Practical Goal Programming

By: Dylan Jones, Mehrdad Tamiz

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Hardcover | 22 March 2010

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186 Pages

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22.86 x 15.24 x 1.27

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Practical Goal Programming is intended to allow academics and practitioners to be able to build effective goal programming models, to detail the current state of the art, and to lay the foundation for its future development and continued application to new and varied fields. Suitable as both a text and reference, its nine chapters first provide a brief history, fundamental definitions, and underlying philosophies, and then detail the goal programming variants and define them algebraically. Chapter 3 details the step-by-step formulation of the basic goal programming model, and Chapter 4 explores more advanced modeling issues and highlights some recently proposed extensions.

Chapter 5 then details the solution methodologies of goal programming, concentrating on computerized solution by the Excel Solver and LINGO packages for each of the three main variants, and includes a discussion of the viability of the use of specialized goal programming packages. Chapter 6 discusses the linkages between Pareto Efficiency and goal programming. Chapters 3 to 6 are supported by a set of ten exercises, and an Excel spreadsheet giving the basic solution of each example is available at an accompanying website.

Chapter 7 details the current state of the art in terms of the integration of goal programming with other techniques, and the text concludes with two case studies which were chosen to demonstrate the application of goal programming in practice and to illustrate the principles developed in Chapters 1 to 7. Chapter 8 details an application in healthcare, and Chapter 9 describes applications in portfolio selection.

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You Can Find This Book In

Non-Fiction Business & Management Operational Research Mathematics Optimisation Linear Programming Sales & Marketing Management & Management Techniques Management of Specific Areas Production of Quality Control Management Engineering & Technology Technology in General

Maths for Engineers Business Mathematics & Systems Applied Mathematics Mathematical Modelling Computing & I.T.Business Applications Management Decision Making

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Published: 3rd May 2012

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History and Philosophy of Goal Programming	p. 1
Terminology	p. 2
Underlying Philosophies	p. 6
Satisficing	p. 6
Optimising	p. 7
Ordering or Ranking	p. 7
Balancing	p. 8
Goal Programming Variants	p. 11
Generic Goal Programme	p. 11
Distance Metric Based Variants	p. 13
Lexicographic Goal Programming	p. 13
Weighted Goal Programming	p. 15
Chebyshev Goal Programming	p. 15
Decision Variable and Goal-Based Variants	p. 16
Fuzzy Goal Programming	p. 17
Integer and Binary Goal Programming	p. 20
Fractional Goal Programming	p. 22
Formulating Goal Programmes	p. 23
Formulating Goals and Setting Target Levels	p. 23
Example	p. 24
Resumption of Example	p. 26
Variant Choice	p. 28
Lexicographic Variant	p. 28
Good Modelling Practice for the Lexicographic Variant	p. 32
Weighted Variant	p. 34
Normalisation	p. 34
Percentage Normalisation	p. 34
Zero-One Normalisation	p. 36
Euclidean Normalisation	p. 38
Preferential Weight Choice	p. 39
Chebyshev Variant	p. 41
Summary – Ten Rules for Avoiding Pitfalls in Goal Programming Formulations	p. 42
Exercises	p. 42
Advanced Topics in Goal Programming Formulation	p. 53
Axioms	p. 53
Non-standard Preference Function Modelling	p. 54
Interval Goal Programming	p. 63
Other Paradigms for Modelling Non-standard Preferences	p. 63
Extended Lexicographic Goal Programming	p. 64
Meta-goal Programming	p. 66
Weight Space Analysis	p. 70
Exercises	p. 72
Solving and Analysing Goal Programming Models	p. 77
Computerised Solution of Weighted Goal Programming Example	p. 77
Solution via Excel Solver	p. 77
Solution via LINGO	p. 78
Computerised Solution of Chebyshev Goal Programming Example	p. 79
Solution via Excel Solver	p. 79
Solution via LINGO	p. 80
Computerised Solution of Lexicographic Goal Programming Examples	p. 81
Theory of Solving Lexicographic Goal Programmes	p. 81
Solution via Excel Solver	p. 83
Solution via LINGO	p. 85
Solution of Other Goal Programming Variants	p. 87
Fuzzy Goal Programmes	p. 87
Integer and Binary Goal Programmes	p. 87
Non-linear Goal Programmes	p. 88
Meta and Extended Lexicographic Goal Programmes	p. 88
Analysis of Goal Programming Results	p. 89
Specialist Goal Programming Packages – Past and Future	p. 90
Exercises	p. 91
Detection and Restoration of Pareto Inefficiency	p. 95
Pareto Definitions	p. 97
Pareto Inefficiency Detection	p. 98
Continuous Weighted and Lexicographic Variants	p. 98
Integer and Binary Variants	p. 100
Restoration of Pareto Efficiency	p. 102
Detection and Restoration of Chebyshev Goal Programmes	p. 106
Detection and Restoration of Non-linear Goal Programmes	p. 109
Conclusion	p. 110
Exercises	p. 110
Trend of Integration and Combination of Goal Programming	p. 113
Goal Programming as a Statistical Tool	p. 113
Goal Programming as a Multi-criteria Decision Analysis Tool	p. 114
Goal Programming and Other Distance Metric Based Approaches	p. 115
Goal Programming and Pairwise Comparison Techniques	p. 116
Goal Programming and Other MCDM/A Techniques	p. 119
Goal Programming and Artificial Intelligence/Soft Computing	p. 121
Goal Programming and Pattern Recognition	p. 121
Goal Programming and Fuzzy Logic	p. 123
Goal Programming and Meta-heuristic Methods	p. 124
Goal Programming and Other Operational Research Techniques	p. 125
Goal Programming and Data Envelopment Analysis	p. 126
Goal Programming and Simulation	p. 126
Case Study: Application of Goal Programming in Health Care	p. 129
Context of Application Area	p. 129
Initial Goal Programming Models	p. 130
Data Collection	p. 130
Model Description	p. 131
Solution and Analysis	p. 137
Combined Simulation and Goal Programming Model	p. 138
Further Data collection for the Simulation Model	p. 139
Simulation Model Description	p. 140
Model Refinement, Verification, and Validation	p. 143
What/If Scenario Investigation	p. 143
Post-goal Programme	p. 146
Conclusions	p. 149
Case Study: Application of Goal Programming in Portfolio Selection	p. 151
Overview of Issues and Objectives in Multi-objective Portfolio Selection	p. 151
Lexicographic Goal Programming in Portfolio Selection Models	p. 153
Chebyshev Goal Programming in Portfolio Selection Models	p. 153
Fuzzy Goal Programming in Portfolio Selection Models	p. 154
Multi-phase Portfolio Models	p. 155
The Two-Phase Model of Tamiz et al.	p. 155
The Three-Phase Model of Perez et al.	p. 158
Summary	p. 160
References	p. 161
Index	p. 169
Table of Contents provided by Ingram. All Rights Reserved.

Practical Goal Programming

At a Glance

Hardcover

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