bluuebunny/crossref_metadata_2025_split huggingface dataset:
Benders Decomposition
Authors: Taşkin Zeki Caner 
Published: June 2010

Abstract
Benders decomposition is a solution method for solving certain large‐scale optimization problems. Instead of considering all decision variables and constraints of a large‐scale problem simultaneously, Benders decomposition partitions the problem into multiple smaller problems. Since computational difficulty of optimization problems increases significantly with the number of variables and constraints, solving these smaller problems iteratively can be more efficient than solving a single large problem. In this article, we first formally describe Benders decomposition. We then briefly describe some extensions and generalizations of Benders decomposition. We conclude our article by illustrating how the decomposition works on a problem encountered in Intensity Modulated Radiation Therapy (IMRT) treatment planning and giving a numerical example.
B
rownian Motion and Queueing Applications
Authors: Yao David D. 
Published: June 2010

Abstract
The objective of this article is to present an elementary introduction to the applications of Brownian motion in queueing theory. We start with an overview of some of the basics of Brownian motion, followed by Donsker's theorem and the Skorohod problem. We then discuss the fluid and diffusion scaling of queueing processes, taking the single‐server GI/GI/1 queue as an example. We illustrate why the limiting regime under the diffusion scaling is a reflected Brownian motion, and how it leads to a diffusion approximation of the original queue model.
Capacity Allocation
Authors: Lariviere Martin A. 
Published: June 2010

AbstractA capacity allocation mechanism allows a supplier to apportion output among downstream buyers when submitted orders exceed available capacity. Some buyers will not receive their full request. How the allocation mechanism determines who is shorted and the extent of the shortfall affects how buyers order. Consequently, the allocation mechanism can have a significant impact on supply‐chain performance. In this article, we first discuss the role allocation mechanisms have had in practice and then review academic research on allocation mechanisms. We consider both, single‐period models in which today's orders determine today's allocations as well as multiperiod models in which today's sales determine tomorrow's allocations.
Computational Methods for
CTMCs
Authors: Dayar Tuğrul , Stewart William J. 
Published: June 2010

Abstract
This article concerns the computation of stationary and transient distributions of continuous‐time Markov chains (CTMCs). Once the problem has been formulated, it is shown how computational methods for computing stationary distributions of discrete‐time Markov chains can be applied in the continuous‐time case. This is not so for the case of transient distributions, which turns out to be a much more difficult problem in general. Different approaches to computing transient distributions of CTMCs are explored, from the simple and efficient uniformization method, through matrix decomposition and powering techniques, to ordinary differential equation (ODE) solvers. This latter approach is the only one currently available for nonhomogeneous CTMCs. The basic concept is explained using simple Euler methods, but formulae for more advanced and efficient single‐step Runge–Kutta and implicit multistep BDF methods are provided.
Cost‐Effectiveness Analysis, Health‐Care Policy, and Operations Research Models
Authors: Zaric Gregory S. 
Published: June 2010

Abstract
Cost‐effectiveness analysis is often used to formally investigate the trade‐offs between the economic costs and the health consequences of new medical technologies. Starting with Ontario and Australia in the early 1990s, many public sector drug plans began requiring cost‐effectiveness estimates for all new drugs prior to their approval for reimbursement. Owing to the difficulties in obtaining cost‐effectiveness estimates directly from clinical trials, many cost‐effectiveness estimates are obtained using decision‐analytic and Monte Carlo simulation models. In this article, we give a brief overview of cost‐effectiveness analysis in health care; we discuss some of the types of models that are commonly used and we give a description of how cost‐effectiveness analysis is used in health policy decision making in three different jurisdictions.
Data Mining in Construction Bidding Policy
Authors: Williams Trefor 
Published: June 2010

AbstractMany construction projects are selected using competitive bidding, where the lowest bidder wins the project. There is often a tendency for projects to cost more than the original bid amount. The data mining techniques of classification and association were used to study patterns in bidding data and to determine if useful patterns do exist that can help predict a projects tendency to have cost overruns. Using highway‐bidding data from Texas highway projects, several different ratios were calculated to describe relationships between the submitted bids such as the spread of the submitted bid and the difference between the low bid and the second lowest bid. Elevated levels of these ratios have been found to be associated with projects that have significant deviations between the original low bid amount and the completed project cost. The tertius and predictivea prioriassociation algorithms, and the Ridor classification algorithm were applied to the data set to determine if useful rules about a projects tendency to have cost overruns could be determined.
Discrete‐Time Martingales
Authors: Lin Kyle Y. 
Published: June 2010

