Praise for the Third Edition: "This is one of the best books available. Its excellent organizational structure allows quick reference to specific models and its clear presentation . . . solidifies the understanding of the concepts being presented."IIE Transactions on Operations EngineeringThoroughly revised and expanded to reflect the latest developments in the field, Fundamentals of Queueing Theory, Fourth Edition continues to present the basic statistical principles that are necessary to analyze the probabilistic nature of queues. Rather than presenting a narrow focus on the subject, this update illustrates the wide-reaching, fundamental concepts in queueing theory and its applications to diverse areas such as computer science, engineering, business, and operations research.This update takes a numerical approach to understanding and making probable estimations relating to queues, with a comprehensive outline of simple and more advanced queueing models. Newly featured topics of the Fourth Edition include:Retrial queuesApproximations for queueing networksNumerical inversion of transformsDetermining the appropriate number of servers to balance quality and cost of serviceEach chapter provides a self-contained presentation of key concepts and formulae, allowing readers to work with each section independently, while a summary table at the end of the book outlines the types of queues that have been discussed and their results. In addition, two new appendices have been added, discussing transforms and generating functions as well as the fundamentals of differential and difference equations. New examples are now included along with problems that incorporate QtsPlus software, which is freely available via the book's related Web site.With its accessible style and wealth of real-world examples, Fundamentals of Queueing Theory, Fourth Edition is an ideal book for courses on queueing theory at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners who analyze congestion in the fields of telecommunications, transportation, aviation, and management science.

Fundamentals of Queueing Theory

Introduction to linear and nonlinear programming

This survey reviews the forty-year history of research on transportation revenue management (also known as yield management). We cover developments in forecasting, overbooking, seat inventory control, and pricing, as they relate to revenue management, and suggest future research directions. The survey includes a glossary of revenue management terminology and a bibliography of over 190 references.

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Revenue Management: Research Overview and Prospects

Research on facility location is abundant. However, this research does not typically address the particular conditions that arise when locating facilities to service large-scale emergencies, such as earthquakes, terrorist attacks, etc. In this work we first survey general facility location problems and identify models used to address common emergency situations, such as house fires and regular health care needs. We then analyze the characteristics of large-scale emergencies and propose a general facility location model that is suited for large-scale emergencies. This general facility location model can be cast as a covering model, a P-median model or a P-center model, each suited for different needs in a large-scale emergency. Illustrative examples are given to show how the proposed model can be used to optimize the locations of facilities for medical supplies to address large-scale emergencies in the Los Angeles area. Furthermore, comparison of the solutions obtained by respectively using the proposed mo...

A modeling framework for facility location of medical services for large-scale emergencies

A system (102) for dynamically pricing media content is operatively coupled to one or more clients (108) over a network (106). The system (102) dynamically adjusts pricing of the media content and delivers the media content to the clients (108) that order the media content a dynamically adjusted price. The price can be dynamically adjusted based on profit optimization. Alternatively or additionally, the price can be adjusted based to time between purchases. Further, the system (102) is capable of rewarding institutions for allowing their members to access the system (102).

Digital online exchange

The present invention provides a dynamic pricing system that generates pricing recommendations for each product in each market. In particular, the system normalizes historic pricing and sales data, and then analyzes this historic data using parameters describing the user's business objectives to produce a pricing list to achieve these objectives. The system uses historical market data to forecast expected sales according to a market segment, product type, and a range of future dates and to determine the effects of price changes on the forecasted future sales. The system further calculates unit costs for the product. The system then estimates profits from sales at different prices by using the sales forecasts, adjusting these sales forecasts for changes in prices, and the costs determinations. The system optionally optimizes prices given current and projected inventory constraints and generates alerts notices according to pre-set conditions.

Dynamic pricing system

Spreadsheet engineering adapts the lessons of software engineering to spreadsheets, providing eight principles as a framework for organizing spreadsheet programming recommendations. Spreadsheets raise issues inadequately addressed by software engineering. Spreadsheets are a powerful modeling language, allowing strategic rapid model change, and enabling exploratory modeling. Spreadsheets users learn slowly with experience because they focus on the problem domain not programming. The heterogeneity of spreadsheet users requires a taxonomy to guide recommendations. Deployment of best practices is difficult and merits research.

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Spreadsheet Engineering: A Research Framework

The business school management science course is suffering serious decline. The traditional model-and algorithm-based course fails to meet the needs of MBA programs and students. Poor student mathematical preparation is a reality, and is not an acceptable justification for poor teaching outcomes. Management science Ph.D.s are often poorly prepared to teach in a general management program, having more experience and interest in algorithms than management. The management science profession as a whole has focused its attention on algorithms and a narrow subset of management problems for which they are most applicable. In contrast, MBA's rarely encounter problems that are suitable for straightforward application of management science tools, living instead in a world where problems are ill-defined, data is scarce, time is short, politics is dominant, and rational “decision makers” are non-existent. The root cause of the profession's failure to address these issues seems to be in Russell Ackoff's words a habit of professional introversion that caused the profession to be uninterested in what MBA's really do on the job and how management science can help them.

Causes of the Decline of the Business School Management Science Course

Logistics optimization has significantly grown in popularity over the last few decades. Improvements in computing power, modeling software, and the willingness of companies to invest time in the modeling effort have allowed models that were once too unwieldy to solve to optimality to be solved quickly. This has led to a more wide-spread recognition by logistics managers of the potential advantages of using optimization. The scope of logistics optimization in companies and organizations has expanded to address strategic, tactical, operational, and collaborative decision making. Spreadsheets, an analytical tool familiar to managers, have played a crucial role in the expanded modeling efforts of companies. Although optimization’s role in logistics has grown tremendously, there still are areas that remain to be explored that will allow it to achieve an even larger and more successful role in the management of companies. Additionally, there are some models that are still too large or too complex to currently solve to optimality, despite the advances in computing power and modeling/solving software.

Optimization Modeling for Logistics: Options and Implementations

Process-driven spreadsheet queuing simulation is a better vehicle for understanding queue behavior than queuing theory or dedicated simulation software. Spreadsheet queuing simulation has many pedagogical benefits in a business school end-user modeling course, including developing students intuition, giving them experience with active modeling skills, and providing access to tools. Spreadsheet queuing simulations are surprisingly easy to program, even for queues with balking and reneging. The ease of prototyping in spreadsheets invites thoughtless design, so careful spreadsheet programming practice is important. Spreadsheet queuing simulation is inferior to dedicated simulation software for analyzing queues but is more likely to be available to managers and students.

Thomas A. Grossman

Papers

Dynamic pricing system

Spreadsheet Engineering: A Research Framework

Causes of the Decline of the Business School Management Science Course

Optimization Modeling for Logistics: Options and Implementations

Teachers' Forum: Spreadsheet Modeling and Simulation Improves Understanding of Queues