Applied Linear Programming

For the Socioeconomic and Environmental Sciences

1st Edition - January 1, 1978
Author: Michael R. Greenberg
Editor: J. William Schmidt
Language: English
eBook ISBN:
9 7 8 - 1 - 4 8 3 2 - 7 3 7 4 - 7

Applied Linear Programming for the Socioeconomic and Environmental Sciences discusses applications of linear and related programming to help in the transformation of the student or… Read more

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Applied Linear Programming for the Socioeconomic and Environmental Sciences discusses applications of linear and related programming to help in the transformation of the student or reader from book learning to computer use. The author reviews the theory, methods and applications of linear programming. The author also presents some programming codes that can be used in solving linear programming problems. He describes processes such as parametric programming, sensitivity analysis, and postoptimal analysis. The author lists five possible applications of linear programming, as follows: 1) estimates involving supply of and demand for services; 2) transport and schedule planning; 3) scale, technologies, and optimal site selection; (4) evaluation of impact of activates; and 5) evaluation of alternative options. The author cites a case study of solid-waste management in New Jersey that is common to other areas: availability of disposal sites, increasing amounts of garbage, and stricter environmental regulations. This book can be appreciated by environmentalist, sociologists, economists, civil engineers, and students and professors of advance mathematics and linear programming.

Preface

Acknowledgments

Part One Theory and Methods

Chapter 1 Theory, Methods, and Applications

A. Optimal Behavior

B. Mathematical Conditions for a Solution

C. An Illustration of Different Solutions

D. The Corner Point, the Optimal Solution, and the Computer

E. Applications : Historical and Recent Perspectives

F. Summary

References

Chapter 2 Algebraic Methods for Solving Linear Programming Problems

A. Essential Matrix Methods

B. Mathematical Solution of Linear Programming Problems

C. Summary

References

Chapter 3 Beyond the Global Optimum

A. Postoptimal Analysis

B. Dealing with Integers and Nonlinearities

C. Branch and Bound and Backtrack Search Procedures for Solving More Complex Programming Problems

References

Chapter 4 Computer Algorithms

A. Introduction to MPS

B. Card Layout

C. Parametric Programming and Sensitivity Analysis

D. Interpretation of the Printout

E. Postoptimal Analysis

F. Parametric Programming Output

G. Summary

Part Two Applications

Chapter 5 Mathematical Models for Policy Analysis

A. An Introduction to Models

B. Mathematical Models Used in the Socioeconomic and Environmental Sciences

C. Guidelines for Using Mathematical Programming

References

Chapter 6 Solid Waste

A. The Problems

B. Fixed-Facility Algorithms

C. Collection Algorithms

D. A Fixed-Facility Example

E. Summary

References

Chapter 7 Water Resources Management

A. Water Quantity

B. Water Quality

C. Sample Problems

D. Summary

References

Chapter 8 Health, Education, and Law Enforcement

A. Allocation of People

B. Allocation of Financial Resources

C. Capital Allocation

D. Sample Problems

E. Summary

References

Chapter 9 Intraregional Land Use and Transportation

A. Place Utility—Location Theory

B. Criticisms of Large-Scale Land-Use Optimization Models

C. Problem-Oriented Land-Use Applications

D. Sample Problems

E. Summary

References

Chapter 10 Economic Development and Transportation

A. Economic Development: The Transportation and Transshipment Problems

B. Economic Development and Classical Applications of the Transportation Problem

C. New Applications of Mathematical Programming for Studying Transportation and Economic Development

D. The Gravity Model and Linear Programming

E. Sample Problem

F. Summary

References

Chapter 11 A Linear Programming Case Study

A. Stating the Problem—Northeastern New Jersey's Solid-Waste Problem

B. Designing the Facility Siting Research Problem

C. Applying the Linear Programming Model and Interpreting the Results

D. Summary

References

Index