Abstract

Discrete‐time martingales are stochastic processes motivated by a sequence of fair bets. If the expected winning of each bet is equal to 0 and their outcomes are independent, then the gambler's expected fortune after the next bet is equal to his current fortune. In this example, the sequence of random variables {
Z
1
,
Z
2
, …}, with
Z

n

denoting the gambler's fortune after
n
bets,
n
= 1, 2, …, is said to be a discrete‐time martingale. This article gives the definition of discrete‐time martingales and provides several examples.
Distributed Simulation inORMS
Authors: Taylor Simon J.E. , Mustafee Navonil 
Published: June 2010

AbstractCommercial off‐the‐shelf (COTS) simulation packages (CSPs) are tools typically used in Operational Research and Management Science for discrete‐event simulation. Distributed simulations using these can be widely beneficial. These require CSP interoperability, that is, a set of techniques that facilitate the execution of a distributed simulation over several computers or for several simulations running on different computers to run together. This article introduces reasons for using distributed simulation and CSP interoperability. A set of guiding techniques used to simplify their realization is then presented. A case study of a health‐care application is used to demonstrate how these are used in practice.
Emergency Medical Service Systems that Improve Patient Survivability
Authors: McLay Laura A. 
Published: June 2010

AbstractThe goal of emergency medical service (EMS) systems is to save lives. Although EMS systems traditionally deploy their units at fixed locations (i.e., “stations”), this is not necessarily the optimal way to respond when resources are limited. This article summarizes the key issues in defining performance measures, utilizing scarce emergency medical resources, and assessing EMS performance when applying operations research methodologies to EMS systems and explicitly focusing on patient outcomes. This article overviews standards currently used by many EMS systems and proposes several performance measures to use in operations research models for EMS systems. Several areas within EMS system design that could benefit from the application of operations research methodologies are described.
Estimating Failure Rates and Hazard Functions
Authors: Giorgio Massimiliano , Pulcini Gianpaolo 
Published: June 2010

AbstractThis article illustrates a number of classical nonparametric and parametric procedures for estimating the failure rate (often called the hazard function) and the cumulative failure rate of non repairable units. Both point and interval estimation procedures are discussed and some graphical methods are briefly presented. A specific attention is devoted to two distribution functions frequently used in the reliability analysis: the standard exponential and the two‐parameter Weibull distributions. Finally, some numerical examples are given to illustrate the estimation procedures.
Hazard Rate Function
Authors: Upadhyay Satyanshu Kumar 
Published: June 2010

AbstractHazard rate function is an important concept for researchers and practitioners working in areas such as engineering statistics, and biomedical sciences. Hazard rate in fact provides an alternative characterization for the distribution of a random variable, especially when dealing with the lifetime data and it is quite useful in defining and formulating a model. The present article begins with a discussion on how the notion of hazard rate function enters into lifetime data analysis, and provides the general concept and definition of the hazard function for both continuous and discrete variates. The article finally focuses on a few typical shapes of the hazard rate function and comments briefly on some important models that can be useful for the considered shapes. Additional list of references is provided at the end for supplemental reading.
H
olt–
W
inters Exponential Smoothing
Authors: Pan Rong 
Published: June 2010

Abstract
The Holt–Winters exponential smoothing method was first introduced more than half a century ago for the seasonal time series forecast and it is still one of the most popular forecasting systems widely used in production planning, inventory control, and many other application areas. Numerous empirical studies have confirmed the accuracy and robustness of this simple method, compared to many other sophisticated forecasting techniques. Recently, the underlying optimal time series models of this heuristic method have been found and they provide the theoretical foundation for obtaining the statistical properties of this method. This article aims to provide a succinct summary of the basic Holt–Winters method and help readers understand and appreciate this simple, yet powerful, forecasting technique.
k‐out‐of‐nSystems
Authors: Zuo Ming J. , Tian Zhigang 
Published: June 2010

AbstractIn this article, we focus on thek‐out‐of‐nsystem structure. This structure is a widely used type of redundancy in both industrial and military systems. In the binaryk‐out‐of‐nsystem models, it is assumed that the system and the components can take only two possible states, completely working or totally failed. In the multistatek‐out‐of‐nsystem and the weighted multistatek‐out‐of‐nsystem models, it is assumed that the system and the components can take multiple discrete states. For all thesek‐out‐of‐nsystem models, algorithms for evaluation of the system state distribution are investigated given component state distributions are investigated.
Large Margin Rule‐Based Classifiers
Authors: Bonates Tibérius O. 
Published: June 2010

AbstractWe introduce an optimization approach for the construction of large margin rule‐based classifiers. We base our description on the Logical Analysis of Data (LAD) methodology, but the same approach can be applied to different rule‐based classification algorithms. The novelty in our algorithm relies on the fact that it unifies the distinct tasks of pattern (rule) generation and creation of a so‐calleddiscriminant functionfor classification. Moreover, the algorithm has a single parameter, thus, significantly reducing the necessity for parameter calibration when learning a new data set. We investigate how accurate the LAD classification models built with our algorithm are and how they compare to other machine learning models.
Load‐Sharing Systems
Authors: Dewan Isha , Naik‐Nimbalkar Uttara V. 
Published: June 2010

