Showing papers on "Goal programming published in 1981"

On fuzzy goal programming

Abstract: This paper illustrates how the goal programming problem with fuzzy goals having linear membership functions may be formulated as a single goal programming problem. Also, a previously defined method for dealing with fuzzy weights for each of the goals is re-examined.

Multiple Objective Linear Fractional Programming

Abstract: This paper presents a simplex-based solution procedure for the multiple objective linear fractional programming problem. By 1 departing slightly from the traditional notion of efficiency and 2 augmenting the feasible region as in goal programming, the solution procedure solves for all weakly efficient vertices of the augmented feasible region. The article discusses the difficulties that must be addressed in multiple objective linear fractional programming and motivates the solution algorithm that is developed.

Interactive Sequential Goal Programming

Abstract: This paper introduces a new solution method based on Goal Programming for Multiple Objective Decision Making (MODM) problems. The method, called Interactive Sequential Goal Programming (ISGP), combines and extends the attractive features of both Goal Programming and interactive solution approaches for MODM problems. ISGP is applicable to both linear and non-linear problems. It uses existing single objective optimization techniques and, hence, available computer codes utilizing these techniques can be adapted for use in ISGP. The non-dominance of the "best-compromise" solution is assured. The information required from the decision maker in each iteration is simple. The proposed method is illustrated by solving a nutrition problem.

Interactive multiple goal programming : applications to financial planning

Abstract: 1. Introduction.- 1.1. Motivation.- 1.2. Scope of the Study.- 1.3. Outline of the Contents.- References.- 2. Multiple Goals in Capital Budgeting and Financial Planning.- 2.1. Introduction.- 2.2. Constraints in Capital Budgeting and Financial Planning.- 2.3. The Goal of Market Value Maximization.- 2.4. Assumptions with Respect to the Decision Maker and the Organization.- 2.5. The Firm's Market Value as One of the Elements in a Dynamic Goal Complex.- References.- 3. A Survey of Multiple Criteria Decision Methods.- 3.1. Terminology and Basic Concepts.- 3.2. Decision Problems and Methods.- 3.3. Some Characteristics of Decision Problems.- 3.4. A General Overview of Available Methods.- 3.5. An Overview of Multiple Objective Programming Methods.- 3.6. Conclusion.- References.- 4. Goal Programming.- 4.1. General Formulation.- 4.2. The Objective Function.- 4.3. Solution Procedures.- 4.4. An Adapted Simplex Procedure.- 4.5. Concluding Remarks.- References.- 5. Interactive Multiple Objective Programming Methods.- 5.1. Features of an Interactive Approach.- 5.2. Elements of Interactive Methods.- 5.3. The Need of an Interactive Variant of Goal Programming.- 5.4. Interactive Goal Programming Methods.- 5.5. Conclusion.- References.- 6. Interactive Multiple Goal Programming.- 6.1. Definitions and Assumptions.- 6.2. Description of the Procedure.- 6.3. IMGP in Linear Terms.- 6.4. Existence, Feasibility, Uniqueness and Convergency.- 6.5. Concluding Remarks.- Appendix 6.a. Suitable Starting Solutions.- References.- 7. IMGP in Practice: Examples and Experiences.- 7.1. Two Simple Examples.- 7.2. Experiments with an Imaginary Decision Maker.- 7.3. Some Empirical Results.- 7.4. IMGP Applied to Discrete Decision Problems.- 7.5. Conclusions.- Appendix 7.a. Computer Program Used for the Experiments with an Imaginary Decision Maker.- Appendix 7.b. An Operational Computer Program for IMGP.- References.- 8. Capital Budgeting and Financial Planning with Multiple Goals.- 8.1. A Brief Survey of the Literature.- 8.2. Large Numbers of Goal Variables.- 8.3. Goal Variables Requiring Special Treatment.- 8.4. Indivisibility of Projects.- 8.5. Conclusion.- References.- 9. Using IMGP for a Financial Planning Model: An Illustration.- 9.1. Introductory Remarks.- 9.2. Description of the Model.- 9.3. Selection of a Financial Plan (Continuous Case).- 9.4. Selection of a Financial Plan (Discrete Case).- 9.5. Conclusion.- References.- 10. Evaluation.- 10.1. Implementation of IMGP.- 10.2. Main Advantages of IMGP.- 10.3. Some Disadvantages and Areas for Further Research.- 10.4. Concluding Remarks.- References.- Author Index.

