Antonie Stam

Selecting a flexible manufacturing system using multiple criteria analysis

A visual interactive decision support framework designed to aid the decision-maker, typically top management, in selecting the most appropriate technology and design when planning a flexible manufacturing system (FMS) is described. The framework can be used in the preinvestment stage of the planning process, after the decision in principle has been made to build an FMS. First, both qualitative and quantitative criteria are used to narrow the set of alternative system configurations under consideration down to a small number of most attractive candidates. After this prescreening phase, a multiobjective programming model is formulated for each remaining configuration, allowing the manager to explore and evaluate the costs and benefits of various different scenarios for each configuration separately by experimenting with different levels of batch sizes and production volumes. The system uses visual interaction with the decision-maker, graphically displaying the relevant trade-offs between such relevant perfo...

Reducing conflict between marketing and manufacturing

Abstract The competitive strategies of an industrial firm may be jeopardized by interfunctional conflict and myopic functional-level decisions. Conflict and misunderstanding can be particularly intense between the marketing and manufacturing functions. This article presents a typology of decisions where strong potential exists for friction between marketing and manufacturing. Company examples are included to provide a degree of face validity for the frequency of such problems. Four major facilitating mechanisms suggested by past research are discussed. Finally, the potential of group decision support systems as a fifth mechanism for facilitating interfunctional cooperation and understanding is examined. Several applications of group decision support systems are presented to illustrate their promise as linking mechanisms.

Artificial neural network representations for hierarchical preference structures

In this paper, we introduce two artificial neural network formulations that can be used to assess the preference ratings from the pairwise comparison matrices of the Analytic Hierarchy Process. First, we introduce a modified Hopfield network that can determine the vector of preference ratings associated with a positive reciprocal comparison matrix. The dynamics of this network are mathematically equivalent to the power method, a widely used numerical method for computing the principal eigenvectors of square matrices. However, this Hopfield network representation is incapable of generalizing the preference patterns, and consequently is not suitable for approximating the preference ratings if the pairwise comparison judgments are imprecise. Second, we present a feed-forward neural network formulation that does have the ability to accurately approximate the preference ratings. We use a simulation experiment to verify the robustness of the feed-forward neural network formulation with respect to imprecise pairwise judgments. From the results of this experiment, we conclude that the feed-forward neural network formulation appears to be a powerful tool for analyzing discrete alternative multicriteria decision problems with imprecise or fuzzy ratio-scale preference judgments.

A comparison of a robust mixed-integer approach to existing methods for establishing classification rules for the discriminant problem

Abstract A nonparametric mixed-integer programming formulation to solve the classification problem in linear discriminant analysis is proposed. The classification performance of this formulation is compared to the MSD linear programming approach and two commonly used statistical methods, Fishers linear discriminant function and the quadratic discriminant function. Using real data with highly nonnormal distributions the mixed-integer formulation is shown to outperform any of the other three approaches. To study the performance of the mixed-integer formulation systematically, a Monte Carlo simulation experiment is conducted, sampling from several different distributions. The results show that the mixed-integer formulation outperforms the other three approaches on the training samples, except when the variance-covariances are heterogeneous, in which case the quadratic function classifies better. When holdout samples are used to evaluate the relative performance, the mixed-integer approach classifies well when the data are highly discrete and the variance-covariances are homogeneous, but does not fare as well under other data conditions. Therefore, this study suggests that under certain conditions the mixed-integer approach is an attractive alternative to establish classification methods.

On multiplicative priority rating methods for the AHP

Abstract Recently, several alternative variants to the original Analytic Hierarchy Process (AHP) have been proposed. Most of these sought to resolve some of the theoretical problems associated with the original AHP, which uses an additive preference aggregation. In this paper, we take a close look at the multiplicative ratings method, which has recently received growing attention. The interest in the multiplicative AHP (MAHP) is motivated by the fact that, in contrast with the original AHP, it precludes certain types of rank reversals as the composite priority ratings continue to follow a ratio scale, even after normalization. The purpose of this paper is threefold. First, we derive and discuss several interesting properties of the MAHP that have eluded attention in previous studies. Second, we argue that these properties of the MAHP are interesting not only for mathematical reasons but also on behavioral grounds. We show how the MAHP offers a more flexible preference modeling framework, while still preserving the ratio scale property, by relaxing the “constant returns to scale” assumption made in previous research. Third, we use simulation experiments to explore the extent to which the theoretical differences between the original AHP (additive AHP) play out computationally for various different types of preference structures, enabling us to assess whether the MAHP is merely an interesting theoretical construct, or can in fact make a substantial difference in terms of the rankings and ratings of the alternatives and rank reversals between the alternatives.

