Jones / Tamiz / Ries New Developments in Multiple Objective and Goal Programming

1. Auflage 2010
ISBN: 978-3-642-10354-4
Verlag: Springer
Format: PDF
Kopierschutz: Wasserzeichen (»Systemvoraussetzungen)

E-Book, Englisch, 163 Seiten

This volume shows the state-of-the-art in both theoretical development and application of multiple objective and goal programming. Applications from the fields of supply chain management, financial portfolio selection, financial risk management, insurance, medical imaging, sustainability, nurse scheduling, project management, water resource management, and the interface with data envelopment analysis give a good reflection of current usage. A pleasing variety of techniques are used including models with fuzzy, group-decision, stochastic, interactive, and binary aspects. Additionally, two papers from the upcoming area of multi-objective evolutionary algorithms are included. The book is based on the papers of the 8th International Conference on Multi-Objective and Goal Programming (MOPGP08) which was held in Portsmouth, UK, in September 2008. TOC:Multi-objective Stochastic Programming Approaches for Supply Chain Management.- A Review of Goal Programming for Portfolio Selection.- A Hypervolume-Based Multiobjective Optimizer for High-Dimensional Objective Spaces.- Minimizing Vector Risk Measures.- Multicriteria Programming Approach to Development Project Design with an Output Goal and a Sustainability Goal.- Automated Aggregation and Omission of Objectives for Tackling Many-Objective Problems.- Fuzzy Group Decision Making and Its Application in Water Resource Planning and Management.- Trade-off Analysis in Discrete Decision Making Problems Under Risk.- Interactive Multiobjective Optimization for 3D HDR Brachytherapy Applying IND-NIMBUS.- Multicriteria Ranking Using Weights Which Minimize the Score Range.- In Search of a European Paper Industry Ranking in Terms of Sustainability by Using Binary Goal Programming.- Nurse Scheduling by Fuzzy Goal Programming.

Autoren/Hrsg.