AbstractLoad‐sharing systems arise in several real‐life situations. Some of them have been described below. Several models proposed in the literature for load‐sharing systems are studied. The emphasis is on parametric estimation and testing and nonparametric estimation of system life distributions under equal and monotone load‐sharing rules. Data sets that describe load‐sharing systems have been described. A few probabilistic results are stated for completeness.
An Overview of Operations Research in Tennis
Authors: Pollard Geoff , Meyer Denny 
Published: June 2010

AbstractFor nearly 100 years, the equipment and the rules of tennis hardly changed. But since the opening of the game to professionals as well as amateurs in 1968, and the expansion of the game worldwide and in the media, scientific and technological developments have created both problems and opportunities for tennis. This article looks at the use and opportunities for operations research and management science methods to analyze the unique tennis scoring system and the optimal strategies and techniques players might employ. It also considers the use of these methods for the optimization of tennis equipment, including court surface and line calling, and coaching while minimizing medical risk. Efficient tournament management and the accuracy of player ranking are also addressed. This article describes how modern developments allow a scientific approach to measuring and categorizing while suggesting that it may be necessary to limit developments in order to maintain the character of the game.
Parallel Discrete‐Event Simulation
Authors: Liu Jason 
Published: June 2010

AbstractParallel discrete‐event simulation (PDES), sometimes simply referred to asparallel simulation, is concerned with the execution of discrete‐event simulation on parallel computers. PDES has been recognized as a challenging field of research bridging modeling and simulation, and high‐performance computing. By exploiting the potential parallelism in a simulation model, PDES can overcome the limitations imposed by sequential simulation, both in the execution time and the memory space. It has been demonstrated to be a viable technique for solving large‐scale complex models. In this article, we provide a brief overview of the current state of PDES, identify its fundamental challenges, and discuss existing principal solutions resulting from three decades of intensive research in this field. Further, we report specific research advances in high‐performance modeling and simulation of large‐scale computer networks as an exemplar of typical PDES applications.
Point and Interval Availability
Authors: Aven Terje 
Published: June 2010

AbstractConsider a system that is repaired or replaced at failures. The problem is to analyze the availability of the system, at a fixed point in time or for an interval [u,v]. This article presents models and methods for analyzing the availability, both for one‐unit systems and more complex systems comprising several components (monotone systems). Exact formulae as well as approximation approaches based on asymptotic results are covered.
Reduction of aPOMDPto anMDP
Authors: Sandikçi Burhaneddin 
Published: June 2010

AbstractA partially observable Markov decision process (POMDP) is an appropriate mathematical modeling tool for dynamic stochastic systems where portions or all of the system states are not completely observable to the decision maker. In this respect, POMDPs generalize completely observable Markov decision processes (MDPs) by allowing infinitely many states to address partial observability. However, the resulting models suffer tremendously from computational intractability even for relatively small problems. Therefore, POMDPs are frequently approximated by solving variants of completely observable MDPs defined on a grid of finite states. This article summarizes the relationships between completely and partially observable MDPs and derives inequalities for the POMDP value function using the optimal value function of the grid‐based MDPs.
Remanufacturing
Authors: Souza Gilvan C. 
Published: June 2010

Abstract
In this article, we provide a general introduction to remanufacturing and closed‐loop supply chains. Closed‐loop supply chains are those where there are flow of used products (“returns”) from consumers back to manufacturers and suppliers. A key disposition alternative for returns is remanufacturing: the process of restoring used products to a common aesthetic and functional standard. We first introduce the terminology: types of returns (end‐of‐life, end‐of‐use, consumer returns), and disposition decisions for returns (incineration, recycling, sell as‐is, parts harvesting, remanufacturing). We distinguish a key unique characteristic of remanufacturing: in contrast to manufacturing, the inputs—product returns—in remanufacturing are variable with respect to quality, quantity, and timing. We then show how operations research can be used to help the remanufacturing firm price its products (relative to comparable new products), and plan its production, considering the variable input stream.
Scoring Rules
Authors: Winkler Robert L. , Jose Victor Richmond R. 
Published: June 2010

AbstractProbabilities are used to quantify uncertainty in operations research and management science modeling. Scoring rules provide a numerical measure (a score or reward) based on probabilities for an event or variable and on what actually occurs. As such, they are valuable in evaluating how “good” probabilities are in light of what happens, and they also provide incentives for careful elicitation of probabilities. We discuss basic properties of scoring rules, present families of scoring rules that are useful under various conditions, and discuss some related issues.
Simplifying and Solving Decision Problems by Stochastic Dominance Relations
Authors: Eeckhoudt Louis , Schlesinger Harris , Tsetlin Ilia 
Published: June 2010