Interactive multiple objective programming using Tchebycheff programs and artificial neural networks

A new interactive multiple objective programming procedure is developed that combines the strengths of the interactive weighted Tchebycheff procedure (Steuer and Choo. Mathematical Programming 1983;26(1):326–44.) and the interactive FFANN procedure (Sun, Stam and Steuer. Management Science 1996;42(6):835–49.). In this new procedure, nondominated solutions are generated by solving augmented weighted Tchebycheff programs (Steuer. Multiple criteria optimization: theory, computation and application. New York: Wiley, 1986.). The decision maker indicates preference information by assigning “values” to or by making pairwise comparisons among these solutions. The revealed preference information is then used to train a feed-forward artificial neural network. The trained feed-forward artificial neural network is used to screen new solutions for presentation to the decision maker on the next iteration. The computational experiments, comparing the current procedure with the interactive weighted Tchebycheff procedure and the interactive FFANN procedure, produced encouraging results.

Nontraditional approaches to statistical classification: Some perspectives on L_p-norm methods

The body of literature on classification methods which estimate boundaries between the groups (classes) by optimizing a function of the Lp-norm distances of observations in each group from these boundaries, is maturing fast. The number of published research articles on this topic, especially on mathematical programming (MP) formulations and techniques for Lp-norm classification, is now sizable. This paper highlights historical developments that have defined the field, and looks ahead at challenges that may shape new research directions in the next decade. In the first part, the paper summarizes basic concepts and ideas, and briefly reviews past research. Throughout, an attempt is made to integrate a number of the most important Lp-norm methods proposed to date within a unified framework, emphasizing their conceptual differences and similarities, rather than focusing on mathematical detail. In the second part, the paper discusses several potential directions for future research in this area. The long-term prospects of Lp-norm classification (and discriminant) research may well hinge upon whether or not the channels of communication between on the one hand researchers active in Lp-norm classification, who tend to have their roots primarily in the decision sciences, the management sciences, computer science and engineering, and on the other hand practitioners and researchers in the statistical classification community, will be improved. This paper offers potential reasons for the lack of communication between these groups, and suggests ways in which Lp-norm research may be strengthened from a statistical viewpoint. The results obtained in Lp-norm classification studies are clearly relevant and of importance to all researchers and practitioners active in classification and discriminant analysis. The paper also briefly discusses artificial neural networks, a promising non-traditional method for classification which has recently emerged, and suggests that it may be useful to explore hybrid classification methods that take advantage of the complementary strengths of different methods, e.g., neural network and Lp-norm methods.

Mathematical Programming Approaches for the Classification Problem in Two-Group Discriminant Analysis.

The authors introduce mathematical programming formulations as new approaches to solve the classification problem in discriminant analysis. These formulations have recently emerged as powerful alternatives to the existing methods of maximizing correct classification of entities into groups. The research literature on mathematical programming formulations is reviewed and summarized. An illustration using a real-world classification problem is provided. issues relevant to potential users of these formulations as well as fruitful future research avenues are discussed.

Multigroup Discriminant Analysis Using Linear Programming

In this paper we introduce a nonparametric linear programming formulation for the general multigroup classification problem. Previous research using linear programming formulations has either been limited to the two-group case, or required complicated constraints and many zero-one variables. We develop general properties of our multigroup formulation and illustrate its use with several small example problems and previously published real data sets. A comparative analysis on the real data sets shows that our formulation may offer an interesting robust alternative to parametric statistical formulations for the multigroup discriminant problem.

Evaluating an Anti-Drinking and Driving Advertising Campaign With a Sample Survey and Time Series Intervention Analysis

The impact of a paid advertising campaign targeted at reducing youthful male drinking-driving behavior is examined using (1) pretest and posttest sample surveys taken at both a campaign site and a control site and (2) time series intervention modeling of monthly traffic accident data from both sites. These compatible analyses provide collaborative evidence that the advertising campaign reduced youthful male drinking and driving behavior and, consequently, traffic accidents.