Jones, Dylan

Tamiz, Mehrdad

Ries, Jana

Inhaltsverzeichnis

1;Preface;5
2;Contents;7
3;List of Contributors;9
4;Multi-Objective Stochastic Programming Approaches for Supply Chain Management;11
4.1;1 Introduction;11
4.2;2 Problem Description;13
4.3;3 Multi-Objective Techniques;15
4.3.1;3.1 Goal Attainment Technique;16
4.3.2;3.2 STEM Method;17
4.3.3;3.3 Surrogate Worth Trade-Off (SWT) Method;19
4.4;4 Numerical Experiments;20
4.5;5 Conclusion;23
4.6;References;23
5;A Review of Goal Programming for Portfolio Selection;25
5.1;1 Introduction;25
5.2;2 The Use of Multi-Criteria Decision Analysis in Portfolio Selection and the Importance of Goal Programming;26
5.3;3 Portfolio Selection Using Goal Programming: Theoretical and Practical Developments;29
5.4;4 Goal Programming Variants for Portfolio Selection;32
5.4.1;4.1 Weighted Goal Programming in Portfolio Selection Models;32
5.4.2;4.2 Lexicographic Goal Programming in Portfolio Selection Models;32
5.4.3;4.3 MINMAX (Chebyshev) Goal Programming in Portfolio Selection Models;33
5.4.4;4.4 Fuzzy Goal Programming in Portfolio Selection Models;33
5.5;5 Performance Measurement for Portfolios;35
5.6;6 Goal Programming and Portfolio Analysis: Other Issues;35
5.6.1;6.1 Issues Concerning Multi-Period Returns;36
5.6.2;6.2 Issues Concerning Extended Factors;37
5.6.3;6.3 Issues Concerning the Measurement of Risk;38
5.7;7 Conclusions;40
5.8;References;40
6;A Hypervolume-Based Optimizer for High-Dimensional Objective Spaces;44
6.1;1 Motivation;44
6.2;2 Related Work;46
6.3;3 HypE: Hypervolume Estimation Algorithm for Multiobjective Optimization;47
6.3.1;3.1 Algorithm;48
6.3.2;3.2 Basic Scheme for Mating Selection;49
6.3.3;3.3 Extended Scheme for Environmental Selection;51
6.3.4;3.4 Estimating the Fitness Values Using Monte Carlo Sampling;54
6.4;4 Experiments;55
6.4.1;4.1 Experimental Setup;55
6.4.2;4.2 Results;56
6.5;5 Conclusion;60
6.6;References;61
7;Minimizing Vector Risk Measures;64
7.1;1 Introduction;64
7.2;2 Dealing with Vector Risk Functions;66
7.3;3 Saddle Point Optimality Conditions;69
7.4;4 Applications;75
7.4.1;4.1 Portfolio Choice;75
7.4.2;4.2 Optimal Reinsurance;75
7.5;5 Conclusions;76
7.6;References;77
8;Multicriteria Programming Approach to Development Project Design with an Output Goal and a Sustainability Goal;79
8.1;1 Introduction;79
8.2;2 Methodology;80
8.2.1;2.1 First Objective: Output Maximization;81
8.2.2;2.2 Second Objective: Sustainability;82
8.2.3;2.3 Compromise Solution;82
8.2.4;2.4 Feedback;83
8.3;3 An Illustrative Example;83
8.3.1;3.1 First Objective: Output Maximization (Unrelatedto the Pattern);84
8.3.2;3.2 Second Objective: Sustainability (Related to the Pattern);84
8.3.3;3.3 Compromise Solution and Final Solution on the Frontier;85
8.3.4;3.4 Comparison of Results;86
8.4;4 Concluding Remarks;86
8.5;References;87
9;Automated Aggregation and Omission of Objectives for Tackling Many-Objective Problems;88
9.1;1 Introduction;88
9.2;2 Objective Reduction by Aggregating Objectives;89
9.3;3 A Greedy Heuristic for Finding the Best Aggregation;93
9.3.1;3.1 Main Procedure;93
9.3.2;3.2 Optimally Aggregating Two Objectives;93
9.4;4 Experimental Validation;97
9.4.1;4.1 The Influence of Different Weight Choices Within the Optimal Interval;98
9.4.2;4.2 Comparison Between Aggregation and Omission;99
9.4.3;4.3 Objective Reduction During Search;101
9.5;5 Application to a Real-World Problem;106
9.6;6 Conclusions;107
9.7;References;108
10;Trade-Off Analysis in Discrete Decision Making Problems Under Risk;110
10.1;1 Introduction;110
10.2;2 Formulation of the Problem;112
10.3;3 Point-to-Point Trade-Offs;113
10.4;4 The Procedure;115
10.5;5 Numerical Example;118
10.6;6 Conclusions;121
10.7;References;122
11;Interactive Multiobjective Optimization for 3D HDR Brachytherapy Applying IND-NIMBUS;123
11.1;1 Introduction;123
11.2;2 Methods;125
11.2.1;2.1 Dose Calculation;125
11.2.2;2.2 Objective Function Formulation;126
11.2.3;2.3 Multiobjective Optimization;127
11.2.3.1;2.3.1 Multiobjective Optimization Problem;127
11.2.3.2;2.3.2 The Interactive Multiobjective Optimization Method NIMBUS;128
11.3;3 Results;129
11.3.1;3.1 Problem Settings;129
11.3.2;3.2 Fletcher–Suit Applicator Example;130
11.3.2.1;3.2.1 Interactive Solution Process;130
11.3.2.2;3.2.2 Comparison and Discussion;133
11.4;4 Conclusions;135
11.5;References;136
12;Multicriteria Ranking Using Weights Which Minimizethe Score Range;138
12.1;1 Introduction;138
12.2;2 Geometric Representation;139
12.3;3 Can the Maximin Approach Produce a SingleWinning Alternative?;143
12.4;4 Criteria Can be Completely Ignored;143
12.5;5 Conclusion;143
12.6;References;144
13;In Search of a European Paper Industry Ranking in Terms of Sustainability by Using Binary Goal Programming;146
13.1;1 Introduction;146
13.2;2 Sustainability Indicators;147
13.3;3 Methodology;150
13.4;4 Results and Conclusions;152
13.5;References;153
14;Nurse Scheduling by Fuzzy Goal Programming;155
14.1;1 Introduction;155
14.2;2 Problem Statement: The Case Study;156
14.2.1;2.1 Legal and Policy Restrictions;157
14.2.2;2.2 Nurses' Preferences;157
14.3;3 Fuzzy Goal Programming;158
14.4;4 The Fuzzy Goal Programming Model for Scheduling Model;160
14.4.1;4.1 Hard Constraints;160
14.4.2;4.2 Fuzzy Goals;163
14.5;5 Results and Discussion;165
14.6;6 Concluding Remarks;166
14.7;References;167