bluuebunny/crossref_metadata_2025_split huggingface dataset:
Applying Value of Information and Real Options inR&Dand New Product Development
Authors: Keisler Jeffrey M. , Mang Paul Y. 
Published: January 2011

AbstractReal options analysis and value of information are related tools that help in guiding staged action in the face of quantifiable risk and uncertainty. Many types of projects involve early investments in the hopes of receiving later payoffs. Classes of projects such as R&D and new product development have received considerable attention in this regard. This article provides an overview of the concepts and literature of real options analysis and its connection to the value of information and decision analysis, and how these relate to such future growth opportunities.R&D and new product development functions can utilize real options analysis/value of information by understanding factors that influence the value of such growth opportunities for the firm and the organizational capabilities required to realize those opportunities. We illustrate this with one set of factors and capabilities associated with a standard R&D project.We define aninnovation optionas an opportunity for a firm to realize the commercial potential of a new technology by translating it into a novel product or service. During the course of development, information regarding the technical and market feasibility of a project is revealed. The firm can utilize the accumulated information regarding the potential net benefits of a project before committing partially or completely irreversible resources to commercialize the technology. In its most simple form, innovation projects should be viewed as a sequence of two decisions: a decision to gather information regarding the project's prospects (experimentation stage), followed by a decision to commercialize the project (implementation stage).A basic two‐stage model illustrates how a firm can maximize the value of its innovation efforts by investing in projects that match its organizational capabilities. First, we identify characteristics that determine the potential option value of an innovation project. We then turn our attention to the firm's capabilities that permit it to exploit high option value opportunities. We specifically investigate a firm'slearning capabilityandabandonment capability. We find that an appropriate fit between project and the firm's characteristics will permit the firm to effectively appropriate the option value associated with its innovation opportunities. We contend that firms that are endowed with projects that tend to have high potential option value can improve their innovation processes by investing in the firm's capabilities that allow them to exploit their opportunities. On the other hand, firms that are endowed with projects that have low potential option value may be better off shedding potentially expensive, but relatively valueless, capabilities. Firms that recognize real options can analyze them, and then focus their efforts on adding value by managing their capabilities to exploit those options.
Approximate Dynamic Programming I: Modeling
Authors: Powell Warren B. 
Published: January 2011

AbstractThe first step in solving a stochastic optimization problem is providing a mathematical model. How the problem is modeled can impact the solution strategy. In this article, we provide a flexible modeling framework that uses a classic control‐theoretic framework, avoiding devices such as one‐step transition matrices. We describe the five fundamental elements of any stochastic, dynamic program. Different notational conventions are introduced, and the types of policies that can be used to guide decisions are described in detail. This discussion puts approximate dynamic programming in the context of a variety of other algorithmic strategies by using the modeling framework to describe a wide range of policies. A brief discussion of model‐free programming is also provided.
Combinatorial Auctions: Complexity and Algorithms
Authors: Bichler Martin 
Published: January 2011

AbstractIn the following, we provide an overview of complexity inherent in the design of combinatorial auctions, and discuss how these challenges are addressed in various combinatorial auction formats.A combinatorial auction allows bidders to submit bids on bundles of objects and can be considered the pivotal example of a multiple object auctions. They also constitute a paradigmatic problem in algorithmic mechanism design. We provide an overview of both the computational complexity and strategic complexity inherent in the design of such auctions, and discuss how these challenges are addressed in various combinatorial auction formats.
Availability in Stochastic Models
Authors: Mercier Sophie 
Published: January 2011

AbstractA system is considered, which evolves in time according to a stochastic process. The system state space is divided into up‐ and down‐states and the quantities of interest are the system point and asymptotic availabilities, namely the probabilities that the system is in an up‐state at some finite timetand astgoes to infinity. Different stochastic continuous‐time models and associated tools are presented, such as alternating renewal models, Markov and semi‐Markov models, regenerative and Markov regenerative models, with the corresponding renewal or Markov renewal equations fulfilled by the point availability. Classical limit theorems then allow to derive expressions for the asymptotic availability, which involve the process on a generic (Markov) cycle.
Aviation Safety and Security
Authors: Barnett Arnold 
Published: January 2011

AbstractOperations research contributes to decision‐making about aviation safety and security in two distinct ways: (i) it quantifies the risk that safety and security threats pose to air travelers and (ii) it facilitates analyses about which measures to reduce aviation risk are most likely to be beneficial. This introductory article illustrates both kinds of contributions, focusing on passenger jet aviation and using data about safety and security over the last fifty years.
BasicCPTheory: Search
Authors: Michel Laurent , Van Hentenryck Pascal 
Published: January 2011

