A GOAL PROGRAMMING MODEL FOR ALLOCATING HUMAN RESOURCES FOR THE GOOD LABORATORY PRACTICE REGULATIONS

The Food and Drug Administration's final Good Laboratory Practice regulation will have a far-reaching effect on testing laboratories that are conducting nonclinical laboratory safety tests. This paper develops a goal programming decision model suitable for setting objectives, meeting budgetary and operational constraints, planning personnel utilization, and evaluating different proposals for allocating laboratory personnel to implement a program.     

WARRANTY COST ESTIMATION: A GOAL PROGRAMMING APPROACH

This paper proposes a goal programming approach to the warranty cost estimation problem. Past research on this topic has mostly dealt with a single objective—the minimization of the warranty reserve cost or the maximization of profit. A more realistic approach to warranty cost problems could, however, involve several goals, some of which might be conflicting to others. In this paper, three goals are prioritized. The goals considered are minimization of warranty reserve cost per unit, offering a minimum level of warranty time based on an allowable proportion of failures within the warranty period, and capturing a minimum specified market share of the product. An example is illustrated using the proposed formulation, and goal achievements are discussed.     

ON THE (RE)DISCOVERY OF FUZZY GOAL PROGRAMMING

An earlier paper and subsequent commentaries in Decision Sciences described purportedly new methods for formulating and solving goal programming problems with fuzzy goals. This note suggests that much of what these articles propose is but a rediscovery of well-known results.     

A NOTE ON DECISION-MAKING CRITERIA FOR ALGORITHM SELECTION: REDUCING GOAL PROGRAMMING COMPUTATIONAL EFFORT

Most linear goal programming (LPG) algorithms are based on a simplex-type solution method that begins with an initial simplex tableau with solution-set variables (basic variables) consisting of all-negative deviational variables or all-positive deviational variables. This note (1) demonstrates how computational solution effort can be reduced by selecting the appropriate all-negative or all-positive deviational variable algorithm and (2) describes a procedure that can be used to aid decision makers in selecting the appropriate algorithm for all types of applied goal programming (GP) models. Results of this study reveal the procedure as accurate and providing computational time savings when applied to a large sample of GP problems.     

A LINEAR PROGRAMMING MODEL OF RESOURCE ALLOCATION IN A UNIVERSITY*

This paper describes the application of a linear programming model to the process of resource allocation at Illinois State University. The model developed is a general model designed to assist academic administrators in allocating the scarce resources at their disposal. The model, and the assumptions upon which it is based, may be altered to suit the particular decision criteria of any particular academic institution. The linear programming model developed here maximizes an objective function which reflects the total net social profit realized by the university on its outputs to society. This objective function was maximized subject to a myriad of constraints which reflected the limitations of budget, space, and other scarce inputs. Further, the constraints reflect the joint interdependency of many parts of the university and the particular educational delivery system employed by a given department. The outputs of the model include the optimal mixture of academic outputs by the university, the optimal distribution and usage of inputs inside the university, and the shadow price of each input. While the linear programming model developed here is general and may be applied to any university, it was applied to Illinois State University in this paper. In the case of Illinois State University, the model recommended a substantial reduction in emphasis upon teacher education.     

EVALUATING ALTERNATIVE LINEAR PROGRAMMING MODELS TO SOLVE THE TWO-GROUP DISCRIMINANT PROBLEM

The two-group discriminant problem has applications in many areas, for example, differentiating between good credit risks and poor ones, between promising new firms and those likely to fail, or between patients with strong prospects for recovery and those highly at risk. To expand our tools for dealing with such problems, we propose a class of nonpara-metric discriminant procedures based on linear programming (LP). Although these procedures have attracted considerable attention recently, only a limited number of computational studies have examined the relative merits of alternative formulations. In this paper we provide a detailed study of three contrasting formulations for the two-group problem. The experimental design provides a variety of test conditions involving both normal and nonnormal populations. Our results establish the LP model which seeks to minimize the sum of deviations beyond the two-group boundary as a promising alternative to more conventional linear discriminant techniques.     