On fuzzy goal programming—some comments

Abstract: This paper pertains to goal programming with fuzzy goals and fuzzy priorities. Hannan [1], in his paper on fuzzy goal programming, alludes to the difficulty of handling fuzzy priorities and further notes that a method that this author proposed [2] may lead to incorrect results. In this note, the general problem of goal programming with fuzzy priorities is reexamined, along with the solution to the specific example presented in my original paper [2]. It is shown that the method for handling fuzzy priorities originally proposed by this author does indeed capture the relative importance of goals.

An adaptive decision support system for academic resource planning

Abstract: Institutions of higher learning are growing increasingly interested in the use of model-based approaches to their resource allocation problems. Recent modeling approaches, however, have failed to consider that resource allocation planning is not a well-structured decision process. Additionally, many decision makers are necessarily involved in the academic planning process and may assume dissimilar perspectives on the importance of achieving different goals and objectives. Furthermore, satisfactory allocation solutions can be expected to vary considerably from decision maker to decision maker as the individual's cognitive processes, perceptions, and evaluations are taken into consideration. This paper describes a decision support system (DSS) approach that attempts to adapt to a variety of academic decision makers with differing planning views in an environment of multiple conflicting objectives. This DSS, which was successfully tested on four academic decision makers in a large midwestern university, shows considerable promise for providing decision support to decision makers with varied problem-solving styles.

A Markovian Goal Programming Approach to Aggregate Manpower Planning

Abstract: The combination of a manpower supply model (a Markov type model based on historical probabilities of losses, promotions and gains) and goal programming with preemptive priorities provides a useful tool for developing a future year manpower plan under conflicting socio-econo-organizational objectives. Successful utilization requires a close management involvement in adjusting probabilities and specifying goals, priorities and impending policy changes. Such an approach is presented in this paper and illustrated by means of an industrial case study example. The presentation is kept simple, yet detailed and unified, so that is is easily understood by practitioners and students of operational research/management science.

Goal Programming Approach to a Type of Quality Control Problem

Abstract: Fixing the levels of inputs and process variables in order to meet a required specification of output is a common quality control problem. However difficulties can arise when the output has a number of characteristics and when each of these characteristics has to satisfy a specification. Such a problem was met in a paper manufacturing factory and the problem was solved using Goal Programming (GP). The method can be applied to other process control problems and this paper gives the GP formulation of the general process control problem. The paper also gives details of the case study from which the method was developed.

A multiobjective dynamic programming method for capacity expansion

Abstract: This paper integrates two existing methodologies-a single-objective dynamic programming method for capacity expansion and the surrogate worth tradeoff (SWT) method for optimizing multiple objectives -into a unified schema. In particular it shows 1) how a multiobjective mixed integer programming formulation representing the multiobjective capacity expansion problem can be translated into a multiobjective dynamic programming formulation, 2) how such DP formulation can be used to generate noninferior solutions, and 3) how tradeoff information can be obtained from solutions in 2). The necessary theoretical machinery for 3) is developed. To demonstrate the computational viability of the proposed schema, an example problem is formulated and solved.

Note-On An Interpretation of Fractional Objectives in Goal Programming as Related to Papers by Awerbuch et al., and Hannan

Abstract: This Note points out a minor error in a technique developed by Soyster and Level Soyster, A. L., B. Lev. 1978. An interpretation of fractional objectives in goal programming as related to papers by Awerbuch et al., and Hannan. Management Sci.24 14 1546-1549. for determining if a linear goal can be substituted for a fractional goal in a goal programming problem. A revised formulation is provided.

