Includes bibliographical references and index.

Network and Discrete Location: Models, Algorithms, and Applications; Contents; Preface to the First and Second Editions; Acknowledgments; 1. Introduction to Location Theory and Models; 1.1 Introduction; 1.2 Key Questions Addressed by Location Models; 1.3 Example Problem Descriptions; 1.3.1 Ambulance Location; 1.3.2 Siting Landfills for Hazardous Wastes; 1.3.3 Summary; 1.4 Key Dimensions of Location Problems and Models; 1.4.1 Planar Versus Network Versus Discrete Location Models; 1.4.2 Tree Problems Versus General Graph Problems; 1.4.3 Distance Metrics; 1.4.4 Number of Facilities to Locate.

1.4.5 Static Versus Dynamic Location Problems1.4.6 Deterministic Versus Probabilistic Models; 1.4.7 Single- Versus Multiple-Product Models; 1.4.8 Private Versus Public Sector Problems; 1.4.9 Single- Versus Multiple-Objective Problems and Models; 1.4.10 Elastic Versus Inelastic Demand; 1.4.11 Capacitated Versus Uncapacitated Facilities; 1.4.12 Nearest Facility Versus General Demand Allocation Models; 1.4.13 Hierarchical Versus Single-Level Models; 1.4.14 Desirable Versus Undesirable Facilities; 1.5 ATaxonomy of Location Models; 1.5.1 Typology of Location Models; 1.5.2 A Simple Analytic Model.

1.6 SummaryExercises; 2. Review of Linear Programming; 2.1 Introduction; 2.2 The Canonical Form of a Linear Programming Problem; 2.3 Constructing the Dual of an LP Problem; 2.4 Complementary Slackness and the Relationships Between the Primal and the Dual Linear Programming Problems; 2.5 Solving a Linear Programming Problem in Excel; 2.6 The Transportation Problem; 2.7 The Shortest Path Problem; 2.7.1 The Shortest Path Problem in Excel; 2.7.2 The Shortest Path Problem in AMPL; 2.8 The Out-of-Kilter Flow Algorithm; 2.9 Integer Programming Problems; 2.10 Summary; Exercises.

3. An Overview of Complexity Analysis3.1 Introduction; 3.2 Basic Concepts and Notation; 3.3 Example Computation of an Algorithm's Complexity; 3.4 The Classes P and NP (and NP-Hard and NP-Complete); 3.5 Summary; Exercises; 4. Covering Problems; 4.1 Introduction and the Notion of Coverage; 4.2 The Set Covering Model; 4.3 Applications of the Set Covering Model; 4.4 Variants of the Set Covering Location Model; 4.5 The Maximum Covering Location Model; 4.5.1 The Greedy Adding Algorithm: A Heuristic Algorithm for Solving the Maximum Covering Location Model.

4.5.2 Lagrangian Relaxation: An Optimization-Based Heuristic Algorithm for Solving the Maximum Covering Location Model4.5.3 Other Solution Approaches and Example Results; 4.6 An Interesting Model Property or It Ain't Necessarily So; 4.7 The Maximum Expected Covering Location Model; 4.8 Summary; Exercises; 5. Center Problems; 5.1 Introduction; 5.2 Vertex P-Center Formulation; 5.3 The Absolute 1- and 2-Center Problems on a Tree; 5.3.1 Absolute 1-Center on an Unweighted Tree; 5.3.2 Absolute 2-Centers on an Unweighted Tree; 5.3.3 Absolute 1-Center on a Weighted Tree.

This Second Edition remains the only hands-on guide to using and developing facility location models. It offers a practice-oriented introduction to model-building methods and solution algorithms complete with software for solving classical problems of realistic size and end-of-chapter exercises to enhance reader understanding. The book introduces readers to the key classical location problems (covering, center, median, and fixed charge); discusses real-world extensions of the basic models used in locating; outlines a host of methodological tools for solving location models; and much more.

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