Jones / Tamiz Practical Goal Programming

1. Auflage 2010
ISBN: 978-1-4419-5771-9
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 141, 170 Seiten

Reihe: International Series in Operations Research & Management Science

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.

Autoren/Hrsg.

Jones, Dylan

Tamiz, Mehrdad

Inhaltsverzeichnis

1;Preface;8
2;Contents;12
3;1 History and Philosophy of Goal Programming;15
3.1;1.1 Terminology;16
3.2;1.2 Underlying Philosophies;20
3.2.1;1.2.1 Satisficing;20
3.2.2;1.2.2 Optimising;21
3.2.3;1.2.3 Ordering or Ranking;21
3.2.4;1.2.4 Balancing;22
4;2 Goal Programming Variants;24
4.1;2.1 Generic Goal Programme;24
4.2;2.2 Distance Metric Based Variants;26
4.2.1;2.2.1 Lexicographic Goal Programming;26
4.2.2;2.2.2 Weighted Goal Programming;28
4.2.3;2.2.3 Chebyshev Goal Programming;28
4.3;2.3 Decision Variable and Goal-Based Variants;29
4.3.1;2.3.1 Fuzzy Goal Programming;30
4.3.2;2.3.2 Integer and Binary Goal Programming;33
4.3.3;2.3.3 Fractional Goal Programming;35
5;3 Formulating Goal Programmes;36
5.1;3.1 Formulating Goals and Setting Target Levels;36
5.1.1;3.1.1 Example;37
5.1.2;3.1.2 Resumption of Example;39
5.2;3.2 Variant Choice;40
5.3;3.3 Lexicographic Variant;41
5.3.1;3.3.1 Good Modelling Practice for the Lexicographic Variant;45
5.4;3.4 Weighted Variant;47
5.5;3.5 Normalisation;47
5.5.1;3.5.1 Percentage Normalisation;47
5.5.2;3.5.2 Zero--One Normalisation;49
5.5.3;3.5.3 Euclidean Normalisation;51
5.6;3.6 Preferential Weight Choice;51
5.7;3.7 Chebyshev Variant;54
5.8;3.8 Summary Ten Rules for Avoiding Pitfalls in Goal Programming Formulations;54
5.9;3.9 Exercises;55
5.9.1;Example 1 -- Conversion from Linear to Goal Programming;55
5.9.2;Example 2 -- On-line Retailer;55
5.9.3;Example 3 -- Production Planning;57
5.9.4;Example 4 -- Employee Scheduling;58
5.9.5;Example 5 -- Diet Planning;58
5.9.6;Example 6 -- Travelling Salesperson;60
5.9.7;Example 7 -- Downstream Oil Industry;60
5.9.8;Example 8 -- Macro Economics;60
5.9.9;Example 9 -- Budget Planning;60
5.9.10;Example 10 -- Healthcare Planning;60
6;4 Advanced Topics in Goal Programming Formulation;65
6.1;4.1 Axioms;65
6.2;4.2 Non-standard Preference Function Modelling;66
6.2.1;Type 1: Increase in Per Unit Penalty (Penalty Function);67
6.2.2;Type 2: Decrease in Per Unit Penalty (Reverse Penalty Function);70
6.2.3;Type 3: Single Increase in Penalty (Discontinuity in Preference);71
6.2.4;Type 4: Non-linearity;74
6.2.5;Model Growth;74
6.2.6;Objective Bounds;74
6.2.7;4.2.1 Interval Goal Programming;75
6.2.8;4.2.2 Other Paradigms for Modelling Non-standard Preferences;75
6.3;4.3 Extended Lexicographic Goal Programming;76
6.4;4.4 Meta-goal Programming;78
6.4.1;Example;79
6.5;4.5 Weight Space Analysis;82
6.6;4.6 Exercises;84
7;5 Solving and Analysing Goal Programming Models;88
7.1;5.1 Computerised Solution of Weighted Goal Programming Example;88
7.1.1;5.1.1 Solution via Excel Solver;88
7.1.2;5.1.2 Solution via LINGO;89
7.2;5.2 Computerised Solution of Chebyshev Goal Programming Example;89
7.2.1;5.2.1 Solution via Excel Solver;90
7.2.2;5.2.2 Solution via LINGO;91
7.3;5.3 Computerised Solution of Lexicographic Goal Programming Examples;92
7.3.1;5.3.1 Theory of Solving Lexicographic Goal Programmes;92
7.3.2;5.3.2 Solution via Excel Solver;94
7.3.3;5.3.3 Solution via LINGO;96
7.4;5.4 Solution of Other Goal Programming Variants;98
7.4.1;5.4.1 Fuzzy Goal Programmes;98
7.4.2;5.4.2 Integer and Binary Goal Programmes;98
7.4.3;5.4.3 Non-linear Goal Programmes;99
7.4.4;5.4.4 Meta and Extended Lexicographic Goal Programmes;99
