Showing papers on "Goal programming published in 1979"

Application of mathematical programming to metal cutting

Abstract: In this paper, a general problem involving the single-pass, single-point turning operation is introduced. Different mathematical models and solution approaches for solving various single objective problems are described. The mathematical properties of the minimization of cost and maximization of production rate solutions are discussed in detail. The solution approaches used are differential calculus, linear programming, and geometric programming. Finally, a multiple criteria machining problem is formulated and solved using goal programming techniques.

Allocation of research and development funds: A zero-one goal programming approach

Abstract: The research and development project selection process is one of the most difficult and important problems faced by management. It is typically complicated by indivisibility of projects and multiple and conflicting objectives, in addition to limitations on funding, facilities, and qualified researchers. In this paper a case example involving a high technology electrical equipment manufacturer is developed to illustrate this problem using zero—one goal programming to accommodate indivisibility of projects in addition to multiple and conflicting goals. The model presented is an attempt to provide managers with a robust tool for allocating scarce resources among research and development projects.

Management Science Relations for Evaluation and Management Accountability

Abstract: : Management accountability as an added dimension for management science research is examined from the standpoint of possible uses in some of the newer comprehensive auditing approaches to propriety, effectiveness and efficiency evaluations of management and organization behavior Attention is centered on non-market activities and not-for-profit organizations Goal focusing is examined, for example, as a relatively recent extension of goal programming for use in effectiveness evaluation and as an alternative to utility theoretic approaches in national goals accounting systems designed to deal with programs or objectives involving numerous kinds of off-market activities The bulk of the paper, however, is devoted to a new mathematical programming model for deriving analytic representations of extremal frontiers or envelopes from empirical data and for measuring the efficiency of not-for-profit entities An illustrative application to a recently completed large-scale social experiment in educating disadvantaged children in the US public schools is used to show how distinctions may also be drawn between program efficiency and management efficiency The appendix develops a canonical form for the types of statistical distributions involved It also provides a beginning for dealing with statistical properties of the extremal relations obtained by applying these kinds of mathematical programming models to observational data

A chance-constrained goal programming model to evaluate response resources for marine pollution disasters. Research report

Abstract: This paper develops a model to aid Coast Guard managers in formulating appropriate policies with respect to planning for various types of equipment required to contain major pollution incidents The model is elaborated in terms of three primary stages of response: offloading, containment, and removal The zero order rule of chance-constrained programming is used to obtain a deterministic equivalent of the original chance-constrained model This is then replaced by a goal programming formulation to allow for plans that come 'as close as possible' to desired quality and risk levels for each pertinent region and type of incident Numerical examples illustrate potential uses of the model with special emphasis on its value for budgetary (equipment) planning by central management that extends to evaluation of risk and performance quality levels, as well as the usual dual evaluator approaches for evaluating initially prescribed levels of equipment and their efficiency coefficients (Author)

Implementation of Large-Scale Financial Planning Models: Solution Efficient Transformations

Abstract: It has long been recognized in the literature of finance that the robustness and analytical potential of mathematical programming procedures can be utilized to structure highly complex decision environments and to ascertain quickly and efficiently the dominant set(s) of actions for achieving an explicit objective(s). Although some formulations involve nonlinear relationships (for instance [13] [15]), the vast majority of the models appearing in the finance literature are variants of linear programming, including such identifiable methodologies as linear programming, goal programming, networks, integer programming, mixed integer programming, and chance-constrained programming. The decision processes for capital budgeting ([25] [1] [2] [4] [14] [16] [24]), working capital management ([20] [18] [21] [6]), cash management ([17] [23]), and portfolio selection ([22] [24]), have been structured as linear programs and have contributed significantly to understanding the dynamics of financial systems. Given the potential of these mathematical approaches, the limited industrial use of financial optimization models is disturbing.

Goal programming for reconciling economic, even-flow, and regulation objectives in forest harvest scheduling

Abstract: To resolve the traditional quantifiable but incommensurate objectives of perfect regulation, maximization of present net worth, and even-flow harvest, goal programming (GP) was applied to a sample forest, providing optimal solutions for each goal and a compromise solution that jointly considered all three as weighted goals. Goal programming overcame problems of infeasible specification and satisfied alternate criteria in cases with multiple optima. The GP approach provided a means of considering each of the three goals and minimizing the appropriately weighted deviations.

An Exploration of Various Goal Programming Formulations — with Application to Advertising Media Scheduling

Abstract: This article examines various goal programming formulations, including the well-known MINSUM and MINMAX models, and their extensions to include explicit recognition of "soft" constraints. The differences in the nature of the solution sets and their sensitivity are illustrated using a real-life advertising media scheduling problem involving 63 media options. It is concluded that MINMAX models may often be more useful in gaining insight into the nature of the problem's solution, and that both MINSUM and MINMAX models may be considerably enriched through the use of soft constraints.

