychology literature relating to group decision making, which, obviously, is relevant to behavioral aggregation.
Estimating Intensity and Mean Value Function
Authors: Giorgio Massimiliano , Pulcini Gianpaolo 
Published: February 2011

AbstractThis article provides nonparametric and parametric estimation procedures of the intensity function and of the mean value function of point stochastic processes describing the failure process of repairable systems. Data arising both from a single repairable system and from multiple repairable systems are analyzed. A specific attention is devoted to two frequently used processes: the homogeneous Poisson process and the power law process. Two numerical examples are then given to illustrate the estimation procedures.
Estimating Survival Probability
Authors: Giorgio Massimiliano , Pulcini Gianpaolo 
Published: February 2011

AbstractThis article illustrates a number of classical nonparametric and parametric procedures for estimating the survival probability (often called the reliability function) of nonrepairable units. Both point and interval estimation procedures are discussed and some graphical methods are briefly presented. A specific attention is devoted to two frequently used distribution functions: the standard exponential and two‐parameter Weibull distributions. Finally, a number of examples are given to illustrate the estimation procedures.
Evaluating and Comparing Forecasting Models
Authors: Wang Shouyi , Chaovalitwongse Wanpracha Art 
Published: February 2011

Abstract
Forecasting is a very important aspect of any business, and has enormous social, economic, and environmental impacts. Various forecasting models have been developed to help people make right decisions against future uncertainties. However, all forecasting models have distinct advantages and limitations. Selecting appropriate forecasting methods from numerous alternatives is crucial to success. This article briefly summarizes the state‐of‐the‐art forecasting methods in terms of basic procedure, underlying assumptions, applications, and limits. And then the most popular model selection criteria and guidelines are presented.
Evolutionary Game Theory and Evolutionary Stability
Authors: Cressman Ross 
Published: February 2011

Abstract
Evolutionary game theory is used to predict the behavior of individuals in populations (either of humans or other species) without relying on a detailed description of how these behaviors evolve over time (e.g., the replicator equation or the best response dynamics). For instance, if these behaviors correspond to a population state that satisfies the static payoff‐comparison conditions of an evolutionarily stable strategy (ESS), then there is typically dynamic (i.e., evolutionary) stability at this state. We begin with a thorough summary of the evolutionary game theory perspective, when there is a finite set of (pure) strategies, for a symmetric two‐player game in either normal or extensive form. The article then briefly discusses generalizations of evolutionary game theory, ESS, and evolutionary stability to several other classes of games. These include symmetric population games where payoffs to pure strategies are nonlinear functions of the current population state as well as asymmetric games where players are assigned different roles (e.g., two‐role bimatrix games). Although terminology borrowed from evolutionary game theory applied to behaviors of individuals in biological species is used throughout, the concepts introduced are equally relevant in games modeling human behavior.
Fluid Models of Queueing Networks
Authors: Gamarnik David 
Published: February 2011

Abstract
Fluid models are a powerful approximation technique for studying important dynamic properties of queueing networks. The model is typically derived from an underlying queueing system by replacing a stochastic discrete model with a continuous deterministic model, using a certain law of large numbers type of rescaling. The obtained process reveals a lot of fundamental dynamic structures in the underlying system. This article introduces basics of fluid models and illustrates them with a lot of examples. We begin with an informal construction to be followed by rigorous definitions and basic properties. A particular focus is given to the notions of stability of fluid models and methods of stability analysis such as the Lyapunov function technique. Examples of stable fluid models are given as well as some counterexamples, illustrating subtle issues in stability analysis. The formality of connections between fluid models and underlying stochastic queueing systems is discussed at a very high level, since the underlying techniques are highly involved and fall outside of the scope of the article. Instead we point the reader to a rich literature on this subject.
Gradient‐Type Methods
Authors: Pajouh Foad Mahdavi , Balasundaram Balabhaskar 
Published: February 2011