7.5;5.5 Analysis of Goal Programming Results;99
7.6;5.6 Specialist Goal Programming Packages Past and Future;101
7.7;5.7 Exercises;101
8;6 Detection and Restoration of Pareto Inefficiency;106
8.1;6.1 Pareto Definitions;108
8.2;6.2 Pareto Inefficiency Detection;109
8.2.1;6.2.1 Continuous Weighted and Lexicographic Variants;109
8.2.2;6.2.2 Integer and Binary Variants;111
8.3;6.3 Restoration of Pareto Efficiency;113
8.4;6.4 Detection and Restoration of Chebyshev Goal Programmes;117
8.5;6.5 Detection and Restoration of Non-linear Goal Programmes;120
8.6;6.6 Conclusion;121
8.7;6.7 Exercises;121
9;7 Trend of Integration and Combination of Goal Programming;124
9.1;7.1 Goal Programming as a Statistical Tool;124
9.2;7.2 Goal Programming as a Multi-criteria Decision Analysis Tool;125
9.2.1;7.2.1 Goal Programming and Other Distance Metric Based Approaches;126
9.2.2;7.2.2 Goal Programming and Pairwise Comparison Techniques;127
9.2.2.1;7.2.2.1 Using the AHP to Determine Goal Programming Preferential Weights;128
9.2.2.2;7.2.2.2 Using Goal Programming as a Technique to Derive the Weighting Vector in AHP;128
9.2.3;7.2.3 Goal Programming and Other MCDM/A Techniques;130
9.2.3.1;7.2.3.1 Goal Programming and Interactive Methods;130
9.2.3.2;7.2.3.2 Goal Programming and A Posteriori Techniques;131
9.2.3.3;7.2.3.3 Goal Programming and Discrete Choice/Outranking Methods;132
9.3;7.3 Goal Programming and Artificial Intelligence/Soft Computing;132
9.3.1;7.3.1 Goal Programming and Pattern Recognition;132
9.3.2;7.3.2 Goal Programming and Fuzzy Logic;134
9.3.3;7.3.3 Goal Programming and Meta-heuristic Methods;135
9.3.3.1;7.3.3.1 Multi-objective Evolutionary Algorithms;136
9.4;7.4 Goal Programming and Other Operational Research Techniques;136
9.4.1;7.4.1 Goal Programming and Data Envelopment Analysis;137
9.4.2;7.4.2 Goal Programming and Simulation;137
10;8 Case Study: Application of Goal Programming in Health Care;140
10.1;8.1 Context of Application Area;140
10.2;8.2 Initial Goal Programming Models;141
10.2.1;8.2.1 Data Collection;141
10.2.2;8.2.2 Model Description;142
10.2.2.1;8.2.2.1 Assumptions;142
10.2.2.2;8.2.2.2 Decision Variables;143
10.2.2.3;8.2.2.3 Achievement Function;144
10.2.2.4;8.2.2.4 Goals;144
10.2.2.5;8.2.2.5 Constraints;146
10.2.2.6;8.2.2.6 Sign Restrictions;148
10.2.3;8.2.3 Solution and Analysis;148
10.3;8.3 Combined Simulation and Goal Programming Model;149
10.3.1;8.3.1 Further Data collection for the Simulation Model;150
10.3.2;8.3.2 Simulation Model Description;151
10.3.3;8.3.3 Model Refinement, Verification, and Validation;154
10.3.4;8.3.4 What/If Scenario Investigation;154
10.3.4.1;8.3.4.1 Second Consultant Post-Take Ward Round;156
10.3.5;8.3.5 Post-goal Programme;157
10.3.5.1;8.3.5.1 Decision Variables;158
10.3.5.2;8.3.5.2 Data;158
10.3.5.3;8.3.5.3 Algebraic Goal Programming Model;158
10.4;8.4 Conclusions;160
11;9 Case Study: Application of Goal Programming in Portfolio Selection;161
11.1;9.1 Overview of Issues and Objectives in Multi-objective Portfolio Selection;161
11.1.1;9.1.1 Lexicographic Goal Programming in Portfolio Selection Models;163
11.1.2;9.1.2 Chebyshev Goal Programming in Portfolio Selection Models;163
11.1.3;9.1.3 Fuzzy Goal Programming in Portfolio Selection Models;164
11.2;9.2 Multi-phase Portfolio Models;164
11.2.1;9.2.1 The Two-Phase Model of Tamiz et al.;165
11.2.1.1;Phase 1 ;165
11.2.1.2;Phase 2 ;166
11.2.2;9.2.2 The Three-Phase Model of Perez et al.;168
11.2.2.1;Phase 1 ;169
11.2.2.2;Phase 2 ;169
11.2.2.3;Phase 3 ;169
11.3;9.3 Summary;169
12;References;171
13;Index;179