Showing papers on "Goal programming published in 1975"

Optimizing organizational goals in assigning faculty teaching schedules

Abstract: The administration of higher education, as with other administrative environments, has become increasingly complex. This paper presents a mathematical model which addresses one of the administrative tasks concerning administrators in all institutions, the establishment of faculty teaching schedules. The assignment technique utilized by the model is linear programming with two special characteristics: goal programming and mixed-integer programming. The goal programming characteristic refers to the provision of explicit stack variables to take on values representing deviations from assignment criteria that may result in resolving conflicts which arise from interactive administrative priorities. The mixed-integer programming characteristic refers to a requirement of the model that certain variables take on only integer values if they appear in the final solution. The model is demonstrated using two different sets of preference orderings for goal achievement.

Goal programming and constrained regression—A comment

Abstract: Goal programming was originated to obtain “constrained regression” estimates for an executive compensation formula which would conform to an organization hierarchy and other company policies prescribed by management. A subsequent history in that context is traced through other uses in personnel planning and in organization designs. Applications to planning advertising strategies are also examined which extend to uses of goal programming that involve fitting frequency functions under a variety of constraints. Finally this is all related to the initial and continuing research on the statistical properties of these estimates.

Resource Allocation through Goal Programming

Abstract: Highlight: One of the major weaknesses of using linear programming in natural resource management is that only a single criterion for determining the optimal strategy is allowed. A goal programming model is presented that allows for multiple, conflicting goals. Results are provided for a management area in northern Colorado. The trade offs between goals are demonstrated by comparison of results from multiple runs in which the order of goal preferences is varied. Goal programming is shown to be a very flexible decision aiding tool which can handle any decision problem formulated by linear programming more efficiently.

Hard and soft constraints in linear programming

Abstract: The linear programme and its constraints are split into two parts. The first consists of the traditional structure, the second being akin to goal programming. SOFT constraints are weighted relative to each other and then approximately weighted relative to the HARD constraints. The LP is run four times giving different emphasis to the SOFT and HARD constraints. The manager requesting the LP has then to decide which gives the most appropriate solution.

Dynamic multiattribute models for mixed manpower systems

Abstract: This paper reviews a wide variety of manpower and personnel models of the goal programming variety. This is done from a strategy-oriented point of view addressing the problems of interest for immediate implementation as well as basic problems of manpower model research development. Particular emphasis in this paper is concerned with how analytical models can be brought to bear on the problems of combining military and civilian manpower into one management system. This includes a discussion of the computer support arrangements necessary to implement the models. First, we discuss an extension of multilevel models to provide an integrated approach to program planning which includes the dynamics of the manpower requirements-inventory relationships of mixed military-civilian manpower systems. Then, focus is given to some of the potential Navy applications particularly in terms of ways the outputs from the global multilevel model might be interfaced with assignment models for operational planning. The paper concludes with a discussion of static and dynamic multiattribute assignment models which operate on the individual man-job matching level. It is at this level of detail that dynamic mixed manpower systems might be constructed for use in equal employment opportunity planning and for local organization design studies.

Allocation of research grants to university research proposals

Abstract: In this paper we examine the optimal allocation of research money to incoming research proposals in a university environment. Since a university has no obvious objective to maximize when allocating research money, but rather has several, possibly conflicting, goals it would like to achieve, the method of goal programming is proposed as a solution technique, and the meaning and estimation of the priority weights in the resulting objective function in the goal programming problem, with regard to tradeoffs between goals, are discussed thoroughly. Finally, calculations based upon hypothetical sample data re presented in order to test the sensitivity of the model to different sets of priority weights.

Quadratic preferences and goal programming

Abstract: One of the shortcomings of goal programming lies in its linearity, assumption, specifically in the objective function. This assumption compels one to work with constant marginal utilities and rates of substitution. In this paper a quadratic preference function, which is more consistent with economic theory and reality, is formulated and introduced into goal programming. In an effort to facilitate the understanding of the proposed procedure, two illustrative examples—one with symmetric preferences and the other with asymmetric preferences, both applied to the objective function—are solved and compared with a goal programming solution.

