Showing papers on "Goal programming published in 1998"

Multiple Criteria Decision Support in Engineering Design

Abstract: 1. Introduction.- 1.1 What is Multiple Criteria Decision Making.- 1.2 Relevance of MCDM to Engineering Design.- 1.2.1 The Structure of a Design Problem.- 1.2.2 The Principal Issues in Multiple Criteria Decision Making.- 1.2.3 Issues of Complexity, Subjectivity and Uncertainty.- 1.3 Design Selection vs Design Synthesis.- 1.4 Outline of the Book.- 2. MCDM and The Nature of Decision Making in Design.- 2.1 Introduction.- 2.2 Pareto Optimality: What are the Options?.- 2.3 MCDM Methods and Some Key Terminology.- 2.4 Concluding Comments.- 3. Multiple Attribute Decision Making.- 3.1 Problem Formulations and Method Classification.- 3.1.1 MADM Problems.- 3.1.2 Classification of MADM Methods.- 3.2 Techniques for Weight Assignment.- 3.2.1 Direct Assignment.- 3.2.2 Eigenvector Method.- 3.2.3 Entropy Method.- 3.2.4 Minimal Information Method.- 3.2.4.1 General Pairwise Comparisons and Minimal Information.- 3.2.4.2 Linear Programming Models for Weight Assignment.- 3.2.4.3 An Example.- 3.3 Typical MADM Methods and Applications.- 3.3.1 AHP Method and Application.- 3.3.2 UTA Method and Application.- 3.3.3 TOPSIS Method and Application.- 3.3.4 CODASID Method and Applications.- 3.3.4.1 Information Requirement and Normalization.- 3.3.4.2 New Concordance and Discordance Analyses.- 3.3.4.3 Preference Matrix and CODASID Algorithm.- 3.3.4.4 Applications.- 3.3.5 Comments.- 3.4 A Hierarchical Evaluation Process.- 3.4.1 Design Decision Problems with Subjective Factors.- 3.4.2 A Hierarchical Evaluation Process.- 3.4.3 The Ship Choice Problem.- 3.5 Concluding Comments.- 4. Multiple Objective Decision Making.- 4.1 Multiobjective Optimisation and Method Classification.- 4.1.1 Multiobjective Optimisation and Utility Functions.- 4.1.2 Classification of MODM Methods.- 4.2 Techniques for Single-Objective Optimisation.- 4.2.1 Optimality Conditions.- 4.2.2 Sequential Linear Programming.- 4.2.3 Penalty Methods.- 4.3 Typical MODM Methods.- 4.3.1 Goal Programming.- 4.3.2 Geoffrion's Method.- 4.3.3 Minimax Method.- 4.3.4 ISTM Method.- 4.3.5 Local Utility Function Method.- 4.4 Multiobjective Ship Design.- 4.4.1 A Nonlinear Preliminary Ship Design Model.- 4.4.2 Generation of Subsets of Efficient Ship Designs.- 4.4.3 Progressive Design.- 4.4.4 Design by Setting Target Values.- 4.4.5 Adaptive and Compromise Design.- 4.5 Concluding Comments.- 5. Multiple Criteria Decision Making and Genetic Algorithms.- 5.1 Introduction.- 5.2 The Mechanics of the Simple Genetic Algorithm.- 5.2.1 Selection, Crossover and Mutation.- 5.2.2 A Bi-Modal Optimisation Problem.- 5.2.3 The Need for a Multiple Criteria Approach.- 5.3 Multiple Criteria Genetic Algorithms.- 5.3.1 Some Comparative Multiple Criteria G A Approaches.- 5.3.2 Common Issues in Multiple Criteria Genetic Algorithms in Engineering Design.- 5.3.3 Crowding and Niching.- 5.3.4 Estimating Niche Sizes.- 5.4 The Multiple Criteria Genetic Algorithm (MCGA): A Summary.- 5.5 A Numerical Example.- 5.6 An MCGA Schedule for a Generalised Job Shop.- 5.6.1 Problem Data.- 5.6.2 String Configuration.- 5.6.3 The Results from MCGA.- 5.7 Concluding Comments.- 6. An Integrated Multiple Criteria Decision Support System.- 6.1 System Structure and Method Selection.- 6.1.1 General Structure of IMC-DSS.- 6.1.2 The Routine Base for MCDM Techniques.- 6.1.3 Rules for Selection of MADM and MODM Methods.- 6.2 Data Base and Model Base.- 6.2.1 Decision Models and File Systems.- 6.2.2 Semi-Automatic Model Generation.- 6.3 A User Interface and Interactive Decision Making.- 6.3.1 Menu-Driven Interfaces.- 6.3.2 A Unified Approach for Generating and Ranking Design.- 6.4 Application of IMC-DSS.- 6.4.1 A Multiattribute Vessel Choice Problem.- 6.4.2 A Multiobjective Semi-Submersible Design Problem.- 6.4.3 Design Using the Unified Approach.- 6.5 Concluding Comments.- 7. Past, Present and the Future.- 7.1 Introduction.- 7.2 Case Studies.- 7.2.1 Designing product development processes to minimize lead times.- 7.2.2 Multicriteria robust optimisation under uncertainty of catamarans from a seakeeping point of view.- 7.3 Concluding Comments.- References.- Topic Index.

