bluuebunny/crossref_metadata_2025_split huggingface dataset:
System Availability
Authors: Gaver Donald P. , Jacobs Patricia A. 
Published: February 2011

Abstract

Availability
describes and quantifies the propensity of a system to be functionally operative upon demand. Example systems include a missile, torpedo, aircraft, or wheeled or tracked vehicles, civilian as well as military, but also an entire computer communication system, as in “cloud computing.” It increases if failure‐free operating times between failures (“up times”) become long, and decreases if following failure or anticipatory removal, logistics delays and repair (“down times”) are protracted. This article summarizes the general availability concept and discusses the limitation of
operational availability
(defined as the probability of being operational at a particular time) suggesting that
mission availability
(probability of being operational at a time and throughout a subsequent mission) is often more useful and appropriate. The important distinction between these is treated later in this chapter.
The Condition‐Based Paradigm
Authors: Meeker William Q. , Hong Yili , Escobar Luis A. 
Published: February 2011

Abstract
The objective of this article is to introduce the condition‐based paradigm for reliability and the use of condition information to assess and maintain product reliability. Repeated measurement of product condition parameters, also known as degradation data, can be used to assess the product reliability and make decisions about product maintenance. After describing several examples of degradation data, we introduce some statistical models and methods, including random effects models and stochastic models, for analyzing degradation data. These methods are illustrated with real examples. We conclude the article with a discussion of continuous monitoring of product condition and system health. Suggestions for further reading are also given.
The Failure‐Based Paradigm
Authors: Meeker William Q. , Hong Yili , Escobar Luis A. 
Published: February 2011

Abstract
The objective of this article is to describe the failure‐based paradigm for reliability and explain the use of failure‐time data to assess product reliability. After discussing the main sources and characteristics of failure‐time data, we introduce some basic reliability metrics such as failure probability and hazard rate and the nonparametric and parametric estimation of these or related metrics. We present a brief introduction to regression methods to incorporate explanatory variables in the analysis of reliability data. The analysis of data with multiple failure modes (competing risks) is illustrated with an example. The article concludes with a discussion of some trends in the next generation of failure‐time reliability data. Materials for further reading are provided.
The
M
/
G
/1 Queue
Authors: Boxma Onno J. 
Published: February 2011

Abstract

The
M
/
G
/1 queue is a single‐server queue, to which customers arrive according to a Poisson process, requiring generally distributed service times. It is one of the most classical and best‐understood queueing systems. We review some of its key properties, focusing on queue lengths, on waiting and sojourn times, and on the busy period. We mainly consider the First Come First Served service discipline, but some other disciplines are also briefly discussed.
The
M
/
G
/∞ Queue
Authors: Gautam Natarajan 
Published: February 2011

Abstract

The objective of this article is to describe the
M
/
G
/∞ system, where the arrivals are according to a Poisson process, the service times are independent and identically distributed, and there are an infinite number of servers. We analyze the number in the system under both transient and steady‐state conditions. We describe results for the busy period distribution of as well as departure from an
M
/
G
/∞ queue. We finally study an extension in terms of considering time‐varying arrivals.
The

M

/

M

/∞ Queue
Authors: D'Auria Bernardo 
Published: February 2011

Abstract

In this article, we describe the
M
/
M
/∞ queueing system, the simplest example of an infinite server queue in which the customers arrive according to a homogeneous Poisson process and get served immediately. The system has no waiting line and its customers are served in an exponential time and leave the system as soon as their service demand is fully met. Applications of this model can be found in many different fields such as physics, telecommunications, and vehicle traffic. The main advantage of the model, thanks to the independence of customers' behavior across the whole queueing process, is the simplicity of its mathematical description. The
M
/
M
/∞ queue is always stable and the stationary distribution of the number of customers in the system is a Poisson random variable. We summarize the main characteristic measures associated with the queue.
Total Expected Discounted RewardMDPs: Value Iteration Algorithm
Authors: Bountourelis Theologos 
Published: February 2011

AbstractIn this article, we discuss the total expected discounted reward Markov decision problem and its popular algorithmic solution, the value iteration algorithm. We formally state the problem, characterize the optimal policy, and describe the algorithm in its simplest form. We conclude with a discussion on the problem assumptions, the more advanced versions of the algorithm, and highlight the relevant literature.
Search Games
Authors: Gal Shmuel 
Published: February 2011

AbstractThis article surveys the area of search games with immobile and mobile hider. The search space is either a graph, a bounded domain or an unbounded set. The search process is presented as a two‐player zero‐sum game between the searcher and the hider. The searcher moves along a continuous trajectory and the cost function is the time needed to find the hider. An overview is presented with emphasis on general results concerning minimax search trajectories and optimal search strategies.
TPZS
Applications: Blotto Games
Authors: Washburn Alan R. 
Published: February 2011

