A MODEL FOR THE PREDICTION OF JUROR DECISIONS*

A logit model approach designed to assign a probability whether prospective jurors will favor the defendant or plaintiff in a case, as a function of perceived individual juror characteristics, is described in the context of a case situation. The model, which has been programmed on a hand-held computer, is designed for implementation in courtroom settings to help defense attorneys evaluate and select jurors in order to minimize the likelihood of large jury awards. Empirical tests of the model are also described.     

A MODEL FOR ALLOCATING LIMITED RESOURCES WHEN MAKING SAFETY-STOCK DECISIONS

This article describes the application of a goal programming model to set safety stocks in a fixed-interval, variable order-quantity inventory system. The firm is constrained by limited storage space and capital in the determination of inventory levels. The costs resulting from the use of the proposed model are contrasted with costs resulting from an alternative method based solely upon a forecasting model. The costs considered are holding costs, stock-out costs, and the costs of acquiring excess resources. Excess resources are required when safety-stock levels, determined by the forecasting model, would utilize more than the available capital and space. The proposed model eliminates the need for acquiring excess resources at the risk of increasing the frequency and size of stock outs. A trade-off occurs between the potential for greater stock-out costs versus the costs of acquiring excess resources-borrowing additional funds or renting additional space. The goal programming model was used to set safety stocks for 15 product groups over a 46 month evaluation period for a multiproduct firm. Significant reductions in costs can be realized if the proposed model is used.     

A BAYESIAN APPROACH TO PLANT-LOCATION DECISIONS*

The paper addresses the problem of plant location in the formal context of decision making under uncertainty and presents a framework employing Bayesian analysis in the collection and assessment of information. As a general model, the Bayesian approach is shown to subsume two practical approaches common to plant-location literature: satisficing and spatial hierarchy of plant-site characteristics.     

Concepts,Theory, and Techniques A NEW METHODOLOGY FOR ORDINAL MULTIOBJECTIVE DECISIONS BASED ON FUZZY SETS

Using the structural forms supplied by fuzzy set theory and approximate reasoning, a new method is presented for solving multiple-objective decision problems for which the decision maker can supply only ordinal information on his preferences and the importance of the individual objectives.     

USING SHORT-TERM DEDICATION FOR SCHEDULING MULTIPLE PRODUCTS ON PARALLEL MACHINES

In a multiproduct, parallel machine environment, it may be beneficial to dedicate one or more of the machines to a single product for consecutive time periods. However, previous lot-sizing and scheduling models usually do not allow for such short-term dedication. This paper presents a mixed-integer programming model that allows for short-term dedication in scheduling parallel machines serving multiple products with dynamic demands. It also describes a Lagrangian-based algorithm for solving such scheduling problems. An experiment verifies the effectiveness of the algorithm and demonstrates the importance of allowing for short-term dedication. Capacity utilization, one of six factors investigated, greatly affects the use of dedication. A strong interaction effect between the number of machines and the number of products is also seen with respect to the usefulness of short-term dedication. A measure of estimated production frequency incorporates these two factors along with magnitudes of periodic demands. This measure has a strong relationship with the amount of short-term dedication used in solutions. Operations managers employing parallel machines could use the experimental results in deciding whether to use short-term dedication to reduce costs.     

A COMPARISON OF PRODUCTION SCHEDULING POLICIES ON COSTS,SERVICE LEVEL,AND SCHEDULE CHANGES

We consider a single product, single level, stochastic master production scheduling (Mps ) model where decisions are made under rolling planning horizons. Outcomes of interest are cost, service level, and schedule stability. The subject of this research is the Mps control system: the method used in determining the amount of stock planned for production in each time period. Typically, Mps control systems utilize a single buffer stock. Here, two Mps dual-buffer stock systems are developed and tested by simulation. We extend the data envelopment analysis (dea ) methodology to aid in the evaluation of the simulation results, where Dea serves to increase the scope of the experimental design. Results indicate that the dual-buffer control systems outperform existing policies.     

MODELING MANAGERS' EFFORT-LEVEL DECISIONS FOR A WITHIN-PERSONS EXAMINATION OF EXPECTANCY THEORY IN A BUDGET SETTING

Ronen and Livingstone [42] have proposed that expectancy theory can provide a unifying framework for accounting research aimed at the motivational impact of budgets on managers. Unfortunately, researchers attempting to use this approach have employed an inappropriate across-persons methodology. This paper uses the decision modeling approach we recently developed [48] to provide a within-persons examination of Vroom's force model in a budget setting. An examination of two hypotheses questions the belief expressed in the budget literature to the effect that the use of currently attainable cost standards results in a cost budget with unique motivating characteristics. Strengths of this research are the use of the within-persons approach and the use of an innovative measure of motivational force.     