Abstract
Stochastic dominance is a formal statistical property that partially orders probability distributions. If two alternatives can be ordered by the stochastic dominance criterion, then all individuals whose utility functions satisfy a few easy‐to‐verify criteria will unanimously prefer the dominant alternative. We review several extant characterizations of stochastic dominance, including a recent result that shows how stochastic dominance can be linked to a preference for combining “good” and “bad” outcomes. In particular, if a decision context is about allocating pairs of alternatives in two different states, then lotteries between two “good‐bad” pairs dominate lotteries that yield both “bad” alternatives in one state versus both “good” alternatives in the other state. We explain this dominance relationship and show how it can be used in solving several decision problems involving choice under risk. We also show how individual preference for always pairing such good with bad alternatives implies utility that is a weighted average of exponential functions, which can each be characterized as exhibiting constant absolute risk aversion (CARA). Thus, if preference for one alternative over the others holds under CARA with a wide range of risk aversion coefficients, the decision can be determined with only a limited amount of information about the decision maker's particular utility function.
Sociedad Peruana De Investigación Operativa
Y
De Sistemas (
SOPIOS
)
Authors: Delgadillo Rosa , Mauricio David 
Published: June 2010

Abstract
In this article the History of Peruvian Society of Operations Research and Systems (Sociedad Peruana de Investigación Operativa y de Sistemas (SOPIOS)) is presented. It details the antecedents and the creation of SOPIOS, as well as its objectives, the activities it realizes, and the achievements reached to date and the perspectives of society.
Stakeholder Participation
Authors: Heath Robert L. 
Published: June 2010

Abstract
As a substantial and growing body of research, theory, and best practices, stakeholder participation addresses the structures, functions, and meaning constructions that shape ethical and effective stakeholder relations as a point of orientation for effective management and operations. The logic centers on the need organizations have for resource others hold. It features the willingness and ability of those others to yield (and seek reciprocation) stakes in mutually beneficial exchange as the rationale for marketplace and public policy negotiations. This analysis leads to enlightened and reflective management styles and collaborative decision making.
Stochastic Optimal Control Formulations of Decision Problems
Authors: Srivastava Kunal , Stipanović Dušan M. 
Published: June 2010

Abstract
In this article, we give a brief overview of different stochastic optimal control problems that arise in decision problems. An informal derivation of the Hamilton–Jacobi–Bellman equation, which characterizes the optimal policy, is derived for the case of full state infinite horizon optimal control problem. We then discuss the optimal control problem where the horizon is governed by a random stopping time. An example illustrating the application of the theory to a portfolio selection problem is provided. After a brief discussion on ergodic and risk sensitive control formulations, we move to the case when the state is partially observed. We then state the key separation result in this case and provide some intuitive explanation.
Supplier Selection
Authors: Beil Damian R. 
Published: June 2010

Abstract
Supplier selection is the process by which firms identify, evaluate, and contract with suppliers. The supplier selection process deploys a tremendous amount of a firm's financial resources. In return, firms expect significant benefits from contracting with suppliers offering high value. This article describes the typical steps of supplier selection processes: identifying suppliers, soliciting information from suppliers, setting contract terms, negotiating with suppliers, and evaluating suppliers. It highlights why each step is important, how the steps are interrelated, and how the resulting complexity provides fertile ground for ORMS research.
Take‐Back Legislation and Its Impact on Closed‐Loop Supply Chains
Authors: Atasu Atalay , Boyaci Tamer 
Published: June 2010

AbstractIn this article, we introduce and exemplify existing legislation that regulates the end‐of‐life processing of used products and discuss potential implications of such legislation on closed‐loop supply chains (CLSCs). Using a generic CLSC model, we provide our perspective on how such legislation in general influences different stakeholders in CLSCs.
Using Queueing Theory to Alleviate Emergency Department Overcrowding
Authors: Green Linda V. 
Published: June 2010

Abstract
Emergency Department (ED) overcrowding is a growing problem in the United States and many other countries. Though sometimes dismissed as a minor issue caused by an increase in those who use the ED for routine medical care, this masks the potentially fatal consequences for seriously ill patients who do not receive timely care. This article will illustrate how problems in emergency responsiveness are often due to capacity management policies used by hospital managers and government officials which are based on an erroneous understanding of service system dynamics. Examples will show how the use of queueing models can help identify root causes of ED overcrowding and identify better policies for resource allocation and management that will result in significant improvements, often with little or no additional capacity.