Abstract

The gradient method
, which is also called
the method of steepest descent
, and
the Cauchy method
, is one of the most fundamental derivative‐based procedure for unconstrained minimization of a differentiable function. The performance of the method in terms of speed of convergence is lacking, and it tends to suffer from very slow convergence, especially as a stationary point is approached. However, it does guarantee global convergence under reasonable conditions and admits a thorough mathematical analysis of its behavior. For this reason, the gradient method has been used as a starting point in the development of more sophisticated, globally convergent algorithms with better convergence properties for unconstrained minimization. This article presents a cogent overview of this fundamental method and its convergence properties under various settings.
Graph Search Techniques
Authors: Yang Boting 
Published: February 2011

AbstractGiven a graph, suppose that there is a robber hiding on vertices or along edges. A graph searching problem is to find the minimum number of searchers required to capture the robber. In this article, we describe the main characteristics of graph searching problems. For undirected graphs, we consider the edge search, node search, mixed search, visible‐robber game, and inert‐robber game. For digraphs, we consider directed search, strong search, weak search, strong direct visible‐robber game, directed visible‐robber game, and directed inert‐robber game. We also deal with cops‐and‐robber games in which cops and the robber take turns to move. Most searching problems correspond to width parameters in graph theory. We describe the relationships between models and width parameters. We only survey the basic models, giving references to other variants.
Grasp: Greedy Randomized Adaptive Search Procedures
Authors: Resende Mauricio G. C. , Silva Ricardo M. A. 
Published: February 2011

AbstractGRASP or greedy randomized adaptive search procedure, is a multistart metaheuristic that repeatedly applies local search starting from solutions constructed by a randomized greedy algorithm. In this article we review the basic building blocks of GRASP. We cover solution construction schemes, local search methods, and the use of path‐relinking as a memory mechanism in GRASP.
History of LP Development
Authors: Murty Katta G. 
Published: February 2011

AbstractThe linear programming (LP) model is the most commonly used mathematical model in decision‐making applications today. LP is the branch of mathematics dealing with methods for solving systems of linear constraints including linear inequalities, and possibly also linear equations. Its origin has its roots in the development of mathematics itself going back to ancient times. The first method developed over 2000 years ago can handle only systems of linear equations, leading to the branch of mathematics now known as linear algebra. After a very long gap, methods for handling LPs have been developed only in the twentieth century. In this article, we summarize the milestones in the fascinating history of LP.
Housing and Community Development
Authors: Johnson Michael P. 
Published: February 2011

AbstractResearchers in housing and community develop, design, and evaluate policies to improve access to attractive, affordable, and sustainable housing and to support the social, physical, and economic infrastructure of communities. Practitioners in this field confront political considerations, administrative guidelines, and limited funding. Operations research and management science can increase the efficiency and effectiveness of policy responses to issues such as affordable housing, residential segregation, and inequalities in economic and social opportunities. This research spans systems modeling, urban economics, multicriteria decision modeling, stochastic models, and decision support systems and is often interdisciplinary and applied in nature. A common thread in this work is the need to explicitly address the needs of multiple stakeholders, to capture the public and private nature of housing, and to incorporate best‐available evidence regarding markets, policies, and impacts of housing and community development. In this article, we describe the policy context for decision modeling in housing and community development, review previous research through the lens of descriptive, prescriptive, and decision support analysis, and identify important limitations to this research. We then describe diverse opportunities to address current policy concerns such as sustainable development, post‐disaster reconstruction, and individual and group decision support.
Implementing the Simplex Method
Authors: Saltzman Matthew J. 
Published: February 2011

AbstractThe basic simplex method is a simple and straightforward algorithm, but modern efficient implementations represent a tour de force of algorithm engineering and numerical methods designed to exploit opportunities for efficiency wherever they arise. This article surveys the key techniques necessary to implement a robust, efficient simplex code. These techniques support the strategic design principles of minimizing the number of iterations and carrying out the computations in each iteration efficiently and accurately. The primal and dual bounded‐variable simplex algorithms are described. Efficient and robust advanced techniques for finding a starting solution, effective pricing, ratio tests and degeneracy management, and sparse linear algebra are covered.
Impossibility Theorems And Voting Paradoxes In Collective Choice Theory
Authors: Penn Elizabeth Maggie 
Published: February 2011