A GOAL PROGRAMMING MODEL FOR OUTPUT PLANNING

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.     

A PREEMPTIVE VALUE-FUNCTION METHOD APPROACH FOR MULTIOBJECTIVE LINEAR PROGRAMMING PROBLEMS

Many common decision-making situations require integration of a number of objectives or goals into a single objective function to be optimized. A number of techniques for performing this analysis are presented in the literature. These procedures generally require a tradeoff between realism and tractability. The present approach uses the idea of preemptive priority levels with an approximate value function at each level. An efficient experimental design is used to assess directly a quadratic approximation of the multiattribute value functions. An existing algorithm is then used to solve the resulting problem. This procedure is shown to give good results when compared to both the value-function method and goal programming.     

A GOAL PROGRAMMING MODEL FOR INSURANCE AGENCY MANAGEMENT

Rapid changes in the business environment have forced property and casualty insurance agencies to re-examine their objectives. The need to maintain contact with a large number of insurers in order to place business, efforts to reduce cost by utilizing company services, and the expanding demand for commercial lines have all had major impacts on these agencies. In this paper, a goal-programming model is developed for use in insurance agency decision-making involving multiple and often conflicting goals. An example is presented.     

A Multiobjective Approach and Empirical Application of Sales-Organization Design

This paper addresses the critical issue of determining the appropriate structural design of a sales organization. We propose a new approach for organizing the selling function using multiobjective modeling techniques. The proposed approach considers the situational characteristics under which the sales organization operates, trade-offs among multiple sales-effectiveness criteria, and the “fit” between the situational characteristics and the structural dimensions (i.e., the structure-contingency relationships). Using data collected from sales branches of brokerage firms, we develop appropriate structural designs for a sales organization using the proposed procedure. The paper concludes by outlining the various decision-making implications of the approach.     

A CHANCE-CONSTRAINED GOAL PROGRAMMING MODEL FOR BANK LIQUIDITY MANAGEMENT

The bank liquidity-management model presented herein is formulated as a chance-constrained goal programming model thus allowing uncertainty to be incorporated into a lexicographic solution of the problem. This model plans both for the reserve-short and reserve-excess bank, describing appropriate investment or disinvestment action during the computation period. The testing of the model was performed on two banks, one a small rural bank with assets of about $25 million, and the other a large metropolitan bank with assets well in excess of $1 billion, with the actual bank results and the prescribed strategy of the model being compared over a one-year period. Additionally, sensitivity analysis was performed on the model, providing valuable information concerning the risk-return relationships associated with each management goal. It is hoped that this model will provide bankers with both an effective diagnostic and operational tool in the development of liquidity-management planning decisions.     

A MATHEMATICAL PROGRAMMING MODEL FOR FACULTY COURSE ASSIGNMENTS

This paper describes an integer programming model that we believe has advantages over other models for determining faculty teaching assignments. Unlike previously proposed models, in this model the decision variables represent the assignment of complete teaching schedules rather than courses to faculty members. The paper compares the proposed model with previous models for application in an academic department.     

ESTIMATION OF MISCLASSIFICATION PROBABILITIES IN THE LINEAR PROGRAMMING APPROACHES TO THE TWO-GROUP DISCRIMINANT PROBLEM

Expressions for misclassification probabilities are derived under a contaminated multivariate normal model for the linear-programming approaches to the two-group discriminant problem.     

A DYNAMIC PROGRAMMING MODEL FOR OPTIMAL ALLOCATION OF DECISION TIME

Recurrent decision making by a lower-level manager can be viewed as a sequential decision process in which time and uncertainty are limiting factors. Under these conditions, the manager must determine how to best utilize his decision making time consistent with his own particular set of decision values. A dynamic programming model was devised to determine the optimal (consistent) allocation of decision time among five different types of problems for a sequence of simulated recurrent decision situations. Fifty-one lower-level managers were interviewed about their use of decision time and decision procedures. The model was validated by comparing model assumptions and results with the findings from the interviews. The model was used to determine the effects of variations in the levels of time available and uncertainty upon the optimal allocation of decision time.     