A Goal Programming Based Interactive Decision Support System

Abstract: The purpose of this research was to develop a multicriteria decision making tool which could take into account the cognitive limitations of human information processing. Specifically, an interactive goal programming methodology was devised which sought to minimize the difficulty of prioritizing goals and of restructuring problems to find improved solutions. This methodology employs a priority elicitation procedure which can accept a variety of ill to well-defined priority structures. It provides feedback after each solution is generated. This feedback describes the tradeoff inherent in improving the achievement of any unsatisfied goals. Goal attainment probabilities and possible goal level revisions are also presented to users of the model to assist them in evaluating alternative restructures of the model. These modifications have been incorporated into a generalizable decision support system which has been tested in an academic decision making environment.

Management of college student recruiting activities using goal programming

Abstract: A shrinking pool of potential students, due to a declining birthrate as well as uncertain economic times, is creating the need for more effective recruiting of college students. One approach using a goal programming model has been developed and is currently being used to manage recruitment activities in a small four-year college in Nebraska. The model identifies both the type and number of activities that must be completed each quarter in order to reach an enrollment goal for a given year. Factors such as budget, time, manpower, and marketing strategies are highlighted in the model. The results of the goal programming model encouraged more field activities in the first two quarters with emphasis on new-candidate identification. The third-quarter recruiting strategy is more balanced while the fourth-quarter emphasis is placed on follow-up activities that occur chiefly in the office. Use of this model will enable recruiters to meet enrollments while managing recruiting resources and activities in order to remain within the recruiting budget.

An application of goal programming to the desegregration busing problem

Abstract: This paper develops a goal programming model for obtaining solutions to the desegregation busing problem. The model is compared with the linear programming approach and is applied to 31 schools serving over 14,000 students. Solutions result in potential cost savings of over one-half million dollars.

Interactive Multiple Goal Programming as an Aid for Capital Budgeting and Financial Planning with Multiple Goals

Abstract: In this paper, we consider capital budgeting and financial planning as decision problems involving a multiplicity of goals. Furthermore, we will investigate the usefulness of a number of multiple-objective programming methods for the solution of these problems.

Goal Programming to Assist in Decision Making

Abstract: Goal programming is a systematic method of decision making with potential for assisting fishery managers and administrators achieve a practical decision when there are multiple or conflicting goals. The technique involves formulating the decision problem by identifying and establishing goals, setting priorities for achieving the goals, and determining what constraints exist on the availabilities of monetary and nonmonetary resources. A model of the problem, formulated as a modification of the simplex method of linear programming, can be solved by hand or with a computer program. The resulting solution gives recommended levels of each choice variable that best achieve stated goals. We describe the construction of a model for goal programming with an example of a fish stocking problem, and perform a sensitivity analysis of goal priorities to demonstrate how the effects on the solution to the problem can be determined. Goal programming supplements, but does not replace, the judgment of good managers...

Optimization approach for allocation of funds for maintenance and preservation of the existing highway system (abridgment)

Abstract: This paper discusses the development and application of goal programming techniques to achieve optimum allocation of federal and state funds for highway system improvement and maintenance. The methodology is applied to the Indiana highway system. An example problem that involves six improvement activities, four routine maintenance activities, and four system objectives is presented. Several scenarios are tested with the model to understand the model's operation and to gain insights into the trade-offs involved. The model is flexible enough to analyze other scenarios that involve revised standards or revised system objectives. (Author)

Organizations: multiple agents with multiple criteria. Proceedings of the Fourth International Conference on Multiple Criteria Decision Making, University of Delaware, Newark, August 10-15, 1980

