Showing papers on "Goal programming published in 1977"

An Iterative Approach to Goal Programming

Abstract: An iterative approach is developed for solving general goal programming problems. This approach is then used to solve several nonlinear examples, including an integer solution problem. A dual for linear goal programming problems is developed and sensitivity analysis is discussed. Several new advances in modeling with goal programming which are made possible with this iterative approach are presented, namely least squares attainability of goals and priority dependent constraints.

Goal Programming and Agricultural Planning

Abstract: Goal Programming is similar in structure to linear programming, but offers a more flexible approach to planning problems by allowing a number of goals which are not necessarily compatible to be taken into account, simultaneously. The use of linear programming in farm planning is reviewed briefly. Consideration is given to published evidence of the goals of farmers, and ways in which these goals can be represented. A goal programming model of a 600 acre mixed farm is described and evaluated. Advantages and shortcomings of goal programming in relation to linear programming are considered. It is found that goal programming can be used as a means of generating a range of possible solutions to the planning problem.

A Chance Constrained Goal Programming Model for Working Capital Management

Abstract: (1977). A Chance Constrained Goal Programming Model for Working Capital Management. The Engineering Economist: Vol. 22, No. 3, pp. 153-174.

An Algorithm for a Restricted Discrete Approximation Problem in the $L_1 $ Norm

Abstract: Discrete approximation problems in the $L_1 $ norm arise in fitting by polynomials, linear regression, and goal programming. This paper presents an algorithm to solve discrete approximations in the $L_1 $ norm when the parameters are restricted by linear constraints. The algorithm is a special purpose primal linear programming method that utilizes certain properties of the problem to substantially reduce storage requirements and solution times. Computational experience with a computer code version of the algorithm will be presented.

A goal programming model for human resource accounting in a CPA firm

Abstract: Goal programming is a vital technique in reconciling conflicting objectives of job productivity, human resource development, and individual satisfaction. This paper uses a non-monetary approach to human resource valuation by exploring a goal programming model for planning the utilization of interacting human resources. A simulated illustration of the model for a hypothetical CPA firm is described, and the planning alternatives open to the partner-in-charge-of-personnel are shown graphically. The paper concludes with a discussion of extensions of the model.

Note—Effects of Substituting a Linear Goal for a Fractional Goal in the Goal Programming Problem

Abstract: In a recent article in Management Science, Awerbuch et al. [Awerbuch, S., J. G. Ecker, W. A. Wallace. 1976. A note: hidden nonlinearities in the application of goal programming. Management Sci. 22 (8, April) 918–920] discuss the occurrence of nonlinearities in the application of goal programming and conclude that direct linearization may not be possible when goal constraints are of a fractional nature.

Multi-level programming

Abstract: Although the behavior of a system may be adequately described by a mathematical programming model, external conditional controls may be imposed on the system. If the values of these controls depend upon the system's reactions to them, then they cannot be represented by exogenous constraints on the mathematical programming model. An algorithm is required which permits the simultaneous and interdependent functioning of two optimization processes. This paper identifies two subproblems facing decision makers in policy analysis, the behavioral simulation subproblem and the policy optimization subproblem. It offers a procedure for combining these in a programming model which contains two distinct and operative objective functions. A summarization and a numerical example of a new algorithm developed for this procedure are provided. Most importantly, the paper provides a formal, potentially quantitative, framework for the analysis of policy problems. It maintains that the more complex the problem, the more important it is to have a formal analytical framework within which the problem can be explicitly defined. This paper represents a modest step towards this goal.

Strategies for optimization of multiple-response simulation models

Abstract: This paper examines several procedures for optimizing simulation models having controllable input variables xi,i = 1,...,n and yielding responses nj,j = 1,...,m. This problem is often formulated as a constrained optimization problem, or it can be formulated in one of several multiple-objective formats, including goal programming. Whatever the mode of problem formulation, the optimization of multiple-response simulations can be approached through direct search methods, a sequence of first-order response-surface experiments, or by applying mathematical programming techniques to a set of second-order response surfaces.

A Goal Focusing Approach to Analysis of Trade-Offs among Household Production Outputs.

Abstract: : The ideas of goal programming are combined with efficient point considerations to yield a computational approach for use in tradeoff analysis and evaluations for goals accounting analysis The orientation is toward an efficient point which comes closest to a set of interrelated goals Attention is thereby restricted to a subset of the possible efficient points Shadow prices to guide further analyses are also automatically supplied for guiding subsequent tradeoff analyses Relations to the utility theoretic and household production considerations of Gary Becker and Kelvin Lancaster are indicated and an illustrative example involving econometric estimates for pertinent quality-of-life dimensions is supplied (Author)

Goal Programming Model for Water Quality Planning

Abstract: The broad goals of the Federal Water Pollution Control Act Amendments (PL 92-500) and the regional nature of water quality plans require that multiple objectives be considered in planning. Goal programming is an extension of linear (or integer) programming where the objective is to minimize the deviations from a set of goals, subject to system constraints. The model is applied to an example river basin system where the planning goals are the quality levels of four constituents required for three desired beneficial water used in five stream reaches, and the budget availability in four municipal regions for financing wastewater treatment. The model solution indicates the combination of treatment levels and costs that minimize deviations from user quality and budgetary goals for all stream reaches. Values of deviation variables in the solution indicate the water quality levels achieved. A comparison with the minimum cost solution for meeting stream standards identifies the costs and tradeoffs of achieving higher user quality goals.