The compromise decision support problem and the adaptive linear programming algorithm

Abstract: 1. Our Frame of Reference A COMPREHENSIVE approach called the decision support problem technique" is being developed and implemented at the University of Houston to provide support for human judgment in designing an artifact that can be manufactured and maintained. Decision support problems (DSPs) provide a means for modeling decisions encountered in design, manufacture, and maintenance. Multiple objectives that are quantified using analysis-based "hard" and insight-based "soft" information can be modeled in the DSPs. For real-world, practical systems, not all of the information will be available for modeling systems comprehensively and correctly in the early stages of the project. Therefore, the solution to the problem, even if it is obtained using optimization techniques, cannot be the optimum with respect to the real world. However, this solution can be used to support a designer's quest for a superior solution. In a computerassisted environment, this support is provided in the form of optimal solutions for decision support problems. Formulation and solution of DSPs provide a means for making the following types of decisions:

Real-time economic dispatch with line flow and emission constraints using quadratic programming

Abstract: The presence of multiple constraints due to network line flow limits and emission allowances in the economic dispatch of modern power systems makes the conventional Lambda-Delta iterative approach no longer effective. This paper proposes a practical strategy based on quadratic programming (QP) techniques to solve the real-time economic dispatch problem. It formulates the problem with a quadratic objective function based on the unit's cost curves in quadratic or piecewise-quadratic forms. The operation constraints are modeled as linear equality/inequality equations, resulting in a typical QP problem. Goal programming techniques are also incorporated in the formulation which guarantees the best available solution even under infeasible conditions. In addition, the proposed strategy formulates the problem in the second phase dispatch in real-time by including a set of emergency control variables to provide effective control strategies for properly relieving constraint violations if they exist. The effectiveness of the proposed strategy is demonstrated by an example power dispatch problem.

Advances in Sensitivity Analysis and Parametric Programming

Abstract: Foreword. Preface. 1. A Historical Sketch on Sensitivity Analysis and Parametric Programming T. Gal. 2. A Systems Perspective: Entity Set Graphs H. Muller-Merbach. 3. Linear Programming 1: Basic Principles H.J. Greenberg. 4. Linear Programming 2: Degeneracy Graphs T. Gal. 5. Linear Programming 3: The Tolerance Approach R.E. Wendell. 6. The Optimal Set and Optimal Partition Approach A.B. Berkelaar, et al. 7. Network Models G.L. Thompson. 8. Qualitative Sensitivity Analysis A. Gautier, et al. 9. Integer and Mixed-Integer Programming C. Blair. 10. Nonlinear Programming A.S. Drud, L. Lasdon. 11. Multi-Criteria and Goal Programming J. Dauer, Yi-Hsin Liu. 12. Stochastic Programming and Robust Optimization H. Vladimirou, S.A. Zenios. 13. Redundancy R.J. Caron, et al. 14. Feasibility and Viability J.W. Chinneck. 15. Fuzzy Mathematical Programming H.-J. Zimmermann. Subject Index.