AbstractConstraint programming (CP) is based on the fundamental idea that the resolution of hard optimization problems is best approached by the combination of a declarative model specification and the writing of a search procedure. The search procedure explores the underlying search space while taking advantage of the pruning derived from the declarative model. The search is a fundamental ingredient to exploit the specificities of problems that are difficult or near impossible to capture through the declarative model. The flexibility it provides lead to marked successes for truly challenging problems in scheduling, bounded program verification, test generation, and distributed systems, to name just a few. This article offers a gentle introduction to search in CP, what it can do, and how it is used.
Basis Reduction Methods
Authors: Pataki Gábor , Tural Mustafa 
Published: January 2011

Abstract

We review lattice based methods to solve integer programming feasibility problems, in particular, the algorithms of Lenstra, and Kannan, and the reformulation methods of Aardal,
et al
. and of Krishnamoorthy and Pataki. The unifying theme in all of them is transforming the problem

urn:x-wiley:9780470400531:media:eorms0093:xm1

where
P
is a polyhedron, into

urn:x-wiley:9780470400531:media:eorms0093:xm2

where the columns of
B
are short, and near orthogonal, that is, they form a reduced basis of the generated lattice, and the choice of
B
and the polyhedron
Q
is specific to each method.

We give simple proofs of the polynomial running time of Lenstra's and Kannan's algorithms under the assumption that the dimension is fixed. We analyze the reformulation methods on knapsack problems with decomposable structure, and more surprisingly, we prove that they solve bounded integer programs with high probability by enumerating only one subproblem.
We include several exercises as well, and we believe that the survey will be suitable to teach a 2–3 class long segment on lattice based methods in a course on Integer Programming.
Batch Arrivals and Service—Single Station Queues
Authors: Jayaraman Raja , Matis Timothy I. 
Published: January 2011

AbstractQueueing models characterized by bulk (or batch, group) arrivals or service performed in batches of either fixed or variable sizes can be commonly found in models of traffic and transportation, complex computer and telecommunication systems, inventory replenishment systems, and many other real‐life applications. In this article, we study single‐server bulk Markovian queueing models, followed by extensions to general distributions for both arrivals and services. We discuss important performance measures in steady state including waiting time characteristics, stochastic ordering properties, approximations, and bounds. We then review methods commonly employed to extend the study of bulk queueing models to more general systems, and present further reading references.
Computational Pool: AnOR—Optimization Point of View
Authors: Dussault Jean‐Pierre , Landry Jean‐François , Mahey Philippe 
Published: January 2011

AbstractComputational and robotic pool has received increasing attention over the past few years. The challenges are important. In this survey, we focus on the computational aspect but nevertheless, acknowledge that the robotic aspect is both, very important and very difficult.Computationally, one may roughly split the tasks into three main aspects: simulation of the physics, execution of the shots, and planning of sequences of shots. We will discuss these aspects from an OR‐optimization point of view. The planning aspect also attracts the interest of the artificial intelligence community, and links between both approaches will be revealed.We will present the state‐of‐the‐art in this new research area, as well as the obstacles that remain before a computer–robotic system actually challenges a human champion.
Call Center Management
Authors: Mehrotra Vijay , Grossman Thomas A. , Samuelson Douglas A. 
Published: January 2011

AbstractA call center is an organization where agents of a company talk on the telephone to customers or potential customers. Call centers are a large global industry that relies heavily on operations research techniques. Inbound systems are driven by random customer call arrivals and need to balance cost and service quality. Call centers are managed using 15–60 min time blocks. For each time block, managers need to execute forecasting (using forecasting models), performance estimation (via analytic models or simulation), and staffing (to determine the right number of agents). Managers then execute shift scheduling (to devise shifts that cut across time blocks) and rostering (to assign individual agents to shifts). Outbound systems, which originate calls, face the analytical challenge of determining when to dial the next call. Blended systems allow managers to shift agents between inbound and outbound calls. Call centers present interesting operational trends and research opportunities, and challenging practitioner issues.
Capacity Planning
Authors: Rajagopalan Sampath 
Published: January 2011

Abstract
This article is an introduction to capacity planning and basic models used to support capacity planning and expansion decisions. The importance of making good capacity decisions by firms is described followed by definitions of various measures of capacity. The main elements of capacity decisions such as size, timing, and location of capacity additions are discussed. The impact of flexibility of capacity and the level of automation on a firm's productivity, the nature of capacity decisions as a function of the production processes, and implications of interactions between capacity levels at firms in a supply chain are discussed. Some standard deterministic and stochastic models in the literature for making capacity decisions are presented. A key insight from continuous time deterministic models is that it is optimal to add capacity at equal time intervals under several demand and capacity cost scenarios. A basic stochastic capacity expansion model is provided that can be transformed to a deterministic model with a different discount rate under fairly general conditions on the demand process. Finally, a stochastic programming model and heuristic solution approaches are described—this is a flexible approach to model capacity expansion decisions when there is uncertainty in both demand and other parameters.
Common Random Numbers
Authors: Schruben Lee W. 
Published: January 2011

