bluuebunny/crossref_metadata_2025_split huggingface dataset:
History of Constraint Programming
Authors: Barták Roman 
Published: January 2011

AbstractConstraint programming is a technology for solving combinatorial optimization problems. It appeared in the 1960s in systems such as Sketchpad, and core ideas such as arc and path consistency techniques were proposed and developed in the 1970s. The real landmark of constraint programming was in the 1980s when researchers realized that unification in logic programming is a special form of constraint solving, and constraint logic programming appeared. Since that time, many solving techniques and approaches were studied, but the core idea of constraint programming remains the same—it is a technology based on integration of search techniques with special inference techniques exploiting the structure of constraints in the problem. Constraint programming is successful in many application areas. Scheduling is probably the most prominent one, but constraints are also applied when solving problems in planning, configuration, vehicle routing, networking, bioinformatics, and so on. This article describes how constraint programming evolved since its beginnings to the current state.
Icelandic Operations Research Society
Authors: Stefánsson Hlynur , Ásgeirsson Eyjólfur Ingi , Ólafsson Snjólfur 
Published: January 2011

AbstractThe Icelandic Operations Research Society was founded in 1985. It has around 100 members. The main activities are in the form of monthly meetings where OR‐related work is presented. The topics presented are from industry and academia alike and so are the members of the society. In 2006 ICORS hosted the EURO conference in Iceland. The theme of the conference was “OR for Better Management of Sustainable Development.” On the whole, the conference was a great success with 1541 delegates from 63 countries.Introductory courses to OR are taught to approximately 250 students every year and several advanced courses are offered at undergraduate and graduate levels. There are in total five faculty positions in Iceland that are devoted to operations research. There are at least four members of ICORS that have OR/management science faculty positions in foreign universities, including Georgia Institute of Technology, London School of Business, Stanford University, and University of Atlanta. Quite a few students have completed MSc or PhD degrees in operations research from various foreign universities, mostly in North America and Europe. OR methods have been used in several significant practical projects and the main emphasis has been on fishing and the fishing industry as it is one of the backbones of the Icelandic economy.
Initial Transient Period in Steady‐State Systems
Authors: White K. Preston , Robinson Stewart 
Published: January 2011

AbstractThis article describes the problem of the initial transient that arises when estimating the limiting distribution of a nonterminating stochastic process. Concepts are explored with references to simple M/M/1 queueing model with an artificially induced initial bias. A range of solution methods have been proposed and here we explore two such approaches, graphical and heuristic. We also demonstrate how these methods might be combined. Results from test problems consistently show that truncation of output data from a single long run is more economical than the replication/deletion approach for nonterminating simulations.
The Naturalistic Decision Making Perspective
Authors: Nemeth Christopher , Klein Gary 
Published: January 2011

AbstractThe naturalistic decision making (NDM) approach seeks to understand human cognitive performance by studying how individuals and teams choose among alternatives in real‐world settings. Unlike laboratory studies, NDM research captures human interactions with actual work settings that are typically uncertain, complex, fluid, and time‐pressured. The NDM approach emphasizes the role of experience in making it possible for individuals to quickly categorize situations in order to make effective decisions. Models of behavior that have resulted from these studies inform research and performance in high hazard work settings such as revisions of military doctrine, training that is focused on decision requirements, and the development of information technologies to support cognitive work.
The Law and Economics of Risk Regulation
Authors: Coglianese Cary 
Published: January 2011

AbstractLaw plays a central role in the management of risk in society. The rules adopted by regulatory agencies now affect nearly every facet of the economy, and as such regulation has motivated a substantial body of academic research. Law and economics research on regulation has, first, demonstrated the normative justification for governmental intervention in the marketplace based on the concept of market failure. Second, political economy research on regulation has shown how, as a positive matter, interest groups, political movements, and public pressure affect the stringency of such regulation, sometimes more than any normative rationale for regulation. Third, risk regulation research has clarified the available normative principles that can guide the selection of regulatory stringency, from the zero‐risk principle to cost‐benefit balancing. Finally, the law and economics literature on risk regulation offers lessons about the form or design of regulatory instruments, from traditional “command‐and‐control” standards to more innovative possibilities, such as market‐based instruments or management‐based regulation.
Maintenance Management
Authors: Leung Kit‐Nam Francis 
Published: January 2011

