bluuebunny/crossref_metadata_2025_split huggingface dataset:
Airline Resource Scheduling
Authors: Cohn Amy , Lapp Marcial 
Published: January 2011

AbstractCommercial airlines face many complex decisions, from strategic planning to daily operations. For more than 30 years, the operations research (OR) community has played a critical role in helping to solve many of these challenging decision‐making problems. In this article, we focus on OR approach to resource allocation problems in passenger aviation. We discuss some of the most well‐known problems and their respective solution approaches, review current research in the field, and highlight emerging research areas of future importance.
Asymptotic Behavior of Continuous‐Time Markov Chains
Authors: Tekin Eylem 
Published: January 2011

Abstract
This article summarizes the theoretical foundations for analyzing the asymptotic behavior of continuous‐time Markov chains. We first introduce several concepts for classifying the states of a continuous‐time Markov chain. These concepts include irreducibility, recurrence, and transience. Then, we describe how to compute the limiting distribution of a continuous‐time Markov chain.
Backward and Forward Equations for Diffusion Processes
Authors: Ghosh Arka P. 
Published: January 2011

AbstractThis article is devoted to the discussion of two fundamental (partial) differential equations, which arise in the context of Markov diffusion processes. After giving a brief introduction of continuous‐time continuous‐state Markov processes, we introduce the forward and backward equations, and provide a heuristic derivation of these equations for diffusion processes. We also discuss some examples and features of these two equations.
Bayesian Network Classifiers
Authors: Goldszmidt Moises 
Published: January 2011

AbstractBayesian network classifiers are used extensively both in academia and in a wide range of industrial applications and domains. This article first reviews the main concepts behind statistical pattern classifiers and Bayesian networks, including the main methods for the automated induction of these models. It then continues with a description of the application of Bayesian networks as statistical pattern classifiers. It discusses the advantages of Bayesian network classifiers over other types of classifiers, and provides up‐to‐date references covering the material and examples of applications. Readers with basic knowledge of statistics will acquire the necessary background to explore more technical papers and decide on whether Bayesian network classifiers are the right technology for their particular problem and domain.
Behavioral Economics And Game Theory
Authors: Costa‐Gomes Miguel A. 
Published: January 2011

Abstract
Game theory is a tool used to predict behavior in situations involving several decision makers. However, when people play games their behavior often shows systematic deviations from the theory's predictions. This article provides brief summaries of the different modeling approaches that attempt to account for such deviations. A common theme of these approaches is its close attention to a variety of empirically based phenomena that have been documented by psychologists. The result is a synthesis of theory and empirical evidence that is known as behavioral game theory.
Bilinear Optimization
Authors: Lim Churlzu 
Published: January 2011

Abstract
The bilinear optimization (or bilinear programming) problem is a specially structured quadratic programming problem, where two sets of variables have bilinear relationships. Assuming nonempty and bounded feasible regions, we first present useful properties of this problem, and discuss two commonly used solution approaches as well as local search methods. We also illustrate how the bilinear programming formulation is related to two game‐theoretic problems, bimatrix game and sequential game. Then, detection of unboundedness of bilinear programming problems is discussed when feasible region(s) are unbounded. Finally, brief remarks on generalized formulations of bilinear program conclude this article.
Branch‐and‐Bound Algorithms
Authors: Kianfar Kiavash 
Published: January 2011

Abstract
“Branch‐and‐bound” is the most common approach to solving integer programming and many combinatorial optimization problems. This article provides an overview of the main concepts in branch‐and‐bound and explains how it works. It also discusses the major design questions that arise in implementing branch‐and‐bound, and briefly reviews the most common answers to these questions by referring to the recent literature providing the reader with ample opportunity for further reading.
Branch‐Price‐and‐Cut Algorithms
Authors: Desrosiers Jacques , Lübbecke Marco E. 
Published: January 2011

AbstractIn many mixed integer programs there is some embedded problem structure which can be exploited, often by a decomposition. When the relaxation in each node of a branch‐and‐bound tree is solved by column generation, one speaks of branch‐and‐price. Optionally, cutting planes can be added in order to strengthen the relaxation, and this is calledbranch‐price‐and‐cut. We introduce the common concepts of convexification and discretization to arrive at a Dantzig–Wolfe type reformulation of a mixed integer program. The relation between the original and the extended formulations helps us understand how cutting planes should be formulated and how branching decisions can be taken while keeping the column generation subproblems manageable.
Capacity Allocation in Supply Chain Scheduling
Authors: Hall Nicholas , Liu Zhixin 
Published: January 2011