Abstract: "Produce or Purchase-A Multiple Goal Linear Programming Model".- "Random Sampling Approach to MCDM".- "Explaining Utility Theory Paradoxes by Decision Regret".- "Stability, Equality, Balance, and Multivariate Risk".- "Efficient Solutions for Point-Objective Discrete Facility Location Problems".- "High-Stake Decision Making-An Empirical Study Based on House Purchase Process".- "Multiple objective Linear Programming: An Economist's Perspective".- "Decision Concepts for Organizations".- "A Goal Programming Based Interactive Decision Support System".- "Multiobjective Optimization in Transportation: The Case of Equilibrium network Design".- "Some Modifications of a Large Step Gradient Method for Interactive Multicriterion Optimization".- "A Generalized Method of Approximating the Set of Efficient Points with Respect to a Convex Cone".- "An Experiment in Multiple Criteria Energy Policy Analysis.- "A Trial Towards Group Decisions in Structuring Environmental Science".- "An Empirical Evaluation of Some Multiple Criteria Methods for Discrete Alternatives" (Abstract only).- "An Interactive Bargaining Procedure for Solving the Multiple Criteria Problem With Discrete Alternatives" (Abstract only).- "Multiple Objective Linear Fractional Programming Algorithms Some Computational Experience".- "Consensus Time Series Forecasting".- "Group Decision Making and Multiple Criteria-A Documented Application".- "A Multiple Goal Model for Allocation of Teaching Personnel".- "A Multi-Objective Force Allocation Model" (Abstract).- "The Choice of Final Compromise Solution in Multiple Criteria Linear Programming Problem".- "Compound Lotteries: Call Option Spreads in Black-Scholes Markets".- "Multidimensional Locational Decisions and Interactive Programming".- "A Model of Internal Decisions in Answering Questions".- "Trilogical Coherence in Multicriteria Decision-Aid Making-A Methodological Approach to Computer-Aid Management".- "Multiobjective Programming Solutions to N-Person Bargaining Games".- "Multiple Objectives in Facility Location: A Review".- "Behavioral Issues in Multiattribute Utility Modeling and Decision Analysis".- "Organizational Aspects of Multicriteria Decision Making".- "An Ellipsoidal Interactive Multiple Goal Programming Method".- "Design Requirements for an Investment Strategy Decision System for Training and Personnel Technology RDT&E".- "On Methodology for Group Decision Support".- "Hierarchical-Multiobjective Framework for Energy Storage Systems".- "On the Fidelity of Multiattribute Preference Representations: Some Analytical Considerations".- "A Mathematical Basis for Satisficing Decision Making".- "Probability Dominance in Random Outcomes-An Introduction".- "A Method for Large-Scale Integer Goal Programming With an Application to a Facility Location/Allocation Problem".- Dialogue Between D. Bell and P. Schoemaker.- Conference Program.- List of Participants.- Minutes of the Organizational Meeting.

Optimization of Radioactive Waste Management System by Application of Multiobjective Linear Programming

Abstract: A mathematical procedure is proposed to make a radioactive waste management plan comprehensively. Since such planning is relevant to some different goals in management, decision making has to be formulated as a multiobjective optimization problem. A mathematical programming method was introduced to make a decision through an interactive manner which enables us to assess the preference of decision maker step by step among the conflicting objectives. The reference system taken as an example is the radioactive waste management system at the Research Reactor Institute of Kyoto University (KUR). Its linear model was built based on the experience in the actual management at KUR. The best-compromise model was then formulated as a multiobjective linear programming by the aid of the computational analysis through a conventional optimization. It was shown from the numerical results that the proposed approach could provide some useful informations to make an actual management plan.

Multiobjective Optimization of a Local Fishing Fleet — A Goal Programming Approach

Abstract: This paper is based on the author’s Dr.ing. thesis, submitted to the Norwegian Institute of Technology in 1976 (Amble1), and on further research done at the Nordland Regional College. The work has been financially supported by the Norwegian University of Fisheries, the Nordland Regional College and the Norwegian Fisheries Research Council.