Abstract
The purpose of this article is to introduce to engineers, health‐care workers, and others who bear the responsibility for decision making in the general area of equipment maintenance, the potential benefits that can accrue to an organization by an examination of maintenance problems from a management science (MS) perspective. MS is concerned, to a large extent, with the construction of mathematical models of problem situations whereby alternative actions can be assessed, and the best identified.
Research into production technologies should consider maintenance. the significance and complexity of maintenance in modern production and manufacturing systems indicate that maintenance should be viewed as an increasingly important area of study and research.
Markov Renewal Processes
Authors: Ziya Serhan 
Published: January 2011

Abstract
A Markov renewal process is a vector valued counting process that keeps track of the number of certain types of events associated with a stochastic process. It is a generalization of a renewal process in that a Markov renewal process with a single‐dimensional state representation is a renewal process. In this article, we first define Markov renewal sequences, which are at the core of Markov renewal processes. Then, we give a formal description of Markov renewal processes and provide a number of examples where these processes appear.
Mass Customization
Authors: Parlaktrk Ali K. , Swaminathan Jayashankar M. 
Published: January 2011

Abstract
Mass customization aims to provide individually customized products with near mass production efficiency. We give examples of different types of mass customization. We then discuss the benefits that make mass customization attractive. This includes ability to charge price premiums, stronger relations with customers, better market understanding, and ability to operate without finished‐goods inventories. Mass customization leads to some challenges due to the variability it introduces to a firm's operations. We discuss some enablers of mass customization that can help firms overcome these challenges.
Metaheuristics for Stochastic Problems
Authors: Hvattum Lars Magnus , Esbensen Eystein Fredrik 
Published: January 2011

AbstractA large number of publications have been written about metaheuristics and their use in solving deterministic optimization problems. As metaheuristic techniques become more widespread in the production of decision support systems, it is clear that they also need to cope with problems, based on real‐world applications, that contain noise and uncertainty. An increasing amount of work is being put into the study of metaheuristics for stochastic problems, and into how metaheuristics must be adapted to efficiently handle different sources of stochasticity. We give examples of how metaheuristics have been used to solve stochastic problems, thereby illustrating the additional considerations that are required in this context.
MINLPSolver Software
Authors: Bussieck Michael R. , Vigerske Stefan 
Published: January 2011

AbstractIn this article we give a brief overview of the start‐of‐the‐art in software for the solution of mixed integer nonlinear programs (MINLP). We establish several groupings with respect to various features and give concise individual descriptions for each solver. The provided information may guide the selection of a best solver for a particular MINLP problem.
Models and Basic Properties
Authors: Henrion René 
Published: January 2011

Abstract
This chapter provides an introduction into basic models and properties of probabilistic constraints. Models are distinguished according to structural assumptions about the underlying system of inequalities and the distribution of the random parameter. Resulting analytical properties like continuity and convexity are discussed for different settings. Since the crucial features of probabilistic programming are hidden in the constraints, we disregard in this introduction, the connection with the objective function which goes basically along the common lines of nonlinear optimization.
Monte Carlo Simulation as an Aid for Deciding Among Treatment Options
Authors: Shechter Steven 
Published: January 2011

AbstractPatients and their physicians are often faced with difficult choices among multiple treatment options, including the proper timing of them. Moreover, these decisions must be made under uncertainty of what outcomes may arise overtime. For various reasons, randomized controlled trials are often not feasible for helping determine the best course of therapy in these situations. Instead, researchers are increasingly turning to Monte Carlo simulation as another means for helping estimate the costs and benefits of different treatment options. We describe the motivation for this methodology, key elements of a treatment simulation model, as well as limitations and challenges in developing these models.
Materials Requirements Planning
Authors: Benton, W.C. 
Published: January 2011

Abstract
Material Requirements Planning (MRP) is a material planning methodology developed in the 1970's making use of computer technology. MRP provide the ability to proactively manage inventory levels and material flows through a manufacturing facility. The primary features of MRP are the creation of material requirements via exploding the bills of material, and time‐phasing of requirements using posted average lead times. MRP II was developed as the second generation of MRP and it features the closed loop system: production planning drives the master schedule which drives the material plan which is the input to the capacity plan. MRP determines when components items are needed and scheduling them to be ready at that time, no earlier or later.
Multicommodity Flows
Authors: Vaidyanathan Balachandran , Ahuja Ravindra K. 
Published: January 2011

Abstract
The multicommodity flow problem is a generalization of the minimum cost network flow problem. In this problem, several commodities governed by their own network flow constraints share the same underlying network. Given the supplies and demands of the different commodities, the shared capacity of each arc in the network, and cost of flow of each commodity on each arc, the objective is to determine the flow that minimizes the total cost. The multicommodity flow problem has been extensively applied to solve problems in areas such as transportation, telecommunications, and scheduling. In this article, we introduce the multicommodity flow problem, outline some of its applications, and describe efficient methods to solve the problem.
Neuroeconomics and Game Theory
Authors: Stallen Mirre , Sanfey Alan G. 
Published: January 2011