Abstract
Capacity allocation problems, which arise frequently in manufacturing systems, include under‐allocation, over‐allocation, and misallocation of capacity. Such problems can originate from either the demand or the supply side. Where capacity is time‐sensitive, these problems need to be resolved in tandem with other operational issues such as scheduling. This article discusses capacity allocation decisions that are modeled as cooperative or noncooperative games within research on supply chain scheduling. For cooperative game models, we focus on the existence of fair allocations of savings and/or capacity that ensure the cooperation of all the players. For noncooperative game models, we discuss the efficiency of capacity allocation mechanisms, and describe incentive schemes that attempt to resolve the problem of strategic ordering whereby players introduce false information in order to gain an advantage. We also describe several topics for future research.
Revenue Management
Authors: Gallego Guillermo 
Published: January 2011

AbstractProviders of perishable capacity sold through reservation systems typically impose purchase and usage restrictions to sell the same capacity to different customers at different prices. We will assume that the fare structure (a menu of prices and restrictions) is given and that the capacity provider's goal is to allocate capacity among fare classes to maximize expected revenues. We first analyze the single resource, multiple‐fare case and then investigate the network model under the assumption of independent demands. Independent demand models are appropriate if fares are well differentiated and alternative sources of capacity are available to customers who are unable to find their preferred fare. When fares are not well differentiated, or alternative sources of capacity are not readily available, customers may buy up to the next higher fare if their preferred fare is not available. Demand dependencies can significantly erode revenues when controls are based on independent demand models. Researchers and practitioners have been actively trying to find solutions that take into account demand dependencies through the use of discrete choice models. We present a summary of these works and point out directions for future research.
Use of a High‐FidelityUASSimulation for Design, Testing, Training, and Mission Planning for Operation in Complex Environments
Authors: Barton Jeffrey D. , Cybyk Bohdan , Drewry David G. , Frey Timothy M. , Funk Brian K. , Hawthorne R. Chad , Keane John F. 
Published: January 2011

AbstractToday, both military and civilian forces operate Unmanned Air Systems (UAS) in mountainous, desert, littoral, and urban areas. The environmental features of these natural and man‐made environments (e.g., wind effects coupled with terrain features) can make low‐level flight of small air vehicles extremely challenging. Modeling and simulation (M&S) tools can provide design engineers and analysts with a high‐fidelity representation of UAS flight in these complex environments where wind gusts and thermally and/or geometrically influenced local vortices are naturally present. An open‐architecture modeling toolset, with modular components representing this and other challenges in these types of terrains, such as communications, air vehicle aerodynamic characteristics, autopilots, sensors, and autonomy algorithms, can provide a capability to evaluate performance enhancing technologies (e.g., development of new airframes specifically designed for operations in these chaotic regimes), to conduct realistic operator training, and to perform more accurate mission planning.
Collaborative Procurement
Authors: Erhun Feryal 
Published: January 2011

Abstract
According to a recent US Department of Commerce survey, for every dollar of sales revenue, 50 cents are spent on component procurement in the US manufacturing industry. It is therefore crucial that firms manage their procurement effectively in order to maintain a competitive edge in the market. One way of achieving this goal is to change the nature of intra‐ and inter‐firm relationships from adversarial to collaborative. Through collaboration in procurement, firms may lower their costs, reduce their inventory, increase their sales and revenue, and utilize their resources more efficiently. In this article, we provide a brief overview of collaborative procurement.
Comparisons of Risk Attitudes Across Individuals
Authors: Egan Daniel , Davies Greg B. , Brooks Peter 
Published: January 2011

AbstractIn comparing risk attitudes across individuals we usually aim to determine whether one can reliably measure a difference in risk attitudes between two individuals, the magnitude of that difference, and what factors should be controlled for to ensure comparisons are meaningful. These comparisons critically depend on the method of risk attitude measurement and elicitation used, and a clear understanding of what the modeled risk attitude represents. Individual risk attitudes are fragile and very specific to domain and framing effects, and thus comparisons should always be made within the same elicitation method.Depending on the measurement method, comparisons can be made on relative or absolute scales. In general, the more precise the measurement, the more sensitive it will be to slight changes in assessment. Psychometric measures are the simplest and most robust, but allow only for ordinal comparisons. Certainty equivalent and utility models of choices can give more precise, but noisy, cardinal estimates of individual risk aversion. The purpose and need for precision of the comparison will often drive the method a researcher chooses for eliciting risk attitudes, and advice is given for researchers seeking to design or analyze such studies.
Condition‐Based Maintenance Under Markovian Deterioration
Authors: Çekyay Bora , Özekici Süleyman 
Published: January 2011