AbstractBy far the simplest and most widely used variance reduction technique (VRT) for stochastic simulations is simply to use the same sets of common random number streams (CRNs) to generate random variables when running simulations at different input factor settings. This is the default, or is easily implemented, in many commercial simulation software packages. However, this potentially powerful VRT should be used differently depending on how random variables are generated, how these random variables are used when running the simulation model to generate output data, and how the output data is used in analysis.
Decision Problems and Applications of Operations Research at Marine Container Terminals
Authors: Goodchild Anne , Zhao Wenjuan , Wygonik Erica 
Published: January 2011

AbstractThis article provides an overview of the applications of operations research (OR) techniques to marine container terminals. The article begins by providing a summary of marine container terminal operations. Following this, a series of decision problems are identified, to which OR techniques have been applied. These include quayside, yard, and landside decision problems. In each section, the problems, technical approaches, and outcomes are described. With respect to quayside decision problems, the specific problems addressed include determination of quayside equipment types, berth allocation, quay crane allocation and scheduling, and operational planning and real‐time decisions related to transportation equipment. Yard decision problems include yard design, storage space allocation, and task assignment for yard handling equipment. Landside decision problems include design of the terminal's landside connection, transport planning, and real‐time control of landside operations. The article provides an overview of the state of the practice. The reader will gain an understanding of the nature of decision problems in marine container terminals, how OR techniques have been used to address these, and the operational improvements made possible through the application of OR techniques to this problem area.
Czech Society for Operations Research
Authors: Fiala Petr 
Published: January 2011

Abstract
The article informs about The Czech Society for Operations Research (CSOR). A short history and activities of the CSOR are briefly described. The annual conferences, Mathematical Methods in Economics, are organized by the Czech Society for Operations Research. The main international activity organized by the Czech Society for Operations Research was the 22nd European Conference on Operational Research EURO XXII in 2007.
Descriptive Models of Perceived Risk
Authors: Wang Yitong , Keller L. Robin , Simon Jay 
Published: January 2011

Abstract
This article provides a brief introduction to descriptive models of perceived risk. The first part focuses on monetary risks or any risks whose information can be distilled into payoffs and their probabilities. The second part focuses on psychological research on societal risks such as psychometric models. Some further developments are also included such as cross‐cultural studies and the risk‐as‐feelings hypothesis.
Design and Control Principles of Flexible Workforce in Manufacturing Systems
Authors: Iravani Seyed M. R. 
Published: January 2011

AbstractAgile (flexible) manufacturing, a natural successor to lean manufacturing, emphasizes customized products, efficiency (as pursued through lean manufacturing and other methods for making more with less), and responsiveness for meeting diverse customer requirements in a prompt and personalized manner. Unlike lean manufacturing, which emphasizes mass production in large‐scale corporate structures, agile manufacturing is better suited to smaller scale and modular flexible production facilities. In this environment, flexible resources such as a flexible (cross‐trained) workforce will become increasingly essential. In this article we show how worker cross‐training can improve the performance of manufacturing systems through capacity balancing and variability buffering. We also present effective design and control principles to utilize the potential benefits of a flexible workforce.
Design Considerations for Supply Chain Tracking Systems
Authors: Dai Hongyan , Tseng Mitchell M. , Monsreal Mario M. 
Published: January 2011

AbstractSupply chain tracking systems are becoming imperative to help achieve competitive advantages from efficiency and responsiveness improvement, and comply with social awareness of product safety, security, and sustainability. It is important to design the supply chain tracking system in such a way that it can achieve the desired benefits and minimize the costs at the same time. However, there are various challenges to gain this cost effectiveness with regard to technologies, standards, operations, and strategies. Moreover, there are no complete design principles available at this moment. It is the intent of this paper to provide a framework for designing a cost‐effective supply chain tracking system. We will explain the main considerations for a tracking system design and articulate how to coordinate various considerations to reach the optimal design in an economical way. Ultimately, we want to provide some feasible and practical guidelines to support and simplify the decision making process for building supply chain tracking systems.
Design for Manufacturing and Assembly
Authors: Koraishy Babar M. , Wood Kristin L. , Tilstra Andrew 
Published: January 2011