A Resource Association Model for Insurance Management Utilizing Goal Programming

Abstract: In a model of a property-liability insurer, Hofflander and Drandell (H-D )I constructed a linear programming model to determine the optimum allocation of assets in order to maximize profits. The model was based on constraints which reflected policy and legal bounds on the insurer's activities. A model company with assets of $100 million served as the hypothetical insurer. Klock and Lee2 have suggested a Goal Programming approach to the (H-D) model. This paper is an analysis of the (H-D) model using Goal Programming and current asset returns. The purpose here is to demonstrate that the Goal Programming model developed is equivalent to the original (H-D) model. Goal Programming The concept of Goal Programming is relatively new although it had been postulated some time earlier.3 A good description of the methodology is presented in a current publication.4 It is a most important extension of linear programming in the treatment of business models. Briefly, a Goal Programming model is expressed mathematically as follows:

Compliance with the occupational safety and health act: a mathematical programming framework

Abstract: As a result of the passage of the Occupational Safety and Health Act of 1970, many employers are confronted with a multiple decision situation. The employer would like to comply with federally imposed standards as quickly as possible, but the employer must consider the budgetary limits under which he can profitably operate while achieving compliance. A goal programming model can be formulated to solve the problem. In this paper a review of the OSHA requirements is made, and a case example is presented to demonstrate the applicability of goal programming to resolving the budgetary pressure of OSHA compliance.

The Design of Acoustic Transducer Arrays Using Goal Programming.

Abstract: : The design of acoustic transducer arrays often involves the satisfaction of multiple, conflicting design specifications. The use of conventional mathematical programming techniques in this endeavor have been inhibited by their inability to consider these multiple objectives within their design formulations. Most present techniques formulate the design problem in terms of a single objective subject to a set of design constraints which become difficult, if not impossible, to solve if these constraints are conflicting in nature. Goal Programming is an effective alternative methodology. Array pattern synthesis through Goal Programming provides the optimal solution to the multiple criteria design of various classes of acoustic transducer arrays. The design problem is formualted in terms of the multiple objectives expressed by the system response functions and the desired characteristics of the design parameters. The solution of two reasonably complex planar array designs is presented. The details of their formulation and solution provide an indication of the ability of this approach to resolve even more complex array design problems. (Author)

A Nonlinear Goal-Arc Network Extension of the Multi-Level Coherence Model for EEO.

Abstract: : The model developed in this report is an extension and reformulation of a model called the Coherence model for guiding EEO (Equal Employment Opportunity) planning at the micro-level in the U.S. Navy's civilian workforce developed by Charnes, Cooper, Lewis and Niehaus. This model is called the Goal-Arc model. Like its predecessors, the Goal-Arc model utilizes a goal programming approach with embedded Markoff processes. As in the Coherence model, piecewise linear goal functionals with 'artifact goals' are used to approximate the transition relations of the Markoff process. The Goal-Arc model, however, carries this to another stage of development. Analytical as well as network formulations and interpretations are provided in the following article. A numerical example with related interpretations for EEO planning is also provided. (Author)

Goal Programming: A New Tool for the Christmas Tree Industry

Abstract: Goal programing (GP) can be useful for decision making in the natural Christmas tree industry Its usefulness is demonstrated through an analysis of a hypothetical problem in which two potential growers decide how to use 10 acres in growing Christmas trees Though the physical settings are identical, distinct differences between their goals significantly influence the recommendations made to them Included also is a simplified presentation of the general GP model plus a discussion of its applicability to production and marketing problems in the industry

Design of optimum sonar transducer arrays using goal programming

Abstract: The design of acoustic antenna arrays often involves the satisfication of multiple, conflicting design specifications. The use of conventional mathematical programming techniques in this endeavor have been inhibited by their inability to consider these multiple objectives within their design formulations. Most present techniques formulate the design problem in terms of a single objective subject to a set of design constraints which become difficult, if not impossible, to solve if these constraints are conflicting in nature. Goal Programming is an effective alternative methodology. Array pattern synthesis through Goal Programming provides the optimal solution to the multiple criteria design of various classes of acoustic antenna arrays. The design problems are formulated in terms of the multiple objectives expressed by the system response functions and the desired characteristics of the design parameters. The solution of two reasonably complex planar array designs is presented. The details of their formula...

Decision Analysis Technique for Program Evaluation (Goal Programming).

Abstract: : A mathematical decision model for information service planning was derived from administrative goals. Program plans are assessed in the context of performance goals for six areas: available staff, available budget, program effectiveness, functional requirements, basic service requirements, investment programs to insure future progress. The technique is an extension of linear programming that calculates optimal solutions to decision problems in the areas of resource allocation, policy analysis and program evaluation according to a set of preemptive priorities established by management. The report includes a program in FORTRAN 4 for computing solutions. (Author)

Computer-Assisted Manpower Models Using Goal Programming.

Abstract: : This paper reviews the large number of manpower and personnel models using goal programming developed by A. Charnes, W. W. Cooper, R. J. Niehaus and others. These models are built around embedding Markov processes into a goal programming structure for examining recruiting and internal staffing decisions. A discussion is first provided of the structures of aggregrate manpower models and how they can be linked to program planning, equal employment opportunity planning, etc. This is followed by a discussion of assignment/distribution model structures. Here, emphasis is placed on a biased quadradic multi-attribute assignment model and on a capacitated distribution model for organization design which uses 'goal artifacts' to move any instabilities in transition rates to a less sensitive part of the model. The final section reviews features such as the introduction of chance-constraints or risk into the models. The features are introduced to illustrate the linking pins to the research frontiers. (Author)

A generalized upper bounding algorithm for large-scale generalized goal programming problems

Abstract: This paper considers solutions to large-scale generalized goal-programming problems using a variant of the generalized upper bounding algorithm. The method outlined takes advantage of the structural features of the problem by maintaining a small working basis and partitioned block inverses in the execution of the revised simplex operations.