bluuebunny/crossref_metadata_2025_split huggingface dataset:
Batch Markovian Arrival Processes (BMAP)
Authors: Cordeiro James D. , Kharoufeh Jeffrey P. 
Published: January 2011

AbstractThis article describes the batch Markovian arrival process (BMAP), a point process that is characterized by Markov‐modulated batch arrivals of random size. The BMAP is a generalization of many well‐known processes including the Markovian arrival process (MAP), the Poisson process, and the Markov‐modulated Poisson process. It provides a common framework for modeling arrival processes in a variety of applications. We formally define the continuous‐ and discrete‐time BMAP, review a few basic results for each, and show how these processes generalize many common point processes. Additionally, we provide suggestions for further reading on the subject.
Behavioral Operations: Applications in Supply Chain Management
Authors: Donohue Karen , Siemsen Enno 
Published: January 2011

AbstractBehavioral operations is a field within operations management that studies human behavior in the context of operations. The discipline uses a variety of research methodologies including behavioral modeling, controlled experiments, and field studies to identify and understand relevant aspects of human behavior in operational contexts. This article gives a brief overview of behavioral operations and its recent applications in the subfield of supply chain management.
Branch and Cut
Authors: Mitchell John E. 
Published: January 2011

AbstractThe performance of branch‐and‐bound methods for integer programming problems has been dramatically improved by incorporating cutting planes. The resulting technique is known asbranch‐and‐cut. Cutting planes are inequalities that can be used to improve the linear programming relaxation of an integer programming problem. They are added as required, at the root node and also elsewhere in the tree. Problem‐specific cutting planes have been developed for many classes of problems. Recently, there has been a great interest in improving and refining general classes of cutting planes, including, for example, Gomory cuts. Branch‐and‐cut algorithms can exploit parallel computers and multicore architecture in a natural way. Branch‐and‐cut has also been applied to mixed‐integer nonlinear programming problems.
The Bulgarian Operational Research Society
Authors: Yanev Nicola 
Published: January 2011

Abstract
The article provides information about Bulgarian Operational Research Society (BORS) to interested members of the general public. BORS is registered as a scientific not‐for‐profit association on November 2, 1992 and is a member of the International Federation of Operational Research Societies (IFORS) and the European Association of Operational Research Societies (EURO) since 1994.
B
yelorussian
O
perational
R
esearch
S
ociety (
By
ORS
)
Authors: Gordon Valery S. , Guschinsky Nikolai N. 
Published: January 2011

Abstract
The history, milestones, objectives and main activities of the Byelorussian Operational Research Society (ByORS) are presented. The ByORS is a non‐profit scientific organization which supports application and promotes development of operations research (OR) methods in Republic of Belarus. It unites the experts in the field of operations research, applied mathematics, mathematical economy, and their applications. The ByORS also represents Belarus in the international network of experts in the field of operations research such as the Association of European Operational Research Societies (EURO) and the International Federation of Operational Research Societies (IFORS).
Central Path and Barrier Algorithms for Linear Optimization
Authors: Roos Kees 
Published: January 2011

Abstract
In this article, the main ideas underlying the modern interior‐point methods are presented. We discuss primal, dual, and primal‐dual methods and show that each of these methods can be used to solve a linear optimization problem in polynomial time. It is made clear that the central path plays a crucial role in each of the methods. The described methods require a starting point on or close to the central path. Given such a point, a variant of Newton's method can be used to construct a numerical procedure that generates a sequence of iterates that converges to an optimal solution of the problem.
C
hinese Postman Problem
Authors: Yu Wanyan , Batta Rajan 
Published: January 2011

Abstract
This article describes one of the classical routing problems—Chinese postman problem (CPP). The objective of CPP is to determine a closed walk of minimum length covering each arc on the graph at least once. Five types of CPP based on the properties of graphs are discussed: Undirected CPP, directed CPP, mixed CPP, windy postman problem (WPP), and hierarchical postman problem (HPP). The first two types of problems are polynomially solvable while the other three are proved to be NP‐hard. Available mathematical models and algorithms are summarized for each problem type. This article also presents some variations of the CPP and recent results from the literature.
Maximum Clique, Maximum Independent Set, and Graph Coloring Problems
Authors: Pattillo Jeff , Butenko Sergiy 
Published: January 2011

AbstractThis article introduces the closely related maximum clique, maximum independent set, graph coloring, and minimum clique partitioning problems. The survey includes some of the most important results concerning these problems, including their computational complexity, known bounds, mathematical programming formulations, and exact and heuristic algorithms to solve them.
Combinatorial Auctions
Authors: Porter David , Rassenti Stephen 
Published: January 2011