Abstract
This article provides an introduction to several of the most well known impossibility theorems in voting theory, including Arrow's theorem, May's theorem, Sen's liberal paradox, and the Gibbard–Satterthwaite theorem. It concludes with a discussion of various widely known voting paradoxes, including Condorect's paradox, Ostrogorski's paradox, and the Alabama paradox.
Inequalities from Group Relaxations
Authors: Richard Jean‐Philippe P. 
Published: February 2011

AbstractThis article provides a review of the group‐theoretic approach to the generation of cutting planes in mixed integer programming. After motivating the notion of corner relaxation graphically, we give a definition of master group problems. We then present a hierarchy of valid inequalities for master group problems that can be used as cutting planes for mixed integer programs. We describe next various procedures that can be used to obtain the “strongest” valid inequalities for master group problems. We conclude by commenting on the computational possibilities of group‐theoretic cuts in mixed integer programming.
Two‐Stage Stochastic Programs: Introduction and Basic Properties
Authors: Birge John R. 
Published: February 2011

Abstract
This article introduces the two‐stage form of stochastic programs that describe decision situations in which an action is taken, followed by observation of a random event and subsequent follow‐on action. The article describes the basic formulation of these problems as optimization models and then describes basic properties of solution existence, optimality conditions, and equivalent formulations that exploit problem structure for computational efficiency.
Introduction to Facility Location
Authors: Snyder Lawrence V. 
Published: February 2011

Abstract
This article provides a broad overview of facility location problems, one of the oldest and most widely studied fields of applied optimization. The article presents a standard taxonomy of location models and discusses key dimensions along which they are classified. Mathematical programming formulations and common solution methods for several classical models are described. A variety of extensions that build upon these models and their application to richer and more complex settings are also discussed. The article covers discrete and network location problems and, to a lesser extent, continuous problems.
An Introduction to Linear Programming
Authors: Cochran James J. 
Published: February 2011

Abstract
This article provides an overview of linear programming. Constrained and unconstrained optimization are first explained and contrasted, and the assumptions of linear programming are explained. An example problem is formulated and solved both graphically and by enumeration, and the example is used as the basis for an explanation of the simplex algorithm. Special cases are discussed, and the relationship between primal and dual formulations is explained.
Introduction to Polynomial Time Algorithms forLP
Authors: Terlaky Tamás 
Published: February 2011

AbstractThe first polynomial time algorithm for linear programming (LP), the so‐called ellipsoid method, was proposed by Leonid Khachiyan in 1979. Then the 1984 paper of Narenda Karmarkar launched the age of interior point methods (IPMs). Several variants of the ellipsoid method, and hundreds of polynomial time IPMs were designed in the past decades. Interior point software implementations have challenged simplex method implementations, and frequently surpassed their performance.The goal of this article is to give a brief introduction to polynomial time algorithms for LP.
Introduction To Robust Optimization
Authors: Smith J. Cole , Ahmed Shabbir 
Published: February 2011

Abstract
Robust optimization models are used to address optimization problems with uncertain data. These models enforce the condition that a feasible solution should satisfy all constraints under any possible realization of constraint data. Furthermore, the optimization process should provide a worst‐case guarantee on the quality of the solution, for any possible realization of objective data. Thus, for a minimization problem, the objective minimizes the maximum objective that could result from uncertain data. This article provides an overview of robust optimization models and properties, with an emphasis on robust linear optimization.
Efficient Iterative Combinatorial Auctions
Authors: Mishra Debasis 
Published: February 2011

Abstract
This survey considers the following setting. A seller wants to sell a set of indivisible goods. A set of buyers are interested in buying the goods. Buyers have private values for bundles of goods they are interested in buying. The seller's objective is to allocate the goods to the buyers efficiently (maximizing the total utility of buyers). Since the values of buyers are private information, this creates an incentive problem. Economic theory prescribes the Vickrey–Clarke–Groves (VCG) mechanism to solve this problem. However, the VCG suffers from various drawbacks because of its centralized implementation. An alternative decentralized way of implementing the VCG is the iterative combinatorial auctions. We survey the foundations of iterative combinatorial auctions that implement the VCG mechanism. We also describe a particular iterative combinatorial auction that implements the VCG mechanism.