Showing papers on "Goal programming published in 1985"

Introduction to Linear Goal Programming

Abstract: Introduction History and Applications Development of the LGP Model An Algorithm for Solution Algorithm Illustration Duality and Sensitivity Analysis Extensions

Goal programming and multiple criteria decision-making in farm planning: some extensions

Abstract: This paper is the sequel to a previous article by Romero and Rehman on the role of multiple criteria decision-making (MCDM) techniques, particularly goal programming (GP), in farm planning. This assessment of the potential usefulness of the MCDM paradigm is carried further and now covers the extensions to GP and other related methodologies such as multiobjective programming, compromise programming, multigoal programming and generalised GP. Later analysis is focused on methods of dealing with uncertainty and risk in farm planning models, by demonstrating how game theoretic principles and the MOTAD approach (along with its main variant, target MOTAD) can be incorporated within the MCDM framework.

Optimal Criterion Weights in Repetitive Multicriteria Decision-Making

Abstract: In repetitive judgmental discrete decision-making with multiple criteria, the decision maker usually behaves as if there is a set of appropriate criterion weights such that the decisions chosen are based on the weighted sum of all the criteria. Many different procedures for estimating these implied criterion weights have been proposed. Most of these procedures emphasize the preference trade-off among the multiple criteria of the decision maker, and thus the criterion weights obtained are not directly related to the hit ratio of matching decisions. Based on past data, statistical discriminant analysis can be used to determine the implied criterion weights that would reflect the past decisions. The most interesting performance measure is the hit ratio. In this work, we use the integer linear goal-programming technique to determine optimal criterion weights which minimize the number of misclassification of decisions. The linear goal-programming formulation has m constraints and m + k + 1 variables, where m is the number of cases and k is the number of criteria. Empirical study is done by using two different procedures on the actual past admission data of an M.B.A. programme. The hit ratios of the different procedures are compared.

A categorized bibliographic survey of goal programming

Abstract: A total of 240 goal programming articles that have so far appeared in over 60 English journal publications are compiled and classified according to technique and application areas. Analysis of these data provides interesting insights regarding goal programming article characteristics, literature trends and future needs.

Goal‐setting theory: Unexplored areas and future research needs

Abstract: Goal-setting research and applications of goal setting to organizations are critically examined along a unidmensional/multidimensional continuum. Four relatively unexplored areas in the goal-setting paradigm are considered in detail: (a) the distinction between quantity and quality goals and performance measures, (b) conflicts inherent in goal-setting processes, (c) individual versus group levels of analysis, and (d) laboratory versus field research support for the goal-setting paradigm. The resultant research needs suggest limits to the application of goal setting as well as some needs regarding theory building and research design.

Interactive decision making for multiobjective linear fractional programming problems with fuzzy parameters

Abstract: In this paper, we focus on multiobjective linear fractional programming problems with fuzzy parameters and present a new interactive decision making method for obtaining the satisficing solution of the decision maker (DM) on the basis of the linear programming method. The fuzzy parameters in the objective functions and the constraints are characterized by fuzzy numbers. The concept of a-Pareto optimality is introduced in which the ordinary Pareto optimality is extended based on the α-level sets of the fuzzy numbers. In our interactive decision making method, in order to generate a candidate for the satisficing solution which is also a-Pareto optimal, if the DM specifies the degree α of the a-level sets and the reference objective values, the minimax problem is solved by combined use of the bisection method and the linear programming method and the DM is supplied with the corresponding α-Pareto optimal solution together with the trade-off rates among the values of the objective functions and the d...

A Linear Goal-Programming Model for Public-Sector Project Selection

Abstract: This paper outlines a generalized public-sector project selection model using linear goal programming and demonstrates its application using data from the energy-based sector in Trinidad and Tobago, a small developing country in the Caribbean. The decision maker's influence on the project portfolio selected is demonstrated by varying parameters, such as the priority structure and the level of availability of key resources. Goal programming emerges as a powerful tool available for use by public-sector planners in developing countries faced with the challenge of formulating an appropriate public-sector investment programme.

Solving Multiple Response Simulation Models Using Modified Response Surface Methodology Within A Lexicographic Goal Programming Framework

Abstract: paper describes a new procedure for obtaining satisfactory solutions to multiple-response, multiple-input simulation models. A modified version of response surface methodology is incorporated to obtain input values which meet user specified goals for the responses. The approach is illustrated with three examples which demonstrate the method. The desirability of incorporating this approach into an interactive computer mode is also discussed.

Multi-Objective and Goal-Programming Approaches as a Distance Function Model

Abstract: This note shows that virtually all the multi-objective and goal-programming approaches can be thought of as special cases of a general distance-function model. Although the outcome of this paper cannot be considered completely new, it can, however, help increasing conceptual clarity and precision in future dialogue, showing the actual links between multi-objective programming, goal programming and traditional mathematical programming.

An Algorithm for Solving the Linear Goal Programming Problem by Solving its Dual

Abstract: Goal programming, and in particular lexicographic goal programming (i.e. goal programming within a so-called ‘pre-emptive priority’ structure or having non-Archimedean weights), has become one of the most widely used of the approaches for multi-objective mathematical programming. While also applicable to non-linear or integer models, most of the literature has considered the lexicographic linear goal-programming model and its solution via primal simplex-based methods. However, in many cases, enhanced efficiency (and significant additional flexibility) may be gained via an investigation of the dual of this problem. In this paper we consider an algorithm for solving such a dual and also indicate how it may be implemented on conventional (i.e. single objective) simplex software.