AbstractWe focus on condition‐based maintenance models with Markovian deterioration, and review the literature for which control‐limit policies are optimal. These polices are not necessarily optimal in a variety of cases and the optimal policy may have a rather complex structure. We also discuss such models where one often uses special simplified policies depicted by a few thresholds and solved using iterative algorithms. The objective is to discuss a wide range of models on condition‐based maintenance and technical details are omitted.
Conservation Laws and Related Applications
Authors: Niño‐Mora José 
Published: January 2011

AbstractWork conservation is a fundamental invariance principle governing the behavior of a wide variety of queueing systems. In certain queueing models, the work conservation principle yields work conservation laws, which are linear constraints on performance measures that give an exact or relaxed linear programming formulation of the region of achievable performance spanned under admissible scheduling policies. Such laws are useful for exact or approximate performance analyses of such models, as well as for design and computation of optimal or nearly optimal scheduling policies relative to given performance objectives.
Defining Objectives and Criteria for Decision Problems
Authors: Merrick Jason R. W. 
Published: January 2011

Abstract
The first step in analyzing a decision is to define what the decision maker wants to achieve. Generating a broad and comprehensive set of objectives leads to a wider focus in our consideration of alternatives and hence results in better decisions. However, several studies show that decision makers are not good at defining all the objectives that they should consider. This article introduces the concepts that help us understand decision makers' objectives and reviews the literature on the problems encountered in defining objectives and on how to overcome them. Examples from the author's research are used to illustrate the concepts discussed.
Degeneracy And Variable Entering/Exiting Rules
Authors: Maros István 
Published: January 2011

Abstract

Degeneracy is a phenomenon in linear programming (LP) that has important implications if the simplex method (SM) is used to solve an LP problem. It can happen that some of the vertices of the convex polyhedron of the feasible solutions (basic solutions) are not uniquely defined. In such a case the SM method may move from one representation of a vertex to another representation of the same vertex, while the value of the objective function remains unchanged. This can lead to a sequence of nonimproving solutions and a return to the first basis in the sequence. If it happens the sequence is repeated infinitely resulting in cycling and the SM does not terminate. There are, however, various theoretical and computational approaches that can prevent the SM from cycling. Even if they may not be able to overcome a long series of nonimproving iterations, called
stalling
, they still can substantially reduce the number of such iterations. This article gives details of all these phenomena and the indicated remedies.
Drama Theory
Authors: Bryant James 
Published: January 2011

AbstractDrama theory explains how people resolve differences. It overcomes the paradoxes created by attempting to be instrumentally rational in multiparty situations where there is interdependence in the choices made by those involved. It is a positive and a nonquantitative approach: assumptions about participants are taken to be empirical statements and numerical utilities are not used. It contrasts with alternative derivatives of metagame analysis, which seek “solutions” to an interaction within a structure where players' preferences and options are fixed. Drama theory recognises that confrontations are practically resolved by appealing to the wider context within which the “small world” of formal game theoretic models is set. It has been applied to situations involving collaboration or conflict and provides a language for people to represent how they would like matters resolved and what they intend if a solution cannot be found. The pressures that each party faces can systematically be explored, and guidance on the management of the interaction can be developed. Confrontations in international relations, corporate alliances, departmental coworking, and human relations management have all been analyzed using drama theory. Analyses can also be used to create focused role‐play training exercises permitting participants to rehearse new roles or relationships.
Effective Application ofGRASP
Authors: Festa Paola , Resende Mauricio G. C. 
Published: January 2011

AbstractA greedy randomized adaptive search procedure (GRASP) is an iterative multistart metaheuristic for difficult combinatorial optimization. Each GRASP iteration consists of two phases: a construction phase, in which a feasible solution is produced, and a local search phase, in which a local optimum in the neighborhood of the constructed solution is sought. Since 1989, GRASP has been applied to a wide range of combinatorial optimization problems, ranging from scheduling and routing to drawing.In this article, we cover the literature where GRASP is applied to scheduling, routing, logic, partitioning, location, graph theory, assignment, manufacturing, transportation, telecommunications, biology and related fields, automatic drawing, power systems, and VLSI design.
Eliciting Subjective Probabilities from Individuals and Reducing Biases
Authors: Mihajlovits Bethany H. , Merrick Jason R. W. 
Published: January 2011

Abstract

Thinking in terms of probabilities is not easy and we tend to use rather primitive cognitive techniques to make such assessments. These techniques are called
heuristics
. Heuristics are simple and intuitive but tend to result in probabilities that are biased. This article reviews the problems associated with probability elicitation and methods for improving such assessments.
Evacuation Planning
Authors: Regnier Eva 
Published: January 2011