AbstractThere are many issues that must be faced when designing an auction which involves multiple resources that must be simultaneously allocated when information concerning values for the various possible combined uses and constraints faced by the potential users is decentralized. This article examines various auction formats used to allocate resources in these combinatorial settings. While there is some consensus on the use of iterative combinatorial clock auctions for these types of allocation problems, many specific implementation issues are still open questions.
Communicating Decision Information to the Lay Risk Manager: A Consultant's Perspective
Authors: Brown Rex 
Published: January 2011

Abstract
This article reports the personal reflections of an experienced consultant on how to communicate decision‐related information to risk managers. The aim is to help them to make up their minds on an immediate or repetitive choice, to justify their actions to others, or to gather more information first. The information includes prescriptive decision analyses, factual risk assessments, value judgments, raw data, or personal observations. The communication may use the information alone or combine it with other available information. Also addressed are the role of decider judgment, layman language, macromodels, interactive communication, and assessment firmness. The author's perspective is based on 50 years of consulting to a variety of deciders and on two research projects.
Contributions to Software Reliability withORApplications
Authors: Kapur P.K. , Kumar Ravi , Gupta Anshu 
Published: January 2011

AbstractComputing systems finds application in every sphere of human life. Almost everything we see around us in this world seems to be driven by the computing systems. It has made it necessary for the software developer to lay special emphasis on the quality of the software. Quality in the field of software industry means having a right balance between reliability, development time, and cost. The wealth of research in reliability modeling and applications is remarkable, which confirms its continuing importance as a research topic.Among the various kind of reliability modeling, non‐homogeneous Poisson process (NHPP)‐based software reliability modeling has been a topic of most practical and academic interest. This article emphasizes on highlighting the research in the NHPP‐based software reliability growth modeling (SRGM), development of unified schemes for the same which makes the study of SRGMs and their application on any specific testing process much easier, and operational research applications of SRGMs in two decision‐making problems related to the software development, namely, software release time problem and resource allocation at unit testing level.
Cooperative Games with Transferable Utility
Authors: Wilson Jennifer 
Published: January 2011

Abstract
Here, we introduce the concept of a cooperative game with transferable utility and analyze the relationship between cooperative and noncooperative games. We discuss properties of cooperative games and illustrate them with several classes of well‐known games including market games, cost allocation games, and simple games. We also discuss the assumptions behind games with transferable utility and compare them to games without transferable utility. Finally, we mention briefly a number of solution concepts for cooperative games and summarize recent extensions of cooperative game theory.
Customized Price Responses to Bid Opportunities in Competitive Markets
Authors: Ferguson Mark 
Published: January 2011

Abstract
Traditional price optimization involves the use of historical price/demand data to measure the percentage change in demand for a given percentage change in the product's price. This science is not useful in analyzing many business‐to‐business transactions, however, because, instead of receiving some portion of the total demand for a given price, suppliers are often faced with a winner‐take‐all opportunity bidding opportunity. In such cases, the historical price/demand data consists of the set of bidding opportunities, some possible attributes of the customer requesting for the bid, the price submitted by the supplier, and whether the bid opportunity was won or lost. In this article, we describe how price optimization techniques can be applied to this setting.
Delayed Renewal Processes
Authors: Argon Nilay Tanik 
Published: January 2011

Abstract

One of the underlying assumptions for a renewal process is that the first renewal takes place at time zero. There may be several applications where this assumption is not satisfied by a stochastic process that would otherwise be a renewal process. In this article, the properties of such stochastic processes, also called
delayed renewal processes
are discussed and it is observed that these processes possess almost all the properties of standard renewal processes.
Dual Simplex
Authors: Banciu Mihai 
Published: January 2011

Abstract
The dual simplex algorithm is an attractive alternative method for solving linear programming problems. Since the addition of new constraints to a problem typically breaks primal feasibility but not dual feasibility, the dual simplex can be deployed for rapid reoptimization, without the need of finding new primal basic feasible solutions. This is especially useful in integer programming, where the use of cutting plane techniques require the introduction of new constraints at various stages of the branch‐and‐bound/cut/price algorithms. In this article, we give a detailed synopsis of the dual simplex method, including its history and relationship to the primal simplex algorithm, as well as its properties, implementation challenges, and applications.
Dynamic Auctions
Authors: Bergemann Dirk , Said Maher 
Published: January 2011

AbstractWe survey the recent literature on designing auctions and mechanisms for dynamic settings. We distinguish between two broad class of settings: those with a dynamic population of agents, whose private information remains fixed throughout time; and those with a fixed population of agents, whose private information changes across time. Within each of these settings, we discuss both efficient (welfare‐maximizing) and optimal (revenue‐maximizing) mechanisms.
Dynamic Pricing Under Consumer Reference‐Price Effects
Authors: Arslan Hasan , Kachani Soulaymane 
Published: January 2011