Strategic planning for investment in R&D using decision analysis and mathematical programming

Abstract: This paper investigates the strategic planning and investments associated with research and development (R&D) project selection and budgeting within a division of an aerospace firm. A model is described that is used in an R&D planning environment where considerable risks result from technological, economic, governmental, and market factors. Several forms of a multi-attribute utility (MAU) objective function are maximized using mathematical programming techniques. Approximate methods, including compromise programming and goal programming, are evaluated and yield results that are reasonably dose to and require less computation than more exact methods. Solutions are used to recommend to management an R&D portfolio that maximizes expected utility for the division.

A computer-based method for the preliminary design of ships

Abstract: Traditionally, the preliminary ship design process involves satisfying each design requirement sequentially modifying the dimensions and repeating the process until all requirements are met. This approach neglects the interaction between the major design requirements (such as the effect of stability on deadweight) resulting in an acceptable instead of an optimal design. In order to achieve the most efficient and effective design, a multiple-objective optimisation technique has been used. In the past, multiple-objective problems could be solved only in the linear domain using goal programming techniques. The proposed preliminary design optimisation model involves a mix of linear and nonlinear goals and constraints and has been solved by a new method. Five comprehensive examples are used to demonstrate the effectiveness of the method and to provide a basis for comparison with other published work. This is believed to be the first application of nonlinear goal programming in this field. The computer-based method proposed is new and makes an important contribution toward the automation of the preliminary design process.

Incorporating Economic Objectives in Goal Programs for Fishery Management

Abstract: Allowing for the biological and economic complexities pertaining to a particular fishery requires a phased, hierarchical approach to fishery management. Mathematical programming models, particularly goal programs, are applicable to this type of management problem because they can readily accommodate the constraints and targets set in previous phases as well as any new ones that may be required There are a number of goal programming formulations that may be useful for modeling the objectives of fishery management problems. Furthermore, economic objectives can be interpreted in different ways. The purpose of this paper is to illustrate a range of goal programming specifications into which alternative economic objectives have been incorporated.

A multiple criteria approach for production planning of automated manufacturing

Abstract: The product range versatility of automated production systems provides scope for economic operations covering a wide range of feasible groups of orders. The selection of a particular group of orders which will facilitate the satisfactory fulfilment of possibly conflicting multiple performance goals requires the formulation of a flexible planning model, incorporating several special characteristics. This paper presents a modelling approach which enables the determination of compromise solutions, using a goal programming formulation with boolean expressions and a weighted attainment function. This can be solved using standard mathematical programming software. Consideration of the modular elements in the model formulation is followed by an example problem to illustrate its application.

An alternative approach to goal programming

Abstract: An alternative approach to goal programming is described. The approach incorporates both cardinal weighting and ordinal ranking of deviation variables, and does not require apriori specification of...

Using linear and goal programming to immunize bond portfolios

Abstract: Bierwag and Khang's (1979) model of immunizing a portfolio of default-free government bonds is expanded here to include default-grade corporate bonds. The immunizing equation is found to be slightly different. Both linear and goal programming are shown to be alternative techniques for identifying an investor's optimal immunizing portfolio.

An Interactive Procedure for Solving Group Decision Problems

Abstract: Decision problems on the company level are often made by a group. Such problems are illstructuralized because decision makers can change their preferences and demands. This paper presents an interactive procedure which makes it possible to solve linear multiple criteria problems. This is done under the assumption that decision makers want to reach consensus. There is no assumption, however about their behaviour. The procedure utilizes goal programming models. It has been programmed. A computer package NEGO is utilized at courses in management and, experimentally, in one company.

Multi-objective microcomputer-based interactive production planning

Abstract: This paper presents a production-planning methodology which brings about interactions between the decision-maker and the analyst in a situation where multiple objectives are considered. An interactive goal-programming technique is developed in which the decision-maker bases his judgement on pairwiae comparisons of the values of the objective functions at each iteration. Three goals are considered: minimizing production cost, maintaining a balanced workforce level for the periods over the time horizon and last, but not least, minimizing the deviation between the actual production level and that specified by the government. A microcomputer-baaed procedure is adopted and its application to the case of a small diesel engine assembly factory is presented.

Preliminary Report on a Theory of Plan Synthesis

Abstract: : Classical planning problems have the following form: given a set of goals, a set of actions, and a description of the initial state of the world, find a sequence of actions that will transform the world from any state satisfying the initial-state description to one that satisfies the goal description. In principle, a problem of this type may be solved by a very simple procedure: merely enumerate all possible sequences of actions and test each until one is found that achieves the intended goals. By this procedure, we will eventually find a solution if one exists. However, in practice, not only do we want to find a solution, we want to do so expeditiously. Quick and efficient problem solving is desirable primarily for reasons of economy: the less time it takes to solve a problem, the more productive one can be. Furthermore, in some situations, the time it takes can mean the difference between success and failure, as is the case when the problem is part of a scholastic exam or when the problem is to prevent meltdown in a nuclear reactor. Previous work aimed at developing efficient planning techniques has been highly experimental in nature, the standard methodology being to explore ideas by constructing computer programs. For the most part, very little theoretical analysis has been done to determine why the programs work when they are applicable, and whether they can be generalized to solve larger classes of problems.