bluuebunny/crossref_metadata_2025_split huggingface dataset:
Probabilistic Distance Clustering
Authors: Iyigun Cem 
Published: February 2011

AbstractProbabilistic distance clustering is an iterative method for probabilistic clustering of data. Given clusters, theircenters, and thedistancesof data points from these centers, theprobabilityof cluster membership at any point is assumed to be inversely proportional to the distance from (the center of) the cluster in question. This assumption is the workingprinciple.The method is a generalization, to several centers, of theWeiszfeld methodfor solving the Fermat–Weber location problem. At each iteration, the distances (Euclidean, Mahalanobis, etc.) from the cluster centers are computed for all data points, and the centers are updated as convex combinations of these points, with weights determined by the above principle. Computations stop when the centers stop moving.Progress is monitored by thejoint distance function, a measure of distance from all cluster centers, which evolves during the iterations and captures the data in its low contours.The method is simple, fast (requiring a small number of cheap iterations), and insensitive to outliers.
An Introduction to Probabilistic Risk Analysis for Engineered Systems
Authors: Paté‐Cornell Elisabeth 
Published: February 2011

AbstractThe risk of failure of a system can sometimes be simply characterized by the probability of system failure per time unit or operation. More often, it include both the probabilities and the consequences of the different failure scenarios, in which case the risk can be described by the probability distribution of the losses in a given time frame. Risk management, in turn, involves finding and fixing a system's weaknesses by asking the following questions. How does it work? How can it fail? What can be done about it given that we are not infinitely rich and time is limited? For a complex engineered system, one may not have a sufficient statistical database to assess a failure probability at the system level. In that case, one can use systems analysis and Bayesian probability to assess the risk. Probabilistic risk analysis (PRA) has been developed in engineering, in particular in the nuclear power industry to quantify the risk of system failures. The method relies in part on event trees and fault trees, or, similarly, on Bayesian networks. It involves first, identification of failure scenarios, then computation of their probabilities by combining the probabilities of events and the distributions of random variables involved, accounting for dependencies and rare occurrences. The next step is the evalution of the consequences of the failure scenarios. The results can then be used as input into a risk management problem with two objectives: to optimize resource allocation (e.g., to minimize the probability of system failure given budget and system failure given budget and schedule constraints) and to check that in the end the failure risk is tolerable. This article describes and illustrates the PRA method and some of its extensions.
Random Variate Generation
Authors: Leemis Lawrence M. 
Published: February 2011

AbstractThis article outlines (i) techniques for the generation of random variates using density‐based and hazard‐based methods, (ii) techniques for the generation of certain stochastic point processes that are useful in stochastic modeling, and (iii) further reading in the area of generation of random objects. The accurate modeling of failure time distributions is critical for the development of valid Monte Carlo and discrete‐event simulation models. Once an accurate model has been established, it is oftentimes the case that the complexity of the model requires an analysis by simulation. The associated variate generation algorithms for common stochastic models are introduced in this article.
Recycling
Authors: Akçali Elif 
Published: February 2011

Abstract
With changes in consumption patterns, complex industrial products (e.g., automobiles, cellular phones, and computers) are beginning to account for an increased fraction of the solid waste generated. Hence, the scope of the recycling industry, which was limited to simple products that are made of steel, glass, and some types of plastics in the past, has been expanding to include the recycling of such complex industrial products. With the expanding scope and size of the recycling industry, operations research (OR) tools and techniques are increasingly being utilized for the optimization of recycling operations and the underlying reverse supply chain (RSC) systems.
This article begins by highlighting some of the practical issues encountered in recycling practice that create unique modeling challenges for the design and operation of underlying RSC systems. Then, a brief review of the related OR literature on important planning and control problems that are encountered in the context of RSC systems is presented. By focusing on a pricing problem that arises in the context of end‐of‐life vehicle recycling, the article demonstrates how OR models can be used to assess the economic and environmental efficiency of RSC systems for recycling and to provide support for decision making.
Reflected
B
rownian Motion
Authors: Dieker A. B. 
Published: February 2011

Abstract

Reflected Brownian motion is a continuous‐time continuous‐state Markov process, which is constrained to its finite‐dimensional state space
S
by a pushing mechanism on the boundary ∂
S
of
S
. In the context of Operations Research and Management Science, the state space
S
is typically the nonnegative orthant. This article reviews several aspects of reflected Brownian motion on this orthant: its construction, its positive recurrence, and its stationary distribution.
Reformulation‐Linearization Technique for
MIPs
Authors: Sherali Hanif D. 
Published: February 2011