AbstractIn this article, we explore the current‐state of literature on how dynamic pricing models incorporate consumer reference‐price effects in developing more informed dynamic pricing strategies for products that have repeated consumer interactions. We first examine the literature on how consumer demand is impacted by reference‐price effects and how consumers form reference prices through their purchase experience. We then explore, in detail, the dynamic pricing models with consumer reference‐price effects in major studies, in order to highlight their key findings and insights. We conclude our review by pointing out research gaps and future research directions on dynamic pricing area in the presence of consumer reference‐price effects.
Dynamic Vehicle Routing
Authors: Thomas Barrett W. 
Published: January 2011

AbstractThis article provides an introduction to dynamic vehicle routing. Dynamic vehicle routing has grown in importance in the last decade as computing and mobile information technology have made it possible for companies to improve routing through real‐time information. Despite these advances, dynamic routing still poses significant computational challenges. This article focuses on the methods that researches have devised to overcome these computational challenges. Three common solution structures are identified: a priori, insertion, and unrestricted. Solutions methodologies are also distinguished by whether or not they incorporate information about future events.
Forecasting: State‐Space Models and Kalman Filter Estimation
Authors: Gürsoy Kemal , Baykal‐Gürsoy Melike 
Published: January 2011

AbstractWe present continuous and discrete time linear state‐space models and explain the Kalman filter algorithm that is used to obtain the one‐step‐ahead state estimations recursively. Adaptive and nonlinear state estimations, as well as parameter estimation, with a Kalman filter are also briefly described.
Foundations of Decision Theory
Authors: Cox Louis Anthony 
Published: January 2011

AbstractAlthough many specific normative decision models and techniques for improving or optimizing decisions have been proposed, most decision‐analytic methods rest on a simple, common conceptual foundation. The basic elements are aset of possible alternativesthat the decision maker must choose among; aset of statesthat may also affect the consequences of the choice; and aconsequence setof different, nonindifferent, possible outcomes that the decision maker cares about. Decision analysis helps the decision maker use beliefs about states and preferences for consequences to identify preferred choices or decisions in the opportunity set. This article discusses these foundations and how they can be applied and extended to support difficult decisions.
Instance Formats for Mathematical Optimization Models
Authors: Gassmann Horand , Ma Jun , Martin Kipp 
Published: January 2011

Abstract
In order to use a piece of software—optimization solver, simulation package, model analyzer, and so on—on a mathematical model, it is necessary to communicate a model instance to the software. Additionally, such software typically accepts options to specify tolerances, set algorithmic parameters, and so on. Finally, the software must communicate the result instance back to the user. In this article we focus on optimization solvers and review existing formats for optimization instances, solver option instances and result instances, and describe the research issues involved in designing these instance formats.
Campaign Analysis: An Introductory Review
Authors: Kline Jeffrey , Hughes Wayne , Otte Douglas 
Published: January 2011

Abstract
Using analytical methods to derive insights into military operational planning is as old as the 2500 year writings of Sun Tzu. Broad in nature, campaign analysis is a field of application, rather than a focused academic discipline. In this introductory review we define and introduce a process for campaign analysis, illustrate where analysis has been informative in past campaigns, discuss how it is used today, and how it is evolving for future military planners. We summarize by highlighting that although campaign analysis uses tools from operations research, systems analysis, and the social sciences, its value is judged solely on its ability to provide the commander with timely, useful, and measurable information to guide decisions. It can supplement, but never replace military judgment.
Just‐In‐Time/Lean Production Systems
Authors: Benton W.C. 
Published: January 2011

Abstract
Just‐In‐Time (JIT) production systems have been successfully implemented in numerous companies throughout the world. JIT manufacturing is a philosophy based on eliminating waste, reducing manufacturing cost, and promoting total quality control. The primary objective of JIT is to produce the right quantity of product in the right place at the right time, while maintaining minimal work‐in‐process and minimal stock of completed work. In JIT production systems, little or no finished product is kept in stock. Rather than producing goods and supplying customers from stock, JIT production systems focus on producing exactly the amount required at exactly the time the customer requires the product.
JIT is also referred to as lean production. An organization driven by a JIT philosophy can improve profits and return on investment by reducing inventory levels, reducing variability, improving product quality, reducing production and delivery lead times, and reducing setup costs. With JIT (Lean) the entire manufacturing system, from purchasing to shop floor management, can be measured and controlled. Therefore, the JIT (Lean) system is a powerful management tool that could easily determine the success or failure of the manufacturing system.
M
arkov Regenerative Processes
Authors: Serin Yasemin 
Published: January 2011