Profit Maximization with the Aid of Goal Programming for Speculative Housing Estate Developers

Abstract: This article discusses the applicability of goal programming as a prescriptive tool for the new town planner. A model is presented which illustrates how the planning objectives for the new community can be transformed into a land allocation problem which is amenable to a goal programming solution. The results of an application of this approach indicate that the model is useful if given an accurate ordering of the planner's objectives. However, the goal programming model requires significantly more computations than an analogous linear programming model with the sole objective of profit maximization.

Some Uses of Mathematical Programming Systems to Solve Problems that are not Linear

Abstract: There are three main reasons why a purely linear programming model may not represent a constrained optimization problem adequately: economies of scale, other non-linearities that do not invalidate a local optimum, random data. These reasons lead respectively to integer programming, non-linear programming and stochastic programming. Examples of each type of model are discussed. These have all been solved using a standard mathematical programming system to exploit sparseness efficiently. Economies of scale arise when selecting a set of new pipelines to expand the capacity of a given network. This problem involves non-linear functions, but is essentially an integer programming problem because we must use branch and bound methods to find the best combinations of pipelines, and pipeline diameters. An unsuccessful and a subsequent successful formulation for this problem are discussed. A non-linear programming model for allocating resources in health care is outlined. A model for multi-time-period production scheduling with stochastic demands is also outlined. The model requires data defining the uncertainties in demand forecasts, and the extent to which these are correlated with each other and with past sales. The existence of software for this model may encourage more people to quantify these data.

A Multi-Objective Framework for Environmental Management Using Goal Programming

Abstract: The scope of environmental management is to reach as much as possible a socially acceptable balance between economic benefits and resulting environmental quality; such a balance is defined in terms of politically established criteria and goals. En­ vironmental management should also be concerned with integrating the often conflicting sub-goals of the economic, environmental, and technological com­ ponents of the economic-environmental system into a multiple objective for guiding the development and evaluation of alternative policies. This paper presents a prescriptive framework for environmental management at the regional level which allows for a sufficiently realistic representation of the total system, deals sys­ tematically with multiple objectives through goal programming optimization techniques, and suggests an effective interaction between the decision-maker and the analyst for devising compromises among conflicting objectives. This is de­ signed as an improvement over the ad hoc trial-and-error analysis based on trade­ off relations. In essence, the procedure outlined here is directed toward establishing the economic-growth/environmental-quality possibility frontier of a region. The Environmental Management Problem The realization that there exists a limit to the capacity of the envi ronment of a site or region to carry undesirable waste p roduc t s , which almost always accompany the benefit generating product ive and consumpt ive activities, has * This work was supported by a grant to the author from the National Research Council of Canada and the Department of Education of Quebec.

The Handling of Goals in Marketing Problems: A Mathematical Approach

Abstract: A market share problem is considered which has only a limited amount of data available, often only assigned attributes, and no quantifiable objective—only a number of goals which may possibly be achieved. The technique of goal programming is employed which builds the situation into a model with an objective and permits solution of this model. The approach is then generalised to show that it is applicable to a variety of problems which arise in marketing and related areas.

Planning the Media Mix through Goal Programming

Abstract: research have attempted to develop models with the intention of providing management with a tool for the effective selection of media mix. The reason for such interest lies in the fact that in the absence of scientific tools a media planner may find it necessary to evaluate each possible medium sub jectively. This evaluation is usually done by guess work rather than by the application of scientific analysis. For example, in the case of a media planner who, within the limitations of a certain budget, is confronted with a choice between a dozen or more advertising vehicles, each with a different circulation, cost, audience, coverage, and timing, it is easy to recognize the difficulty of such a choice. Scientific tools, therefore, are designed to make it possible for the media planner to consider many more alternative vehicles than otherwise possible, thus enabling him to select an optimum media mix.