AbstractEvacuation planning encompasses a broad set of decisions that sometimes mean the difference between life and death. The Operations Research (OR) community can bring important strengths to evacuation planning. Some of OR's powerful modeling tools are readily applicable to evacuation decisions. In addition, OR researchers and practitioners have expertise in framing complex, multiobjective public decisions to produce solutions that can be implemented in the real world. This article outlines areas in which OR has contributed and has the potential to contribute, as well as challenges to the application of OR tools and approaches in evacuation planning.
Fast and Frugal Heuristics
Authors: Bertel Sven , Kirlik Alex 
Published: January 2011

AbstractCognitive heuristics are fundamental to human judgment and decision making. Often described as rules of thumb that are used in lieu of optimal procedures (e.g., when resources are limited, or knowledge is incomplete), cognitive heuristics may also be understood as essential psychological mechanisms that guide information search and produce decisions by effectively and efficiently exploiting information structures in the environment. This article describes a selection of simple but powerful, task‐specific, fast and frugal heuristics (FFHs) as suggested by Gigerenzer, Todd, and the ABC Research Group. Underlying assumptions related to concepts of ecological rationality are presented, as are basic mechanisms of, and research into the validity of FFHs.
Finite Population Models—Single Station Queues
Authors: Jayaraman Raja , Matis Timothy I. 
Published: January 2011

AbstractA finite population queueing model consists of service requests generated by finite number of customers handled by either a single or multiple number of servers. There are several practical applications of finite‐source models including the machine–repairman model, multiprocessor systems, time‐sharing systems, telecommunication models, vacation models, reliability models, and so on. In this article, we review several applications of finite‐source models, and analyze the classical machine–repairman model in steady state to discuss related performance measures. Variations to the underlying characteristics and assumptions of this model lead to the development and analysis of a broader class of queueing problems motivated by these applications, for which we present extensions and suggestions for further reading.
Forecasting for Inventory Planning under Correlated Demand
Authors: Baykal‐Gürsoy Melike , Erkip Nesim K. 
Published: January 2011

Abstract
In this article, we review inventory planning approaches in order to find out demand‐forecasting requirements. We consider single‐ and multi‐item supply chains under stationary and nonstationary, correlated demand. We also examine Martingale model of forecast evolution (MMFE), which is a way for modeling forecasts. We then summarize forecasting procedures that will be helpful in realizing the aforementioned inventory models.
Foundations of Simulation Modeling
Authors: Kin Wai , Chan Victor 
Published: January 2011

AbstractSimulation modeling is an important step of a simulation study. It concerns with the creation of an efficient simulation model as accurate as needed to mimic the behavior of the underlying system and to reproduce useful observations for subsequent analysis. This introductory article discusses three main simulation modeling methods: discrete‐event simulation, continuous simulation, and agent‐based simulation. In particular, three discrete‐event simulation modeling paradigms are introduced: event scheduling, process interaction, and activity scanning. Several modeling issues are exemplified by a simple single‐server queueing system and a slightly more complex multiple‐server finite capacity queueing system with impatient customers. Continuous simulation is briefly discussed to draw the differences between discrete‐event and continuous simulations. Simulating emergent behavior as a common purpose of agent‐based simulation is demonstrated by a classic game of life model and a variation model. Monte‐Carlo simulation and other simulation modeling formalisms are briefly highlighted.
Generating Homogeneous Poisson Processes
Authors: Pasupathy Raghu 
Published: January 2011

Abstract
We present an overview of existing methods to generate pseudorandom numbers from a nonhomogeneous Poisson process. We start with various definitions of the nonhomogeneous Poisson process, present theoretical results (sometimes with a proof) that form the basis of existing generation algorithms, and provide algorithm listings. Whenever available, we also provide links to sources containing computer codes. With the intent of guiding users seeking an appropriate algorithm for a given setting, we emphasize computationally burdensome operations within each algorithm. Our treatment includes both one‐dimensional and two‐dimensional Poisson processes.
Genetic Algorithms
Authors: Pelikan Martin 
Published: January 2011

AbstractGenetic algorithms are stochastic optimization methods inspired by natural evolution and genetics. Over the last few decades, genetic algorithms have been successfully applied to many problems of business, engineering, and science. Because of their operational simplicity and wide applicability, genetic algorithms are now playing an increasingly important role in computational optimization and operations research. This article provides an introduction to genetic algorithms as well as numerous pointers for obtaining additional information.
Hellenic Operational Research Society
Authors: Matsatsinis Nikolaos 
Published: January 2011

Abstract
This paper describes the current status, the trends and challenges of the Hellenic Operations Research Society (HELORS). The profile of OR in public and private sectors, the specific role of the country's OR society and the educational opportunities are outlined. Finally, the results of a survey on OR work carried out and presented in Greece during the last 10 years are presented and the challenges that OR will face in the future are discussed.