Multiple Criteria Decision Making and its Applications to Economic Problems

Abstract: Preface. 1. Multiple Criteria Decision Making: An Introduction. 2. Multiobjective Optimisation Methods. 3. Satisficing MCDM Approaches: Goal Programming. 4. Multiattribute Utility Approaches. 5. Miscellaneous Questions. 6. A First Linkage: CP and Bi-Attribute Utility. 7. Joint Production Shadow Prices and the Three Optima Theorem. 8. A Further Linkage: Multi-Attribute Utility in a Risk Aversion Context. References. Index.

Decision Support for Target-Based Resource Allocation of Public Services in Multiunit and Multilevel Systems

Abstract: This paper is concerned with the problem of resource allocationand target setting in the provision of public services. The paper develops a network-based representation of multilevel resource management with equity, efficiency, and effectiveness being recognised as the fundamental objectives of the system. On the modelling side, the proposed method combines data envelopment analysis and goal programming formulations integrated within an interactive planning framework. An illustrative application on fire departments is used to show the applicability of the method developed to assist the resource allocation process.

Goal programming, compromise programming and reference point method formulations : linkages and utility interpretations

Abstract: This paper looks at connections between the multi-criteria techniques of goal programming, compromise programming, and the reference point method. The utility function structure of each method is examined and interpretation of the techniques in terms of pareto efficiency and the equilibrated nature of the solution is given. Means of ensuring pareto efficiency whilst maintaining as much as possible the equilibrated character are given for a certain class of model. Methods for ensuring conceptual correctness and computational accuracy by reducing reference point models to equivalent goal programmes are given. The findings are illustrated by means of an example.

Interactive fuzzy programming for multilevel linear programming problems

Abstract: This paper presents interactive fuzzy programming for multilevel linear programming problems. In fuzzy programming for multilevel linear programming problems, recently developed by Lai et al., since the fuzzy goals are determined for both an objective function and decision variables at the upper level, undesirable solutions are produced when these fuzzy goals are inconsistent. In order to overcome such problems, after eliminating the fuzzy goals for decision variables, interactive fuzzy programming for multilevel linear programming problems is presented. In our interactive method, after determining the fuzzy goals of the decision makers at all levels, a satisfactory solution is derived efficiently by updating the satisfactory degrees of decision makers at the upper level with considerations of overall satisfactory balance among all levels. Illustrative numerical examples for two-level and for three-level linear programming problems are provided to demonstrate the feasibility of the proposed method.

Diverse Imprecise Goal Programming Model Formulations

Abstract: The goal programming (GP) model is probably the best known in mathematical programming with multiple objectives. Available in various versions, GP is one of the most powerful multiple objective methods which has been applied in much varied fields. It has also been the target of many criticisms among which are those related to the difficulty of determining precisely the goal values as well as those concerning the decision-maker‘s near absence in this modelling process. In this paper, we will use the concept of indifference thresholds for modelling the imprecision related to the goal values. Many classical imprecise and fuzzy GP model formulations can be considered as a particular case of the proposed formulation.

Pattern Classification by Linear Goal Programming and its Extensions

Abstract: Pattern classification is one of the main themes in pattern recognition, and has been tackled by several methods such as the statistic one, artificial neural networks, mathematical programming and so on. Among them, the multi-surface method proposed by Mangasarian is very attractive, because it can provide an exact discrimination function even for highly nonlinear problems without any assumption on the data distribution. However, the method often causes many slits on the discrimination curve. In other words, the piecewise linear discrimination curve is sometimes too complex resulting in a poor generalization ability. In this paper, several trials in order to overcome the difficulties of the multi-surface method are suggested. One of them is the utilization of goal programming in which the auxiliary linear programming problem is formulated as a goal programming in order to get as simple discrimination curves as possible. Another one is to apply fuzzy programming by which we can get fuzzy discrimination curves with gray zones. In addition, it will be shown that using the suggested methods, the additional learning can be easily made. These features of the methods make the discrimination more realistic. The effectiveness of the methods is shown on the basis of some applications.