Abstract
The reformulation‐linearization technique (RLT) for mixed‐integer programs is an automatic model enhancement approach that generates a hierarchy of relaxations spanning the spectrum from the continuous linear programming (LP) relaxation to the convex hull of feasible solutions. This process is applicable to both 0‐1 as well as general discrete programs, and can be further augmented through the use of classes of semidefinite cuts. Often, the first‐level relaxation itself provides a sufficiently tight model reformulation that significantly improves problem solvability. The RLT procedure also offers a unifying framework for solving continuous nonconvex factorable optimization problems to global optimality.
Reinforcement Learning Algorithms forMDPs
Authors: Szepesvári Csaba 
Published: February 2011

AbstractReinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long‐term objective. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. In this article we focus on a few selected algorithms of reinforcement learning which build on the powerful theory of dynamic programming.
Relationship among
B
enders,
D
antzig–
W
olfe, and
L
agrangian Optimization
Authors: Lim Churlzu 
Published: February 2011

Abstract
In this article, we present the equivalence of Benders, Dantzig–Wolfe, and Lagrangian optimization methods for solving linear programs. In particular, we illustrate that applying Dantzig–Wolfe decomposition for solving a linear program is equivalent to employing Benders decomposition to solve its dual linear program, which is in turn equivalent to implementing a cutting plane method to solve its Lagrangian dual problem. We first demonstrate this equivalence when solving a simply structured linear program having a bounded feasible region, and then extend the results to more general cases such as an unbounded feasible region and block diagonal structure.
Retrial Queues
Authors: Artalejo Jesús R. 
Published: February 2011

Abstract

This article surveys the main results and methods for retrial queueing systems. Since the closed‐form results are reduced to a few particular models, this article distinguish between explicit analytical methods and approximate and computational solutions. Although the main focus on the
M
/
M
/
c
and
M
/
G
/1 retrial queues, many other retrial models are also presented. Emphasis is on the limiting distribution of the system state and the waiting time, but other interesting performance descriptors are also discussed.
Revenue Management with Incomplete Demand Information
Authors: Araman Victor F. , Caldentey René 
Published: February 2011

AbstractConsider a seller who is endowed with a fixed number of units of a product that he/she can sell to a price‐sensitive and stochastically arriving stream of consumers during a finite time horizon. The seller has incomplete demand information, that is, there are some characteristics of the demand process (e.g., the arrival rate or the price elasticity) that he/she does not know with certainty. The seller's problem is to dynamically adjust the product's price to maximize the expected revenues he/she can collect if no replenishment is possible during the selling season.
Selective Support Vector Machines
Authors: Seref Onur 
Published: February 2011

AbstractSelective support vector machines are a generalization of support vector machine (SVM) classifiers. In the selective SVM classification, there arenmutually exclusive setsXi,i = 1, …,n, of positive and negative labeled pattern vectors of dimensiond, such that all pattern vectorsxi,k,k = 1, …, |Xi| have the same class labelyi ∈ {1, − 1}. Only one pattern vector from each set is to be chosen such that the margin between the selected positive and negative pattern vectors is maximized. This problem can be formulated as a quadratic mixed 0‐1 programming problem, which is a generalization of the standard SVM classification formulation and it is NP‐hard. An alternative convex quadratic programming formulation based on the restricted free slack concept can be used to find good solutions. The alternative formulation provides flexibility to the separating hyperplane and helps identify the pattern vectors with a large margin. This formulation can be used in an iterative elimination or a direct selection method with applications for simulated and real‐life data.
Self‐Dual Embedding Technique for Linear Optimization
Authors: Roos Kees 
Published: February 2011

Abstract

In the article titled

Central Path and Barrier Algorithms for Linear Programs

in this encyclopedia, the main ideas underlying the modern interior‐point methods are presented. In these methods the central path plays a crucial role. To start these methods one needs a starting point on or close to the central path. Given such a point, a variant of Newton's method is used to generate a sequence of iterates that converges to an optimal solution of the problem. In this section, we show how to get into the situation that a starting point on the central path is known. This goes via the construction of an artificial problem whose solution either yields an optimal solution for a given problem, or detects that no such solution exists.
Semidefinite Optimization Applications
Authors: Man–Cho So Anthony 
Published: February 2011

AbstractOwing to its versatile model power and wide applicability, semidefinite programming (SDP) has received a lot of attention of late in many communities. In this article, we will survey some recent applications of SDP, with a particular emphasis on semidefinite relaxations of hard optimization problems. We will also provide pointers to the literature, for further reading.
Sensitivity Analysis of Simulation Models
Authors: Kleijnen Jack P.C. 
Published: February 2011