A PENSION DECISION MODEL FOR THE EMPLOYER*

Recently, the Financial Accounting Standards Board (FASB) reevaluated accounting for pension plans. The issue is emotional and highly political in nature. The FASB attempted to justify its approach on the basis of measuring economic activity, but it failed to provide much in the way of analytical support. This paper provides a managerial decision model and an economic basis for the existence of pension plans. A pension plan is described as a cost-saving, risk-sharing, incentive contract. The analysis is developed using agency theory. The model presented here meets three suggested objectives of an employer: 1. Maximization of utility through the maximization of profit 2. Ability to conform the risk characteristics of an employment contract to the risk characteristics of the employer 3. Diversification of the risk inherent in the employment contract Profit is maximized by producing cost savings associated with employee tenure and loyalty. Sharing cost savings with employees (i.e., offering a pension plan) meets the above objectives. The employer determines the optimal sharing rate for the expected cost savings. An examination of the employer's underlying decision process reveals implications for pension plan accounting which generally are consistent with and support the FASB's Statement of Financial Accounting Standards No. 87 [5].     

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.     

USING L2 ESTIMATION FOR L1 ESTIMATORS: AN APPLICATION TO THE SINGLE-INDEX MODEL

The bootstrap method is used to compute the standard error of regression parameters when the data are non-Gaussian distributed. Simulation results with L₁ and L₂ norms for various degrees of “non-Gaussianess” are provided. The computationally efficient L₂ norm, based on the bootstrap method, provides a good approximation to the L₁ norm. The methodology is illustrated with daily security return data. The results show that decisions can be reversed when the ordinary least-squares estimate of standard errors is used with non-Gaussian data.     

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.     

AN EXPERIMENTAL MODEL FOR INVESTIGATING THE SENSITIVITY OF JOB SHOP PERFORMANCE TO JOB RELEASE TIME DISTRIBUTION PARAMETERS

Work flows in a job shop are influenced by the load per release and time interval between release factors. We focus on the latter factor, job release times. Building on Elvers' work, this study evaluates the impact of different job release time distributions on shop performance. Using a computer simulation of a random job shop and a full factorial experimental design, we demonstrate that the type of distribution does affect performance–a finding consistent with results from job shops characterized by good shop floor control practices. These findings are explained by examining the shape and variance traits of the underlying job release time distributions.     

A LOGIT-MARKOVIAN MODEL OF CONSUMER PURCHASE BEHAVIOR BASED ON EXPLANATORY VARIABLES: EMPIRICAL EVALUATION AND IMPLICATIONS FOR DECISION MAKING*

This study describes and empirically evaluates an approach to modeling purchase behavior that integrates a Logit-Markov-based multivariate brand-choice model structure with stochastic components. Using actual market data for a frequently purchased consumer product obtained from a consumer purchase panel survey, the paper highlights some marketing applications of the model. Given its general structure, the model can aid marketing managers in defining and evaluating target market segments and in assessing the impact of alternative marketing strategies.     

ON THE COST OF NEW ISSUES OF COMMON STOCK: AN AMENDMENT TO THE CONSTANT GROWTH MODEL

Expansion decisions by corporate managements are importantly influenced by their perceptions of the firm's cost of capital and especially the cost of common equity. Their decision making is then influenced by the common perception that new issues of common stock are more costly than retained earnings because of the flotation costs inherent in new issues. Thus a rate of expansion that requires new issues is downgraded in decision making. In this paper the authors show that in actuality new issues of common equity may be less costly than retained earnings. Thus different decision-making implications are clearly involved.     

A DYNAMIC PROCESS MODEL OF DISSATISFACTION FOR UNFAVORABLE,NON-ROUTINE SERVICE ENCOUNTERS

This paper explores the effect of expectations and information on customer dissatisfaction in unfavorable, nonroutine service encounters. In complex services (e.g., health care) with multiple encounters and wide range of services, customers use some of the services rarely or only once. In such encounters, customers may not have clear expectations regarding the process and/or outcome of the impending service delivery. This may increase the likelihood of the customer to perceive poor service or be dissatisfied. Hypotheses regarding the nature of expectations—levels, uncertainty, consistency—and its affects on customer dissatisfaction are tested using a dynamic process model of customer dissatisfaction.     

A MODEL FOR SWITCHING DISPATCHING RULES IN REAL TIME IN A FLEXIBLE MANUFACTURING CELL

We model choice of dispatching rules in real time (system state dependent) as a pattern recognition problem, using a modified version of Data Envelopment Analysis. A data base of system state and performance values is created from extensive simulation, and this data base is used to train the pattern-recognition model. Our results show that the model is very effective in choosing a mix of dispatching rules over a period of time, varying the mix with system objectives, and performing better than the strategy of using fixed rules. We show how “If-Then” decision rules can be created from the model and portrayed in a decision-tree-like diagram. Since such decision rules are based on rigorous mathematical foundations, optimization will be ensured in our approach.     

A MODEL FOR BATCH ADVANCED AVAILABLE‐TO‐PROMISE

The available‐to‐promise (atp) function is becoming increasingly important in supply chain management since it directly links production resources with customer orders. In this paper, a mixed integer programming (mip) ATP model is presented. This model can provide an order‐promising and ‐fulfillment solution for a batch of orders that arrive within a predefined batching interval. A variety of constraints, such as raw material availability, production capacity, material compatibility, and customer preferences, are considered. Simulation experiments using the model investigate the sensitivity of supply chain performance to changes in certain parameters, such as batching interval size and customer order flexibility.