Abstract
Blotto games are a class of two‐person zero–sum game where each player must allocate a budget of some kind among several independent areas. In each area, the payoff depends only on the two allocations to that area, and the total payoff is the sum of the individual area payoffs. The class has properties that permit the solution of large games and includes numerous military applications. This article discusses applications and solution methods.
Traffic Network Analysis and Design
Authors: Boyles Stephen D. , Waller S. Travis 
Published: February 2011

AbstractThis article provides an overview of basic traffic network analysis methods, focusing on the central traffic assignment problem. Formulations of the user‐optimal and system‐optimal assignment problems are given and link‐based, path‐based, and origin‐based solution methods are presented. Congestion pricing and network design are then presented as two applications of these models. Finally, a brief treatment is given to extensions of the basic model: stochastic user equilibrium, elastic demand, uncertain costs, and dynamic traffic assignment. A discussion of seminal papers in the field is provided for historical context and further reference.
Transportation Algorithms
Authors: Altner Douglas S. , Brooks J. Paul 
Published: February 2011

AbstractThe transportation problem requires, given a set of suppliers and a set of customers, finding a minimum cost plan to determine how many goods each supplier should send to each customer. Each customer's minimum requirements must be satisfied without exceeding the supply of any supplier. This problem has a plethora of industrial and military applications including applications beyond modeling the physical transporting of goods. In this article, we present the transportation problem; discuss its early history, properties, and relationship to other problems; describe the well‐known transportation simplex method; and survey the literature on transportation algorithms.
Transportation Resource Management
Authors: Kunnumkal Sumit , Topaloglu Huseyin 
Published: February 2011

Abstract
In this article, we describe a variety of modeling and solution approaches for transportation resource management problems. The solution approaches that we propose either build on deterministic linear programming formulations, or formulate the problem as a dynamic program and use tractable approximations to the value functions. We describe two classes of methods to construct approximations to the value functions. The first class of methods relax certain constraints in the dynamic programming formulation of the problem by associating Lagrange multipliers with them, in which case, we can solve the relaxed dynamic program by concentrating on one location at a time. The second class of methods use a stochastic approximation idea along with sampled trajectories of the system to iteratively update and improve the value function approximations. After describing several solution approaches, we provide a flexible paradigm that is useful for modeling and communicating complex resource management problems. Our numerical experiments demonstrate the benefits from using models that explicitly address the randomness in resource management problems.
Treewidth, Tree Decompositions, and Brambles
Authors: Koster Arie M.C.A. 
Published: February 2011

AbstractThe notions of tree decomposition and treewidth play an important role in the design and analysis of algorithms for combinatorial optimization problems. The notion of a bramble can be used to find a lower bound on the treewidth of a graph. In this article we review the most important results for treewidth, tree decompositions, and brambles, both from a theoretical and practical point of view.
Triage in the Aftermath of Mass‐Casualty Incidents
Authors: Argon Nilay Tanik , Ziya Serhan , Winslow James E. 
Published: February 2011

AbstractMass‐casualty incidents, such as plane crashes and terrorist attacks, take place rarely but when they happen they may result in a massive number of casualties with severe injuries requiring immediate medical attention. In the aftermath of such an event, medical resources are typically overwhelmed by the sudden jump in demand and hence they need to be rationed in some way to minimize the number of deaths and permanent injuries. This rationing process that determines the priorities according to which patients are given access to the limited resources is calledtriage. This chapter provides a review of the operations research literature on patient triage and prioritization decisions in the aftermath of mass‐casualty incidents. In the literature, there are two lines of research that take two different approaches to the problem but with similar conclusions. The most important conclusion is that mass‐casualty triage systems should take into account the number of casualties and the capacity of resources to save the maximum number of lives possible. Furthermore, an effective rule that categorizes the patients based on their service requirements, chances of survival, and patterns of health deterioration is essential for the success of the emergency response effort.
Interior Point Methods for Nonlinear Programs
Authors: Peng Jiming , Salahi Maziar 
Published: February 2011

AbstractIn the past decades, there has been substantial progress in the study of interior point methods for solving linear and nonlinear programming problems arising from various real‐world applications. Several powerful software packages have been developed that are now widely used as one of the working horses by many practitioners. In this article, we give a brief overview of interior point algorithms for solving nonlinear programming problems (NLPs).
Heuristics for the Traveling Salesman Problem
Authors: Kurz Mary E. 
Published: February 2011

Abstract

The article considers heuristics (methods to create feasible solutions) for the traveling salesman problem (TSP). We differentiate between
constructive
heuristics which create feasible solutions from no solution, and
improvement
heuristics which potentially create new feasible solutions from existing feasible solutions. While the list of published heuristics and variants of the TSP is nearly unlimited, this article focuses on an overview of the most enduring methods for the TSP with an unrestricted distance matrix.
Bilevel Network Interdiction Models: Formulations and Solutions
Authors: Wood R. Kevin 
Published: February 2011