AbstractMany of our decisions are made in the context of highly complex social interactions. By combining neuroscientific measures with formal models of game theory, researchers have gained insight into the mechanisms underlying social decision making. Early findings from this interdisciplinary effort have already begun to show how reasoning about the intentions of others is important for interactive behavior, how the reward system of the brain is directly involved in cooperation and social exchange, and how the experience of emotions plays a vital role in successful decision making. The neuroeconomic approach to social decision making offers exciting potential to move beyond speculation about the processes that determine our decisions in interactive contexts, and to extend our knowledge about the functioning of the human brain.
Omega Rho International Honor Society for Operations Research and Management Science
Authors: Rogers David F. 
Published: January 2011

Abstract

Founded in 1976 and formally a part of INFORMS since 1998, Omega Rho International Honor Society for operations research and management science is a member of the Association of College Honor Societies and a foundation for excellence in operations research and management science disciplines. Primarily designed for honoring our best students and faculty through chapters established at universities, Omega Rho also sponsors the
Omega Rho Distinguished Lecture
at INFORMS Annual Meetings and select International Meetings, and provides annual support for the INFORMS Doctoral Student Colloquium and other select student‐related events. Chapter expansion around the globe to honor deserving students effectively is an ongoing emphasis.
Operations Research Models for Cancer Screening
Authors: Alagoz Oguzhan , Ayer Turgay , Erenay Fatih Safa 
Published: January 2011

AbstractEarly diagnosis through screening is an effective means of decreasing the death rate from cancer, the second leading cause of death in the US, since most cancers are now curable if detected early. On the other hand, the interventional procedures that result from detected abnormalities (both false and true positives) during screening and the significant cost of population‐based screening of asymptomatic individuals require a careful analysis of the benefits and risks of screening in both utility and economic terms. In this article, we summarize the studies applying Operations Research (OR) tools to investigate issues related to cancer screening. In particular, we review OR studies considering cancer screening that are either briefly described or not mentioned in previous review articles. We classify relevant literature by cancer type including breast cancer, colorectal cancer, and cervical cancer. We also discuss several issues for future research in cancer screening.
Operations Research Tools for Addressing Current Challenges in Emergency Medical Services
Authors: Henderson Shane G. 
Published: January 2011

Abstract
Emergency medical service (EMS) providers face a host of significant challenges, including traffic congestion, increasing call volumes, hospital diversion (i.e., emergency departments at hospitals not allowing ambulances to deliver patients to them), and increasing transfer times at emergency departments. Many of these challenges can be attacked through an application of operations research (OR) techniques in conjunction with EMS expertise. The purpose of this article is to outline some of the key challenges and potential OR‐based remedies, and to discuss in detail one such remedy that has various names including “system‐status management” and “move up.”
Non‐Expected Utility Theories
Authors: Bordley Robert F. 
Published: January 2011

AbstractIn this article nonexpected utility theories are reviewed, which focuses on four controversial axioms underlying Savage's subjective expected utility theory. For each axiom, we present practical concerns raised by each axiom along with Savage's defense of that axiom. We then discuss efforts to address these problems that are consistent with Savage's axioms (which we refer to asparadigm preserving) as well as efforts to address these problems that relax or violate Savage's axioms (which we refer to asparadigm shifting.) We conclude that this tension between paradigm‐preserving versus paradigm‐shifting approaches to address problems in Savage's formulation has been invaluable.
Optimization Problems in Passenger Railway Systems
Authors: Caprara Alberto , Kroon Leo , Toth Paolo 
Published: January 2011

Abstract
This article describes the main optimization problems that are faced in the planning of a passenger railway system, from the definition of the routes and frequencies of the trains in the railway network to the construction of the duties and the rosters for drivers and conductors. These problems are presented in the order in which they are faced in practice, illustrating what they are aimed at, and how they are solved by mathematical programming techniques. The article also briefly illustrates recent advances in producing “robust” (i.e., fault‐tolerant) solutions for these problems, and in the application of optimization methods for real‐time recovery of railway systems.
Perfect Information and Backward Induction
Authors: Kaminski Marek M. 
Published: January 2011

Abstract
Backward induction (BI) is defined for extensive‐form games of perfect information. The relation of BI to Nash equilibrium, the concept of incredible threat, and subgame perfect equilibrium is described. Variants of the procedure include cutting long subgames or cutting any subset of disjoint subgames. The discussion of criticisms is focused on the incorrect predictions of BI in games such as Centipede. An example of the incorrect application of BI concludes the article.
Evaluations of Single‐ and Repeated‐Play Gambles
Authors: Wedell Douglas H. 
Published: January 2011