Integer goal programming in advertising media selection

Abstract: The media selection decision allocates advertising dollars among competing media so as to optimize promotional and corporate objectives. Linear programming attempts to model this process have been complicated by multiple and often conflicting management goals, the need for integer solutions, and nonlinearities. This study offers a technique that is sufficiently robust to simultaneously handle these problems. An alternative media selection framework is presented and the results of an illustrative application of integer goal programming are discussed. The proposed model improves on linear programming by success fully providing for optimal, integer solutions in settings that more realistically reflect the complexity of the media decision environment.

Alternate programming structures for bank portfolios

Abstract: Recently a number of mathematical programming models have been developed to assist banks in their portfolio (balance sheet) management decision making. Generally, the model structures used may be classified as either linear, linear goal, or two-stage linear programming. Of these, linear programming models are the most common. The purpose of this paper is to discuss the optimal bank portfolio management solutions produced by each of the above programming structures. In addition, a new model structure, two-stage linear goal programming, is developed and compared to the other structures. From a decision-making perspective, this new model structure is found to provide additional useful information.

Goal Programming Models for Assigning Search and Rescue Aircraft to Bases

Abstract: This paper develops several variations of a goal programming model for optimally allocating a fleet of search and rescue aircraft to a fixed set of available and potentially available bases In addition, the model determines the number of aircraft of each type from each base (at which that type has been stationed) to assign to the various search locations The criterion for optimality is to maximize the probability of locating each distress in a specified time These models are then modified to include fleet planning issues Solution procedures relating to the models are discussed

A multiple-objective planning methodology for information service managers

Abstract: The problem of allocation of scarce resources in an organization is frequently complicated by the presence of multiple, often conflicting, managerial objectives and a high degree of ambiguity in the definition of the organization's purpose. This is particularly true within the operating environment of many public and privately operated information centers and special libraries. In recent years, the managers of such centers have been pressed by both the funders and the users of their services to improve the efficiency of their operations. Although techniques are available for determining and evaluating various measures of the performance of these centers, relatively little has been accomplished with respect to the more fundamental decision making problem of allocating resources in order to optimize the achievement level of the various objectives of the decision maker. The problem addressed in this research is the extension of existing techniques to more adequately deal with the resource allocation problem in an ambiguous environment. The technique known as goal programming is extended and a new methodology, termed goal-range programming, is presented. The goal-range programming methodology is related to the information center planning environment through examples derived from the interaction of the authors with several centers.

Conflict resolution in investment decisions: implementation of goal programming methodology for dual-purpose funds*

Abstract: Goal programming (GP) is designed to resolve allocation problems with conflicting goals. Both goals and constraints are incorporated in the allocational decision, and the objective function is stated in a way that, upon solution, yields a result “as close as possible” to the priority-weighted goals. The present paper applies GP methodology to the investment decision of dual-purpose funds (DPFs), that are required by law to pursue allocational decisions with potentially conflicting objectives. It provides an empirical demonstration that DPF managers could have improved their investment selection and subsequent performance by the use of GP methodology. Finally the paper stresses the importance of sensitivity analysis to improve both the goal-ranking and target-selection aspects of the methodology and provides a limited but illuminating empirical demonstration of post-optimality analysis.

A goal programming model for output planning

Abstract: This paper describes a goal programming procedure for determining satisfactory output plans for a work center. The situation being modeled is one in which work center inputs are known but vary significantly across time periods. Input levels are fixed relative to a given master production schedule, and output levels can be varied only within certain prescribed limits, at least in the short term. The similarity of the output planning problem to the more familiar aggregate planning problem is noted and discussed.

Goal programming sensitivity analysis using interval penalty weights

Abstract: This paper presents an application of the vector-maximum research [4–8] to the sensitivity analysis of goal programming problems as several of the criterion function penalty weights are simultaneously and independently varied. A generalized goal programming capability is presented and a six-stage analytic procedure is described. The problem is generalized in the sense that the regular goal programming penalty weights can be expanded to intervals if desired. The solution procedure is new in that it depends upon an algorithm for the vector-maximum problem, “criterion cone” contraction procedures, and “filtering” techniques. Together they are able to generate and process all extreme points on the portion of the surface of the goal programming “augmented” feasible region corresponding to the interval penalty weights specified. In effect, the procedure and adapted algorithm of this paper delivers to goal programming an operational power of sensitivity analysis not previously available to users. A numerical example is provided in order to illustrate the computerized application of the total goal programming procedure outlined.