Abstract
Markov regenerative processes are continuous‐time stochastic processes with more general conditions of regeneration than regenerative processes. The limiting behavior of some queues with general distributions can be obtained using Markov regenerative argument. In this article, after some basic definitions, some results related to the transient and limiting properties of the Markov regenerative processes are given. Some results are demonstrated with examples.
Mathematical Programming Approaches to the Traveling Salesman Problem
Authors: Letchford Adam N. , Lodi Andrea 
Published: January 2011

AbstractTheTraveling Salesman Problemor TSP is a fundamental and well‐known problem in combinatorial optimization. At present, the most successful algorithms for solving large‐scale instances of the TSP to proven (near‐)optimality are based on integer programming. This entry introduces the main theoretical and algorithmic tools involved. Topics covered include: formulations of the TSP, polyhedral theory, separation routines, and exact TSP algorithms.
MDPBasic Components
Authors: Patrick Jonathan 
Published: January 2011

AbstractThis article outlines the basic components of any Markov decision process (MDP) model. The purpose is to outline the process of turning a generic sequential decision problem into a mathematical model that can be used to find an optimal policy. The five components of any MDP model are presented as well as the definition of decision rules and policies that are the output of an MDP. The potential goals or objectives in an MDP are discussed but the details of determining the optimal set of decisions are developed in other articles. This article is essential for understanding the optimization tools outlined in the rest of the articles on MDP in the encyclopedia.
Direct Search Methods
Authors: Lewis Robert Michael , Torczon Virginia 
Published: January 2011

AbstractDirect search methods are a class of optimization techniques that do not make explicit use of derivatives. Instead, they workdirectlywith values of the objective function to drive the search for an optimal value. Direct search methods were first formally proposed in the late 1950s and early 1960s and have remained popular with users due to their simplicity and their practical success on a wide range of problems. In recent years, direct search methods have received renewed interest due to new mathematical analysis, their suitability for parallel and distributed computing, and their utility in addressing optimization problems that involve complex computer simulations.
Heuristics and Their Use in Military Modeling
Authors: Hill Raymond R. , Pohl Edward A. 
Published: January 2011

AbstractThe military establishment uses many types of models during the planning and execution of military operations. Optimization models have always been an important class of models. However, as military systems, the scenarios in which those military systems are employed, and the impact of operations involving those military systems become more complex, classic optimization techniques based on mathematical programming can become impractical. Advances in computing capabilities have helped advance the use of heuristics to quickly find reasonable solutions to complex optimization problems. This article provides an overview of the more popular and powerful modern heuristics, most of which have analogies to natural systems. We also provide a representative survey of how these heuristics have been applied to specific classes of military optimization applications.
MonteCarlo Simulation for Quantitative Health Risk Analysis
Authors: Cullen Alison 
Published: January 2011

AbstractManaging risky opportunities, scenarios, and decisions are the most interesting and challenging aspects of human life. But meaningful characterization of risks is elusive because it relies on information that is incomplete, imperfect, and/or variable across time, space, and populations. And, consequently, informed decision making may be compromised. Probabilistic tools such as Monte Carlo simulation have evolved by which uncertainty and variability are characterized and carried through a process of generating probabilistic estimates of risk. The goal of this article is to introduce basic tools and techniques, and some of the more common extensions practiced in the field.
Neuroeconomics Insights for Decision Analysis
Authors: Hytönen Kaisa , Sanfey Alan G. 
Published: January 2011

AbstractNeuroeconomics is a developing field that utilizes methodologies from neuroscience, psychology and economics to study the brain networks that are activated during decision making. In this chapter, we outline the primary methods used by neuroeconomics and examine how this field can help build better models of decision‐making. First, we review research related to valuation processes and biases that occur in the brain, and discuss the properties of the human reward circuitry which reacts to both the anticipation and receipt of financial and hedonic rewards. Next, we discuss the balance between emotional and cognitive control areas in the brain, which have a central role in behavioral framing effects. We conclude with a discussion of how a better understanding of the brain processes can increase our knowledge of why decision‐making in the real world often strays quite far from the predictions made by standard utility maximization accounts.
The Operational Research Society
Authors: Sharp Graham 
Published: January 2011

Abstract
Formed as The OR Club in 1947, The Operational Research Society (The OR Society) was the world's first professional body to cater to the OR profession. The OR Society continues to be an innovator and leader by offering members a range of publications and journals as well as a comprehensive training programme, special interest groups and regional societies. The Society organises the largest national OR conference outside the US in addition to a YoungOR conference for people with up to ten years experience in OR.