AbstractThebilevel network‐interdiction problem(BNI) models the effective use of limited resources, by an “interdictor,” to attack a network that an “enemy” may use to the interdictor's disadvantage. BNI is a two‐person, zero‐sum, sequential‐play (Stackelberg) game with two stages: in the first stage, the interdictor attacks components of his enemy's network, that is, nodes and or/arcs; and in the second stage the enemy observes the damage caused by the attack and operates the damaged network so as to optimize a measure of functionality such as throughput, output or cost. For example, using a limited number of aerial sorties to attack and destroy production and distribution assets, an interdictor may wish to minimize the maximum “flow” of munitions that his enemy can produce and move to a battlefront. This article models interdictions with binary variables and simple constraints; solution of an optimization model, often a linear program, represents optimal network operation by the enemy. Simple models can be converted mixed‐integer programs, but solution methods based on decomposition may be preferred for efficiency and generality.
Variants ofBrownian Motion
Authors: Bhamidi Shankar , Greenwood Priscilla E. 
Published: February 2011

AbstractVariants of Brownian motion and related processes arise in diverse contexts. Examples include scaling limits of discrete models like queueing networks, combinatorial structures such as random trees, physical models of particles diffusing through disordered media, and the evolution of stock process in finance. Here, we describe some of these variants, including reflected Brownian motion, geometric Brownian motion, the Brownian sheet, Brownian excursions, and fractional Brownian motion.
Vendor‐Managed Inventory
Authors: Fry Michael J. 
Published: February 2011

Abstract
Vendor‐managed inventory (VMI) programs transfer inventory control decisions from the retailer to the supplier. VMI programs rely on information sharing to transmit customer demand and current inventory information to the supplier. The supplier is then in charge of determining the timing and quantity of inventory deliveries to retailers. Most VMI programs contain an initiating contract that specifies elements of the agreement including performance metrics and payment terms. VMI programs have been shown to reduce supply chain costs through shipment consolidation and incentive alignment of decentralized supply chain agents. However, not all VMI programs result in supply chain savings. Even in those cases where VMI can result in system‐wide savings, care must be taken to create agreements that are preferable to both suppliers and retailers.
Very Large‐Scale Neighborhood Search
Authors: Altner Douglas S. , Ahuja Ravindra K. , Ergun Özlem , Orlin James B. 
Published: February 2011

AbstractMany optimization problems that model the essential issues of important real‐world decision making are computationally intractable. Therefore, a practical approach for solving such problems is to employ heuristic techniques that find nearly optimal solutions within a reasonable amount of computation time.Improvement algorithmsgenerally start with a feasible solution and iteratively try to obtain a better solution. Neighborhood search algorithms, which are alternatively calledlocal search algorithms, are a wide class of improvement algorithms where at each iteration an improving solution is found by searching a “neighborhood” of the current solution. A critical issue in the design of a neighborhood search algorithm is defining what solutions constitute the neighborhood of a solution. As a rule of thumb, the larger the neighborhood, the better is the quality of the locally optimal solutions, including the final solution selected upon termination. Similarly, the larger the neighborhood, the longer it takes to search the neighborhood. Thus, a larger neighborhood does not necessarily produce a more effective heuristic unless one can search the larger neighborhood efficiently. This article concentrates on neighborhood search algorithms where the size of the neighborhood is “very large” with respect to the size of the input data and the neighborhood can be searched efficiently. We survey three broad classes of very large‐scale neighborhood (VLSN) search algorithms: variable‐depth methods in which large neighborhoods are searched heuristically, large neighborhoods that are searched by solving a constrained minimum–cost flow problem, and other situations that give rise to efficiently searchable large neighborhoods.
Cognitive Mapping and Strategic Options Development and Analysis (SODA)
Authors: Georgiou Ion 
Published: February 2011

AbstractStrategic options development and analysis (SODA) is a problem structuring method that incorporates a particular approach to cognitive mapping that draws from the psychological theory of personal constructs. What differentiates it from all other cognitive mapping approaches is its use of bipolar constructs that enable the clarification of meaning. Besides their qualitative content, the structure of SODA maps allow for a range of quantitative analyses, especially through the use of graph theory. This article describes the contemporary context of cognitive mapping, introduces the design and analysis of SODA maps, and concludes with a critical appreciation that identifies three significant areas for further research.
Constraint Qualifications
Authors: Solodov Mikhail V. 
Published: February 2011