Solution Approaches for Network Flow Problems with Multiple Criteria

Abstract: : A network variant of the multicriteria linear programming problem is presented The primal simplex multicriteria algorithm first developed by Yu and Zeleny is specialized to handle the simple basis structure of the multicriteria uncapacitated transshipment problem Specifically, the basis tree representation and updating techniques that have proven to be successful for single criterion network flow problems are used to substantially reduce the computational effort required for the multicriteria simplex algorithm The fundamental theoretical results for the general multicriteria linear programming problem are presented followed by a brief review of the relevant aspects of the network basis structure Next, the specialized multicriteria primal simplex algorithm for the uncapacitated transshipment problem is presented and a small example problem is given Then a network variant of the surrogate criterion linear programming approach is presented For sake of illustration, the shortest path problem is used as the class of networks to be solved However this approach can be easily extended to any of the other classes of network flow problems In this section an interactive solution procedure is described that involves both the decision maker and the computer at each stage

a Multiobjective Model for Regional Water Quality Management

Abstract: Linear programming is the simplest of all the optimization techniques used in regional water quality management studies; but the technique can optimize only one goal. When there are multiple goals with the same or different priorities, goal programming is a useful decisionmaking tool. This paper illustrates the application of goal programming to a regional water quality management problem where the following two goals are considered: (1) minimize the total cost of waste treatment, and (2) maintain the water quality goals (dissolved oxygen) close to the minimum level stated in the stream standards.

Goal Programming for Decision Making in Closely Held Businesses

Abstract: There are few analytical and managerial tools available to assist the small business decision maker. This paper presents a practical goal Programming model which can be easily generalized to fit the planning needs of most small businesses. Specifically the model explicitly considers the multiple goals and priorities of the owner-manager and determines if these goals can be accomplished under various demand Projections. An illustrative example of the use of this model with a small fast-food business is given.

Two-stage optimization in a transportation problem

Abstract: A study of the economic distribution of maize throughout South Africa is reported. Although the problem of minimizing total transportation costs in such a situation is a classical one, and its solution is well known, there was in this case a high degree of degeneracy in the system and thus the solution was not unique. Also, since a user is required to pay his own transportation costs, the various optimal solutions were not equivalent. A secondary problem thus arose, viz. that of selecting from these optimal solutions the one which would be fairest to all users. A heuristic and a goal programming method for solving this secondary problem are discussed.

Goal Programming Application to Assignment Problem in Marketing

Abstract: The purpose of this article is to draw a comparison between linear programming and goal programming with respect to the assignment model. The presentation of goal programming attempts to strike a balance between theory and practice by presenting a simplified example to explain the principles and computation process of this technique without confusing the readers with excessive calculations. Goal programming is a modification and extension of linear programming which allows a simultaneous solution of a shstem of complex conflicting objectives rather than a single objective as in the case of linear programming. Thus, goal programming is a way of handling previously unsolvable linear programming problems. In comparing linear programming versus goal programming, two cases of assignment are presented. The first case had one single objective where linear programming is used while the second has multiple objectives where goal programming is used.

Reliability Optimization with Multiple Properties and Integer Variables

Abstract: The paper considers the problem of determining an optimal design of a reliability system with multiple properties and integer variables. This problem is formulated as a multiobjective nonlinear pure-integer programming problem. The method for solving this problem consists of three techniques: 1) narrowing a given feasible region if the region is too wide, 2) obtaining exactly the set of Pareto-optimal solutions, and 3) selecting the best one for decision makers from the set of Pareto-optimal solutions. An increase in the number of objective functions scarcely affects the size of problem. An example is included.

a Viable Methodology to Implement the Principles and Standards

Abstract: The “principles and standards for planning water and related land resources” were made effective October 25, 1973. The document was noticeably deficient in suggestions for the necessary implementing procedures to ensure its success. Current implementing procedures are based on an incorrect premise of maximizing a single objective subject to non-quantified constraints. A successful implementation of multiple objective planning requires optimizing simultaneously several competitive goals. A system of goal programming has been developed and applied to decisionmaking situations as a test of its usefulness in planning for multiple objective water resources projects. The result is a project planning process which can be replicated for adjustments in expected resource supplies or demands to provide a tradeoff matrix between economic and environmental objectives as well as traditional functional purposes. This procedure, tested on the Cross Florida Barge Canal, is an integrated analysis of economic and environmental values which may be as effective in implementing multiple objective planning as the “Green Book” was in developing the now inappropriate benefit cost analysis.

An Application of Goal Programming at Lord Corporation

Abstract: In this paper we report an application of a goal programming model at Lord Corporation. The model was developed and used at Lord to assist top management in the problem of allocating funds to competing research and development projects. Ten goals were established, and 25 projects considered. We describe those goals, the program results, and management's involvement in model construction and use.