DYNAMIC LOT SIZING FOR A SINGLE-FACILITY MULTIPRODUCT PROBLEM IN A ROLLING-HORIZON ENVIRONMENT

This paper considers a production planning model for a single-facility multiproduct problem where backlogging is not allowed. A planning-horizon theorem is derived. From that theorem, a forward algorithm for finding an optimal solution over a finite horizon and a procedure for selecting the first-period production in a rolling-horizon environment are developed. Computational results from a set of simulation experiments designed to investigate the cost effectiveness of the procedure demonstrate its effectiveness.     

SOME DIFFICULTIES AND IMPROVEMENTS IN APPLYING LINEAR PROGRAMMING FORMULATIONS TO THE DISCRIMINANT PROBLEM

In this paper, we discuss some disturbing features of two linear programming (LP) approaches to the discriminant problem. Specifically, we show that both approaches are sensitive to the choice of origin for the data although, intuitively, placement of origin should have no effect on the method of assigning cases to groups. In addition, we show that these LP approaches may lead to discriminant functions which assign all cases to the same group. We show that the usual statistical approach to this problem does not share these difficulties, and we make recommendations for implementing these LP approaches which help to alleviate the difficulties.     

Concepts,Theory, and Techniques DISTRIBUTED COMPUTER SYSTEM DESIGN: A MULTICRITERIA DECISION-MAKING METHODOLOGY*

The design of distributed computer systems (DCSs) requires compromise among several conflicting objectives. For instance, high system availability conflicts with low cost which in turn conflicts with quick response time. This paper presents an approach, based on multi-criteria decision-making techniques, to arrive at a good design in this multiobjective environment. An interactive procedure is developed to support the decision making of system designers. Starting from an initial solution, the procedure presents a sequence of non-dominated vectors to designers, allowing them to explore systematically alternative possibilities on the path to a final design. The model user has control over trade-offs among different design objectives. This paper focuses on the details of the mathematical model used to provide decision support. Accordingly, a formulation of DCS design as a multicriteria decision problem is developed. The exchange search heuristic used to generate nondominated solutions also is presented. We argue that multicriteria models provide a more realistic formulation of the DCS design problem than the single-criterion models used widely in the literature. While obtaining a clear definition of design objectives (single or multiple) is an important activity, by explicitly acknowledging the trade-offs among multiple objectives in the design process, our methodology is more likely to produce a better overall design than methods addressing a single criterion in isolation.     

A NEW CLASS OF MODELS FOR THE DISCRIMINANT PROBLEM

Discriminant analysis is relevant to business decision making in a variety of contexts, such as when one decides to make or buy a specified component, fund a venture project, or hire a particular person. Potential applications in artificial intelligence, particularly in the area of pattern recognition, have further underscored the importance of the field. A recent innovation in discriminant analysis is provided by special linear programming (LP) models, which offer attractive alternatives to classical statistical approaches. The scope of application in which discriminant analysis can be advantageously employed is broadened by the flexibility to tailor parameters in the LP approaches to reflect diverse goals and by the power to explore the sensitivity of these parameters. In spite of the promise of the LP formulations, however, limitations to their effectiveness have been uncovered in certain settings. A recent advance involving a normalization construct removes some of the limitations but entails solving the LP model twice (to allow for different signs of a normalization constant) and does not yield equivalent solutions for different rotations of the problem data. This paper introduces a new model and a new class of normalizations that remedy both remaining limitations, making it possible to take advantage of the modeling capabilities of the LP formulations without the attendant shortcomings encountered by earlier investigations. Our development shows by empirical testing and illustrative analysis that the quality of solutions from LP discriminant approaches is more favorable (relative to the classical model) than previously supposed.     

A DISCRETE DYNAMIC OPTIMIZATION MODEL FOR MADE-TO-ORDER COST ANALYSIS

A model is presented that yields optimal production rates for a firm producing a contracted order. The model is unique in that it considers the influence of production rate and learning on total program cost. An application to the specific characteristics of two military production programs is presented. As demonstrated by the application, models of this type may be used as decision-making tools when negotiating the cost impact of contract modifications.