AbstractThis contribution presents an overview of sensitivity analysis of simulation models, including the estimation of gradients. It covers classic designs and their corresponding metamodels, namely, resolution‐III designs including fractional‐factorial two‐level designs for first‐order polynomial metamodels, resolution‐IV and resolution‐V designs for metamodels augmented with two‐factor interactions, and designs for second‐degree polynomial metamodels including central composite designs. It also reviews factor screening for simulation models with very many factors, focusing on the so‐calledsequential bifurcationmethod. Furthermore, it reviews Kriging metamodels and their designs. It points out that sensitivity analysis may also aim at the optimization of the simulated system, allowing multiple random simulation outputs.
Service Outsourcing
Authors: Zhou Yong‐Pin , Ren Zhong Justin 
Published: February 2011

Abstract
This article reviews the operations management (OM) research on service outsourcing, a common practice among current businesses. We focus on recent literature in three areas: capacity planning and supplier coordination, service outsourcing under information asymmetry, and quality concerns. Additionally, a mathematical framework is presented that can be used to analyze service outsourcing supply chains. We conclude with discussions on some promising future research areas.
Shipper And Carrier Collaboration
Authors: Ergun Özlem , Gui Luyi , Houghtalen Lori , Özener Okan Örsan 
Published: February 2011

AbstractCollaboration among shippers and carriers in the transportation industry facilitates more efficient and cost‐effective business solutions that cannot be accomplished by companies operating alone, due to the economies of scope and scale. This article surveys the problem of designing and managing collaborative initiatives to promote both the overall efficiency and the stability of a transportation alliance. We discuss three management mechanisms, each requiring different levels of central coordination and information sharing among the participating entities, and yielding different levels of guaranteed social efficiency and coalition stability. We show that by taking into account the economies of scope and scale that arise in collaboration, a fair and efficient mechanism with limited central intervention can be designed based on analytical tools, such as those in network optimization theory, cooperative game theory, and algorithmic mechanism design. We provide an overview of the existing literature on the topic and discuss the business implications of the results, as well as point out some future research directions.
Simplex‐BasedLPSolvers
Authors: Maros István 
Published: February 2011

AbstractThe simplex method is widely used to solve linear programming problems in diverse application areas. It is used as a stand‐alone solver and also as the computational engine of the solution of other optimization problems. The emerging problems tend to be large to very large. Practical solution of such problems requires sophisticated implementation of the simplex method. This article gives an account of the most important developments that have enabled the simplex method to become a reliable, efficient, and flexible solver.
Simulated Annealing
Authors: Wierzchoń Sławomir T. 
Published: February 2011

AbstractSimulated annealing (SA) is a popular metaheuristic proposed for finding global minimum of a function possessing many local minima. It was inspired by the annealing technique used in metallurgy. In the article, both the theoretical and implementational aspects of SA are discussed. First, main notions from the field of Markov chains are reviewed and the Metropolis sampling algorithm is precisely described. This algorithm, implementing “annealing” at fixed temperature, is the main building block of the SA algorithm. Variants of SA devoted to combinatorial and continuous optimization problems are discussed. Further modifications based on Lévy distributions and their extensions with so‐calledq‐normal distribution are mentioned. Lastly, the specializations of SA to multiobjective and stochastic optimization are briefly reviewed.
Decision‐Theoretic Foundations of Simulation Optimization
Authors: Frazier Peter I. 
Published: February 2011

AbstractIn simulation optimization, a task appearing frequently in applications, we wish to find a set of inputs to a simulator that causes its output to be maximal in some sense. Within any simulation optimization algorithm, we must make a sequence of decisions about which input to test at each point in time. The problem of making this sequence of decisions so as to discover a near‐optimal point as quickly as possible may be understood within a decision‐theoretic framework. We describe this decision‐theoretic framework in the context of two problems: ranking and selection and Bayesian global optimization. This decision‐theoretic framework provides both a way of better understanding existing algorithms, and a path to developing new algorithms.
Single Machine Scheduling
Authors: Geismar Neil 
Published: February 2011

Abstract
The single machine scheduling problem is foundational to the study of production scheduling in general. This article describes four basic problems and their solution methods: total weighted completion time, maximum of nondecreasing cost functions (which includes the maximum lateness problem and the problem with precedence constraints), weighted number of tardy jobs, and total weighted tardiness. Additional problems considered include those with both earliness and tardiness penalties, and those with release dates and preemptions.
Soccer/World Football
Authors: Brillinger David R. 
Published: February 2011

AbstractThis article constitutes a review of magagement science, operations research, and statistics as they arise in the study of data arising from the world of soccer/football. The data may have been collected during a game, may be summaries after games, or after a season, or after a tournament, or over many years. The main sections of the article include Data Collection and Descriptive Analyses, Stochastic Modeling (with subsections Between Game Modeling and Within Game Modeling), Ranking, Tournaments and Scheduling, Game Theory, and Economics and Management. The reference list contains 167 books and articles. The analytic study of the field has reached maturity following the contributions of many individuals and institutions.
Solving Influence Diagrams: Exact Algorithms
Authors: Shachter Ross D. , Bhattacharjya Debarun 
Published: February 2011