Abstract
Choices and evaluations of risky prospects often violate norms of expected utility theory and the underlying normative principles of rational decision‐making. However, when risky prospects are represented as being repeatedly instantiated, choices and evaluations often change to conform more closely to the long‐run perspective reflected in normative models. Differences between responses for single and repeated gambles have been analyzed normatively, explored empirically, and evaluated theoretically. Normative analyses have argued both for and against changing behavior for single and repeated gambles. Empirically, shifts in behavior for single and repeated risky prospects are found to be common, stronger when outcomes are perceived as fungible, and stronger when the repeated outcome distribution is displayed. Furthermore, these shifts have been found to occur in both laboratory and real‐world settings and have been considered a possible moderator of violations of expected utility theory. A variety of theoretical explanations are posited to explain this behavior, including those that emphasize consequences of loss aversion as well as those that implicate alternative bases for evaluating single and repeated cases.
Quantum Command and Control Theory
Authors: Forsythe Steve 
Published: January 2011

AbstractThis article presents results of a Johns Hopkins University Applied Physics Laboratory (JHU/APL) effort on the fundamental theory of C2. A standard method for course of action (COA) selection is the weighted numerical comparison (WNC) method. There are numerous issues with the WNC method. One of these issues is that the expected results are frequently used to select a course of action (COA). The Quantum C2 Theory, like the physics‐based quantum theory, treats outcomes not as point estimates but rather as probability distributions. Understanding and employing these probability distributions can aid commanders in shaping and selecting courses of actions that are the best trade‐off between mission success and risk. Several alternatives to WNC are presented which mitigate some of the issues with WNC and provide improved support for the commander's COA selection.
Risk Assessments and Black Swans
Authors: Aven Terje 
Published: January 2011

AbstractRisk assessments are used to identify hazards/threats, assess their consequences, and describe risk. The assessments provide a basis for evaluating whether risk is tolerable or acceptable and for choosing the most effective and efficient risk reduction measure. The issue raised in this article is to what extent the assessments are able to reveal and adequately describe risk contributions from potential “surprises” (“black swans”). We argue that the standard probabilistic‐based assessments have too narrow a perspective on uncertainties to be successful in this respect. An extended risk assessment approach, which sees beyond the probabilistic analysis, is required. The basic pillars of such an approach are outlined.
Surgery Planning and Scheduling
Authors: Erdogan S. Ayca , Denton Brian T. 
Published: January 2011

AbstractSurgery planning and scheduling systems play a critical role in the efficient matching of supply and demand for surgery. Competing performance measures and a number of complicating factors make surgery planning and scheduling challenging. Operating room (OR) managers must consider uncertainty in the duration of surgery and other critical activities, the arrival of unexpected urgent add‐on patients, and cancellations on the day of surgery while efficiently managing a variety of human and technical resources. Surgery schedules must be designed to balance competing performance criteria such as patient waiting time as well as idle time and overtime for both the OR team and other personnel. This article presents an introduction to surgery scheduling, a literature review, a small example, and a description of some of the important open challenges.
Why Traditional Kanban Calculations Fail in Volatile Environments
Authors: Kessinger Colin , Gray Allan 
Published: January 2011

Abstract
Recent years have seen the extension of “kanban thinking” from the factory floor to entire supply chain networks. However, the traditional calculations used to size kanbans often perform poorly in these new environments, which are characterized by longer leads times and higher demand volatility. This article examines why that is the case, and highlights many of the common pitfalls facing practitioners when implementing kanbans or reorder points (ROPs). Among the issues tackled are why “consumption‐based” reorder points are flawed in volatile environments; how companies' attempts to incorporate forecasts in kanban calculations go awry; and how confusion among alternative definitions of service level can lead to excess inventory.
Why Risk is Not Variance
Authors: Cox, Louis Anthony , Huber William A. 
Published: January 2011

Abstract
Variance (or standard deviation) of return is widely used to measure risk in financial investment risk analysis and mean‐variance portfolio optimization. Yet, except under very special conditions (such as normally distributed returns), mean‐variance decision making violates the principle that a rational decision maker should prefer higher probabilities of receiving a fixed gain to lower probabilities, all else being equal. Indeed, a mean‐variance decision maker who considers all prospects corresponding to some particular mean‐variance combination to be unacceptable would necessarily refuse some prospects that offer a positive probability of gain and no possibility of loss—a rule that few canny risk managers would want to live by.