Abstract
We discuss assumptions on the constraint functions that allow constructive description of the geometry of a given set around a given point in terms of the constraints derivatives. Consequences for characterizing solutions of variational and optimization problems are discussed. In the optimization case, these include primal and primal‐dual first‐ and second‐order necessary optimality conditions.
Subgradient Optimization
Authors: Nesterov Yurii 
Published: February 2011

Abstract
We discuss the numerical schemes and complexity of nonsmooth optimization problems, both in convex and nonconvex variants. For nonconvex problems, we present the concepts of Clarke subdifferential and lexicographic differentiation. The main computational approaches are given. On the other hand, we show NP‐hardness of the simplest nonsmooth and nonconvex optimization problem. For convex problems, we describe the primal and dual subgradient methods, the ellipsoid method, and other cutting plane schemes. Corresponding complexity bound are also presented.
Primal–Dual Methods for Nonlinear Constrained Optimization
Authors: Griva Igor , Polyak Roman A. 
Published: February 2011

Abstract
The article describes interior‐ and exterior‐point primal–dual methods. The interior‐point methods are related to sequential unconstrained minimization techniques, while the exterior‐point methods are based on nonlinear rescaling and augmented Lagrangian theory. One of the main advantages of the primal–dual methods described in the article is their asymptotic quadratic convergence under the standard second‐order optimality conditions.
Strategic Customer Behavior in a Single Server Queue
Authors: Haviv Moshe 
Published: February 2011

AbstractThe purpose of this article is to introduce the reader to the topic of strategic behavior of customers in queues. We assume that customers individually decide on issues such as whether or not to join the queue, whether or not to renege from a queue after waiting for a while, or whether or not to pay some fee in order to belong to a higher priority class. For some examples, we define the appropriate noncooperative games and look for their Nash equilibria. We restrict ourselves to M/M/1 queues with homogeneous customers (with respect to their cost/reward parameters).
Optimizing the Aviation Checkpoint Process to Enhance Security and Expedite Screening
Authors: Lee Adrian J. , Jacobson Sheldon H. 
Published: February 2011

Abstract
A queueing analysis is used to model the sequential screening of passengers at an airport security checkpoint. A multiobjective, nonlinear optimization problem is then formulated to balance the expected number of true alarms with the expected amount of time a passenger spends in the security system, thereby enhancing security operations, while also improving passenger throughput. Both multilevel security systems and those comprising primary and secondary levels of screening are analyzed to demonstrate the benefits from implementing each security system structure. Applying optimization techniques to the design of aviation passenger screening policies provides the means to both increase security and expedite screening by efficiently and effectively utilizing available screening resources.
Analytics in Retail
Authors: Klabjan Diego , Jiang Yan , Williams Loren 
Published: February 2011

Abstract
Retailing has always been an active research area for OR/MS researchers. Nowadays, the collaboration between retail practitioners and academic researchers yield more and more useful analytical models and solution algorithms to tackle complicated retail problems. This article illustrates two types of commonly‐used analytic in retail practice, namely forecasting and optimization, using two models that are in production use or development in retail business settings.
Uncertainty in Forest Production Planning
Authors: Alonso‐Ayuso Antonio , Escudero Laureano F. , Guignard Monique , Quinteros Martin , Weintraub Andres 
Published: February 2011

Abstract
The forest harvest and road construction planning problem consists fundamentally of managing land designated for timber production and divided into harvest cells. For each time period, the planner must decide which cells to cut and what access roads to build in order to maximize expected net profit under uncertainty in the timber price. By introducing a multistage stochastic mixed 0–1 programming model, the planner is able to make more robust decisions based on a range of timber price scenarios over time, maximizing the expected value subject to the satisfaction of the constraints in all scenarios and satisfying the so‐called nonanticipativity constraints, instead of merely analyzing a single average scenario. A case study is presented based on the Chilean forest property Los Copihues as a proof of concept.
Using Operations Research to Plan Natural Gas Production and Transportation on the
N
orwegian Continental Shelf
Authors: Nørstebø Vibeke Stærkebye , Schütz Peter , Fodstad Marte , Hellemo Lars , Midthun Kjetil , Rømo Frode , Tomasgard Asgeir 
Published: February 2011

Abstract
Decisions regarding natural gas production, processing, and transportation in interconnected networks depend on each other. Changes in the system affect both its capacity and flexibility. We discuss the main elements of the GassOpt model, an application developed to ensure optimal operation of this network by considering the complete system, its integration, and to provide valuable insights in the dependencies between the different system elements. The objective of GassOpt is to maximize the total flow of natural gas from the North Sea to the customers. The model determines the optimal routing, processing, and blending of natural gas ensuring that demand and quality constraints are satisfied. The application is used by natural gas producers and network operators to plan their activities, to evaluate the operational consequences of plans and investment decisions, and to maximize the value of their operations.