AbstractInfluence diagrams were developed as a graphical representation for formulating a decision analysis model, facilitating communication between the decision maker and the analyst. We show several approaches for evaluating influence diagrams, determining the optimal strategy for the decision maker and the value or certain equivalent of the decision situation when that optimal strategy is applied. They can be converted into decision trees, evaluated directly as influence diagrams, or converted into another graphical model, a rooted cluster tree, for analysis.
Standby Redundant Systems
Authors: Distefano Salvatore 
Published: February 2011

Abstract
In order to conjugate the increasing demand of reliability, dependability, and safety with the costs of redundancy, several efforts have been made. The most promising and effective solution, guaranteeing the desired trade‐offs among quality and cost requirements, is the standby redundancy. The main goal of standby redundancy is to improve system reliability and availability by exploiting standby components. Standby redundant systems are the technical implementation of the standby redundancy view.
This article provides an overview of the concepts related to standby redundant systems. The reliability of standby redundant systems is evaluated obtaining specific formulas, and an overview of possible standby redundant systems management techniques, issues, and challenges is provided.
Statistical Process Control
Authors: Fogliatto Flavio Sanson , Anzanello Michel J. 
Published: February 2011

Abstract
Production processes aimed at meeting customers' requirements need to be stable or repeatable. More specifically, the process must yield outputs around the target or nominal specifications of the product's quality characteristics with little or no variability. Statistical process control (SPC), or Statistical quality control (SQC), act as a set of powerful tools for improving process performance and achieving process stability. In this article we briefly present the fundamentals of some of these tools, namely Control charts and capability indices.
Stochastic Network Interdiction
Authors: Morton David P. 
Published: February 2011

AbstractWe describe discrete stochastic network interdiction of shortest‐path, and maximum‐reliability‐path, networks. A nested “max–min” model is reformulated using duality and, if necessary, an equivalent penalty‐based model. The resulting mixed‐integer program is then amenable to solution by standard solvers. Special‐purpose decomposition algorithms, and accompanying valid inequalities, are also developed, which permit solution of larger scale problem instances. Throughout the article, we point to important models that arise when key assumptions in our formulations are modified.
Subjective Probability
Authors: Al‐Najjar Nabil I. , de Castro Luciano 
Published: February 2011

AbstractWe provide an overview of the idea of subjective probability and its foundational role in decision making and modern management sciences. We highlight the role of Savage's theory as an organizing methodology to guide and constrain our modeling of choice under uncertainty, rather than a substantive statement subject to refutations by experimental or psychological evidence.
Supply Chain Risk Management
Authors: Sodhi ManMohan S. , Tang Christopher S. 
Published: February 2011

AbstractWe present different aspects of supply chain risk management. In doing so, we bring together different strands in the literature from supply chain management, enterprise risk management, and crisis management/business continuity, areas that are themselves multidisciplinary. As such, supply chain management is still an emerging area and we present these diverse strands from an enterprise risk management perspective in four categories: (i) identifying risk, (ii) assessing risk (iii) mitigating risk, and (iv) responding to risk incidents, including communication.
Support Vector Machines for Classification
Authors: Gilbert Robin C. , Trafalis Theodore B. , Adrianto Indra 
Published: February 2011

AbstractIn this article, we introduce the subject of support vector machines (SVMs), describing their applications to binary and multiclass classification as well as different SVM formulations that are used in such supervised learning problems. This article is not exhaustive and many approaches have not been considered. More detailed treatments are discussed in the books by Abe, Cristianini and Shawe‐Taylor, Schölkopf, and Smola and Vapnik.
Symmetry Handling in Mixed‐Integer Programming
Authors: Ostrowski Jim 
Published: February 2011

AbstractThis article focuses on solving integer programs whose feasible regions are highly symmetric. Symmetry has long been considered a curse in integer programming, and auxiliary (often extended) formulations are sought to reduce the amount of symmetry in an integer linear programming (ILP) formulation. The approach taken in this article describes methods that seek toexploitthe symmetry, not avoid it by reformulation.A standard method for solving integer programs isbranch‐and‐bound. In branch‐and‐bound, the set of feasible solutions is partitioned, forming more easily solved subproblems. The presence of symmetry means that many of these subproblems are equivalent in a sense we describe later. Only one member of each collection of equivalent subproblems needs to be solved. Failure to recognize that many subproblems are equivalent results in a waste of computational effort that can render an instance unsolvable by branch‐and‐bound. In this article, we describe methods that use the symmetry of the problem formulation to reduce the size of the feasible region.