A Nonpreemptive Goal Programming Approach to Truck Dispatching in Open Pit Mines

Abstract: A nonpreemptive goal programming dispatching model that provides an efficient basis for maximizing production and maintaining ore quality characteristics within prescribed limits is formulated, developed and validated with data from an operating mine. The ore quality characteristics are modeled using target (goal) constraints. The target, ore quality parameter value is set midway between the prescribed upper and lower limit values. The results show that the goal programming model ensures the maintenance of ore quality characteristics within prescribed lower and upper limits and the maximization of production. A linear programming model, on the other hand, results in production maximization only, which is not significantly different from the goal programming model, and requires re-handling or stockpiling.

Multiple Objective Programming Support

Abstract: This paper gives a brief introduction into multiple objective programming support. We will overview basic concepts, formulations, and principles of solving multiple programming problems.To solve those problems requires the the intervention of a decision-maker. That's why behavioral assumptions play an important role in multiple objective programming. Which assumptions are made affects which kindof support is given to adecision maker. We will demonstrate how a free search type approach can be used to solve multiple objective programming problems.

Efficiency measurement and strategic classification of Japanese banking institutions

Abstract: This article proposes a new two-staged discriminant approach, consisting of DEA (Data Envelopment Analysis) and GPNDA (Goal Programming for Nonlinear Discriminant Analysis). The proposed approach measures the DEA efficiencies of Japanese banks and then separates the banks into several groups according to these efficiency scores. Separated groups of banks are characterized by GPNDA. The methodological strength of GPNDA is confirmed by comparing it with other discriminant analysis techniques. Our empirical results may serve as a basis for developing financial strategies for Japanese banks whose business environment is now shifting from governmental regulation to international competition.

Intelligent solution and analysis of goal programmes: the GPSYS system

Abstract: An overview of GPSYS, an intelligent linear and integer goal programming system is presented in this paper. The intelligent goal programming system is one which is designed to allow a non-specialist access to, and clear understanding of, goal programming solution and analysis techniques. GPSYS is equipped with GP speed up techniques and analysis tools such as Pareto detection and restoration, normalisation, automated lexicographic redundancy checking and an interactive facility.

Theory and Methodology Goal programming for decision making: An overview of the current state-of-the-art

Abstract: There have been significant advances in the theory of goal programming (GP) in recent years, particularly in the areaof intelligent modelling and solution analysis. The intention of this paper is to provide an overview of these devel-opments, to detail and assess the current state-of-the-art in the subject, and to highlight areas which seem promising forfuture research. Modelling techniques such as detection and restoration of pareto e†ciency, normalisation, redundancychecking, and non-standard utility function modelling are overviewed. The connection between GP and other multi-objective-programming techniques as well as a utility interpretation of GP are examined. The rationality of rankingMulti-Criteria Decision Making techniques, and of placing GP in such a ranking, is discussed. O 1998 Elsevier ScienceB.V. All rights reserved.Keywords: Goal programming; Decision analysis; Modelling; Utility theory 1. IntroductionGoal Programming (GP) is a multi-objectiveprogramming technique. The ethos of GP lies inthe Simonan [50] concept of satisfying of objec-tives. Simon conjectures that in today’s complexorganisations the decision makers (DMs) do nottry to maximise a well defined utility function. Infact the conflicts of interest and the incompletenessof available information make it almost impossibleto build a reliable mathematical representation ofthe DMs’ preferences. On the contrary, within thiskind of decision environment the DMs try andachieve a set of goals (or targets) as closely aspossible. Although GP was not originally con-ceived within a satisfying philosophy it still pro-vides a good framework in which to implementthis kind of philosophy.The roots of GP lie in a paper by Charnes et al.in 1955 [6] in which they deal with executivecompensation methods. A more explicit definitionis given by Charnes and Cooper [7] in 1961 inwhich the term GP is first used. Until the middle ofthe 1970s, GP applications reported in the litera-ture were rather scarce. Since that time, and chiefly

Contemporary Management Science with Spreadsheets

Abstract: 1. Introduction. 2. An Introduction to Linear Programming. 3. Linear Programming: Sensitivity Analysis and Interpretation of Solution. 4. Linear Programming Applications. 5. Transportation, Assignment, and Transshipment Problems. 6. Integer Linear Programming. 7. Waiting Line Models. 8. Simulation. 9. Decision Analysis. 10. Multicriteria Decision Making.