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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.
Goal Programming Applications in Accounting 74 Goal Programming Applications in Agriculture 76 Goal Programming Applications in Economics 78 Goal Programming Applications in Engineering 79 Goal Programming Applications in Finance 80 Goal Programming Applications in Government 83 Goal Programming Applications in an International Context 88 Goal Programming Applications in Management 90 Goal Programming Applications in Marketing 97 Summary 98 CHAPTER 5. FUTURE TRENDS IN GOAL PROORAMMING 101 GP is Positioned for Growth 101 Shifting the Life Cycle of GP Research to Growth 103 Summary 107 Reference 108 APPENDIX A TEXTBOOKS, READINGS BOOKS AND MONOORAPHS ON GOAL PROORAMMING 109 APPENDIX B. JOURNAL RESEARCH PUBLICATIONS ON GOAL PROORAMMING 113 INDEX 213 viii LIST OF FIGURES Figure 1-1. Summary Relationship of GP with MS/OR and MCDM Figure 1-2. Frequency Distribution for GP Journal Publications Figure 1-3. Life Cycle ofGP Research Figure 2-1. Set of GP Efficient Solutions Figure 5-1. Life Cycle of GP Research ix LIST OF TABLES Table 1-1. MS/OR Topics and Their Related GP Topics Table 1-2. MCDM Subareas and Their Related GP Topics Table 1-3. Frequency Listing ofGP Journal Publications and Book Titles Table 2-1. Solutions for a Dominated GP Problem Table 2-2. Conversion ofLP Constraints to Goal Constraints Table 2-3. GP Citations on Dominance, Inferiority and Inefficiency Table 2-4. GP Citations on Relative Weighting, Prioritization and Incommensurability Table 2-5. MS/OR Topics and Their Related GP Topics Table 3-1. Citations on WeightedlPreemptive GP Methodology Table 3-2. Citations on Pure/Mixed Integer GP Methodology Table 3-3.
Goal Programming (GP) is perhaps the oldest and most widely used approach within the Multiple Criteria Decision Making (MCDM) paradigm. GP combines the logic of optimisation in mathematical programming with the decision maker's desire to satisfy several goals. The primary purpose of this book is to identify the critical issues in GP and to demonstrate different procedures capable of avoiding or mitigating the inherent pitfalls associated with these issues. The outcome of a search of the literature shows many instances where GP models produced misleading or even erroneous results simply because of a careless formulation of the problem. Rather than being in itself a textbook, Critical Issues in Goal Programming is designed to complement existing textbooks. It will be useful to students and researchers with a basic knowledge of GP as well as to those interested in building GP models which analyse real decision problems.
Goal Programming Applications in Accounting 74 Goal Programming Applications in Agriculture 76 Goal Programming Applications in Economics 78 Goal Programming Applications in Engineering 79 Goal Programming Applications in Finance 80 Goal Programming Applications in Government 83 Goal Programming Applications in an International Context 88 Goal Programming Applications in Management 90 Goal Programming Applications in Marketing 97 Summary 98 CHAPTER 5. FUTURE TRENDS IN GOAL PROORAMMING 101 GP is Positioned for Growth 101 Shifting the Life Cycle of GP Research to Growth 103 Summary 107 Reference 108 APPENDIX A TEXTBOOKS, READINGS BOOKS AND MONOORAPHS ON GOAL PROORAMMING 109 APPENDIX B. JOURNAL RESEARCH PUBLICATIONS ON GOAL PROORAMMING 113 INDEX 213 viii LIST OF FIGURES Figure 1-1. Summary Relationship of GP with MS/OR and MCDM Figure 1-2. Frequency Distribution for GP Journal Publications Figure 1-3. Life Cycle ofGP Research Figure 2-1. Set of GP Efficient Solutions Figure 5-1. Life Cycle of GP Research ix LIST OF TABLES Table 1-1. MS/OR Topics and Their Related GP Topics Table 1-2. MCDM Subareas and Their Related GP Topics Table 1-3. Frequency Listing ofGP Journal Publications and Book Titles Table 2-1. Solutions for a Dominated GP Problem Table 2-2. Conversion ofLP Constraints to Goal Constraints Table 2-3. GP Citations on Dominance, Inferiority and Inefficiency Table 2-4. GP Citations on Relative Weighting, Prioritization and Incommensurability Table 2-5. MS/OR Topics and Their Related GP Topics Table 3-1. Citations on WeightedlPreemptive GP Methodology Table 3-2. Citations on Pure/Mixed Integer GP Methodology Table 3-3.
Other publications that exist on this topic, are mainly focused on the general aspects and methodologies of the field and do not refer extensively to bank ALM. On the other hand the existing books on goal programming techniques do not involve the ALM problem and more specifically the bank ALM one. Therefore, there is a lack in the existing literature of a comprehensive text book that combines both the concepts of bank ALM and goal programming techniques and illustrates the contribution of goal programming techniques to bank ALM. This is the major contributing feature of this book and its distinguishing characteristic as opposed to the existing literature. This volume would be suitable for academics and practitioners in operations research, management scientists, financial managers, bank managers, economists and risk analysts. The book can also be used as a textbook for graduate courses of asset liability management, financial risk management and banking risks.
The book is dedicated to multi-objective methods in decision making. The first part which is devoted to theoretical aspects, covers a broad range of multi-objective methods such as multiple linear programming, vector optimisation, fuzzy goal programming, data envelopment analysis, game theory, and dynamic programming. The reader who is interested in practical applications, will find in the remaining parts a variety of approaches applied in numerous fields including production planning, logistics, marketing, and finance.
The book is dedicated to multi-objective methods in decision making. The first part which is devoted to theoretical aspects, covers a broad range of multi-objective methods such as multiple linear programming, vector optimisation, fuzzy goal programming, data envelopment analysis, game theory, and dynamic programming. The reader who is interested in practical applications, will find in the remaining parts a variety of approaches applied in numerous fields including production planning, logistics, marketing, and finance.
Goal programming is one of the most widely used methodologies in operations research and management science, and encompasses most classes of multiple objective programming models. Ignizio provides a concise and lucid overview of (a) the linear goal programming model, (b) a computationally efficient algorithm for solution, (c) duality and sensitivity analysis and (d) extensions of the methodology to integer as well as non-linear models.
Most real-life problems involve making decisions to optimally achieve a number of criteria while satisfying some hard or soft constraints. In this book several methods for solving such problems are presented by the leading experts in the area. The book also contains a number of very interesting application papers which demonstrate theoretical modelling, analysing and solution of real-life problems.