Cost and Optimization in Government

By Aman Khan | Go to book overview

to imprecise decision. Another important point to keep in mind is that decision makers can waste a lot of time on elements of a problem that, in retrospect, may not be quite as important. Ideally, if the decision makers could have only one factor to consider instead of a handful, much unnecessary work could be avoided. Therefore, in constructing a hierarchy the objective should be to keep the number as few as possible. This will not only save time but will also improve the quality of solution.

Finally, while linear, integer, and dynamic programming models are useful when one has a single overriding goal or objective, goal programming is much better suited to problems that have more than one goal or objective. Although it uses the same simplex method of solution as linear programming, its formulation is quited different. As noted earlier, instead of attempting to maximize or minimize the objective function directly, as in linear programming, goal programming attempts to minimize the deviations from goals sequentially, thus allowing for both scalar and ordinal ranking. This gives the decision makers some flexibility by allowing them to identify a cut-off point for the solution that will be acceptable to them.


Notes
1.
L. D. Miles, Techniques of Value Analysis and Engineering. New York, NY: McGraw-Hill, 1961.
2.
G. A. Miller, "The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information," The Psychological Review, 63 ( 1956): 81- 97.
3.
T. L. Saaty, The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation. New York, NY: McGraw-Hill, 1980.
4.
A. Charnes and W. W. Cooper, Management Models and Industrial Applications of Linear Programming. Englewood-Cliffs, NJ: John Wiley and Sons, 1961.
5.
S. M. Lee, Goal Programming for Decision Analysis. Philadelphia, PA: Auerbach Publishing, 1972.
6.
J.P Ignizio, Goal Programming and Extension. Lexington, MA: Lexington Books, 1976.
7.
R. E. Steuer, Multiple Criteria Optimization: Theory, Computation, and Application. New York, NY: John Wiley & Sons, 1986.
8.
T. L. Saaty and L. G. Vargas, Prediction, Projection, and Forecasting. Boston, MA: Kluwer Academic Publishers, 1991.

-305-

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Cost and Optimization in Government
Table of contents

Table of contents

  • Title Page iii
  • Contents vii
  • Preface ix
  • Acknowledgments xii
  • Chapter 1 Basic Cost Concepts 1
  • Chapter 2 Cost Behavior 23
  • Notes 59
  • Chapter 3 Cost Analysis 60
  • Notes 105
  • Chapter 4 Cost Accounting 106
  • Notes 146
  • Chapter 5 Classical Optimization 148
  • Chapter 5 Classical Optimization 148
  • Notes 190
  • Chapter 6 Network Analysis 191
  • Notes 213
  • Chapter 7 Mathematical Programming 215
  • Notes 251
  • Chapter 8 - Games and Decisions 253
  • Notes 279
  • Chapter 9 Multicriteria Analysis 281
  • Chapter 9 Multicriteria Analysis 281
  • Notes 305
  • Chapter 10 Productivity Measurement 346
  • Chapter 11 Quality Control 348
  • Notes 373
  • Chapter 12 Besides Cost and Optimization 375
  • Notes 381
  • Bibliography 383
  • Index 389
  • About the Author 395
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