AbstractDesign for manufacturing and assembly (DFMA) represents a strategic approach implemented by enterprises to ensure simultaneous consideration of the manufacturing processes and assemblability of a product or system as the product or system proceeds from conception to installation, implementation, and use. DFMA applies across an enterprise's organization and structure, from multidisciplinary team formation, the establishment of supply chains, activity‐based costing, to manufacturing and assembly infrastructure, process and quality control, and the actual architectural and design choices for a particular product or system. This article will not attempt to cover this broad spectrum. Instead, our focus entails a study of basic design guidelines, tools, and techniques that can be employed by a designer or design team to improve the manufacturing and assembly associated with a product or system. Specifically, we present the basic constructs of design for manufacturing and design for assembly. Using these constructs as a foundation, we explore a set of practical design guidelines as they relate to both manufacturability and assemblability. A basic DFMA technique, known as effort‐flow analysis, is then presented to analyze and improve a product or system. The knowledge gained from the design guidelines and effort–flow analysis can be used to refine a design so that it is easier to fabricate and easier to assemble. Improvements of these types, in turn, can lead to significant cost savings and reduced failure modes. They can also lead to novel and innovative solutions of products and systems at the component level.
Introduction to Discrete‐Event Simulation
Authors: Ingalls Ricki G. 
Published: January 2011

Abstract
Simulation is a powerful tool if understood and used properly. This introduction is designed to teach the basics of simulation, including structure, function, data generated, and its proper use. The introduction starts with a definition of simulation, goes through a discussion about the elements of a simulation, how the simulation actually works, and how to handle data generated by the simulation. Throughout the article, there is discussion on issues concerning the use of simulation in industry.
The Scatter Search Methodology
Authors: Laguna Manuel , Martí Rafael , Gallego Micael , Duarte Abraham 
Published: January 2011

AbstractScatter search (SS) is an evolutionary approach for optimization. It has been applied to problems with continuous and discrete variables and with a single or multiple objectives. The success of SS as an optimization technique is well documented in a constantly growing number of journal articles and book chapters. This article first focuses on the basic SS framework, which is responsible for most of the outcomes reported in the literature, and then covers advanced elements that have been introduced in a few selected papers, such as the hybridization with tabu search, a well‐known memory‐based metaheuristic. We consider the maximum diversity problem to illustrate the search elements, methods and strategies described here.
Efficient Use of Materials and Energy
Authors: Thomas Valerie M. 
Published: January 2011

Abstract
The efficient use of materials and energy is at the core of energy and environmental challenges. At the company level, environmentally preferable supply chains is a key area in which operations research can make significant contributions. At the global level, key challenges relate to the dynamics of technology, economics, and environmental impacts, including the potential for material and energy efficiency, the relation of technological and economic development to changes in consumption patterns, and the potential for technology to reduce environmental impacts and constraints.
Feature Extraction and Feature Selection: A Survey of Methods in Industrial Applications
Authors: Anzanello Michel J. 
Published: January 2011

AbstractIn industrial processes, the handling of large amounts of data collected by sensors and computers has challenged engineers and practitioners, making feature selection a subject of massive research in academy and industry. The use of redundant, irrelevant, and noisy features (also known as variables) tends to spoil the performance of many statistical tools, leading to unreliable inferences and costly data collection. In this article we present a survey on variable selection methods and applications in the manufacturing and chemometrics field. We deploy these methods in two major classes: variable selection for prediction and for classification. We then briefly suggest future developments on the topic.
Fictitious Play Algorithm
Authors: Zafra Pablo 
Published: January 2011

Abstract

The original fictitious play algorithm is an iterative procedure developed by G. Brown to find an estimate of a solution to two‐person zero‐sum games (also commonly referred to as
matrix games
). The simplicity of the method is very appealing, but the convergence is very slow. Since its development, most of the ensuing research had focused on modifications to speed up convergence rate, identifying other classes of games where the method (or its variants) converges or fails to converge, and implementing the fictitious play concept in solving some applied problems. A complete description of the algorithm is provided, along with an example where a detailed step‐by‐step execution of the first few iterations is illustrated.
Generating Nonhomogeneous Poisson Processes
Authors: Pasupathy Raghu 
Published: January 2011

Abstract
We present an overview of existing methods to generate pseudorandom numbers from homogeneous Poisson processes. We provide three well‐known definitions of the homogeneous Poisson process, present results that form the basis of various existing generation algorithms, and provide algorithm listings. With the intent of guiding users seeking an appropriate algorithm for a given setting, we note the computationally burdensome operations within each algorithm. Our treatment includes one‐dimensional and two‐dimensional homogeneous Poisson processes.