Sheila M. Lawrence

A multiple criteria approach to aggregate industrial capacity expansion

Abstract This paper is concerned with a multiproduct, multiregion, multiperiod capacity expansion problem involving both the expansion of existing facilities and the development of new facilities. An input- output model structure is used to describe the flows of products through regions over time. Multiple objectives are incorporated into the analysis and an interactive multiple objective solution procedure is employed. The model and the solution procedure are illustrated with an example.

A Multiple objective shift allocation model

Abstract This paper is concerned with a production planning problem of allocating production items to production shifts on production lines in a multiple objective decision environment. The problem is modeled and solutions are generated using integer goal programming techniques. Objectives are formulated not only in terms of minimizing the sum of deviations from goal target levels, but also in terms of minimizing the maximum deviation. Test results indicate that the model structure and solution process utilized can provide decision makers with a good set of representative solutions in a rather complex multiple objective decision environment.

Experimental comparison of parametric, non-parametric, and hybrid multigroup classification

Highlights? The proposed hybrid method dominates almost all the other methods on classification performance. ? Logistic regression and neural network provides worst relative performance under most scenarios. ? This shows that the data complexities have adverse impact on the multinomial logistic regression. ? The results indicate that all classification methods are adversely affected by the nonstatic data. ? This study demonstrates the effectiveness of the hybrid method in improving classification accuracy. This study evaluates the relative performance of some well-known classification techniques, as well as a proposed hybrid method. The proposed hybrid method is a combination of k-nearest neighbor (kNN) and linear programming (LP) method for four group classification. Computational experiments are conducted to evaluate the performances of these classification techniques. Monte Carlo simulation is used to generate dataset with varying characteristics such as multicollinearity, nonlinearity, etc. for the experiments. The experimental results indicate that LP approaches, in general, and the proposed hybrid method, in particular, consistently have lower misclassification rates for most data characteristics. Furthermore, the hybrid method utilizes the strengths of both methods - k-NN and linear programming - resulting in considerable improvement in the classification accuracy. The results of this study can aid in the design of various hybrid techniques that combine the strengths of different methods to improve classification accuracy and reliability.

Combining earnings forecasts using multiple objective linear programming

Abstract In this study, exponential smoothing, univariate time series and (transfer function) bivariate time series models are combined to forecast annual corporate earnings for six major corporations. Consideration is given to combining forecasts generated by the same technique at different points in time as well as those generated by different techniques. Multiple objectives are incorporated into the forecasting process. Mathematical programming is utilized to generate combined forecasts that are efficient with respect to multiple objectives. Results indicate that combined forecasts outperform individual forecasts, that all three major categories of forecasting techniques are utilized in the construction of the efficient combined forecasts, that the techniques included in the combined forecasts and their relative weights can change over time and that the most recent forecasts do not always receive the most weight when combined with older forecasts.

Allocation of teaching personnel: A goal programming model

Abstract This paper is concerned with the development of a model structure for allocating the teaching resources of a high school in an effective and efficient manner. In allocating these resources the multiple and often conflicting goals of the school system administrators will be considered, as well as the various operating and financial constraints of the school system.

Enterprise Information System and Data Mining

The advent of information technology and the consequent proliferation of information systems have lead to generation of vast amounts of data, both within the organization and across its supply chain. Enterprise information systems (EIS) have added to organizational complexity, and at the same time, created opportunities for enhancing its competitive advantage by utilizing this data for business intelligence purposes. Various data mining tools have been used to gain a competitive edge through these large data bases. In this paper, the authors discuss EIS-aided business intelligence and data mining as applicable to organizational functions, such as supply chain management (SCM), marketing, and customer relationship management (CRM) in the context of EIS.

Forecasting new adoptions: A comparative evaluation of three techniques of parameter estimation

Forecasting sales for an innovation before the products introduction is a necessary but difficult task. Forecasting is a crucial analytic tool when assessing the business case for internal or external investments in new technologies. For early stage investments or internal business cases for new products, it is essential to have some understanding of the likely diffusion of the technology. Diffusion of innovation models are important tools for effectively assessing the merits of investing in technologies that are new or novel and do not have prima facie, predictable patterns of user uptake. Most new product forecasting models require the estimation of parameters for use in the models. In this chapter, we evaluate three techniques to determine the parameters of the Bass diffusion model by using an example of a new movie.

Improved performance evaluation of comparable units with data envelopment analysis (DEA)

Regression analysis is a commonly applied technique used to measure the relationship/predict/forecast of comparable units. A set of comparable units is some group of entities each performing somewhat the same set of activities. In this chapter, we will apply a modified version of our recently developed methodology to incorporate into the regression analysis a new variable that captures the unique weighting of each comparable unit. This new variable is the relative efficiency of each comparable unit that will be generated by a technique called data envelopment analysis (DEA). The results of applying this methodology with the DEA variable to a hospital labor data set will be presented.

A DEA efficiency analysis of operating units within electronic shopping stores

Abstract This chapter assesses the operating units within electronic shopping stores with regard to their productivity. The methodology used to measure the productivity is data envelopment analysis (DEA). Two different linear programming model formulations of the DEA model are used. In the first linear programming model, the weights are applied to the inputs, with the outputs remaining the same. In the second model, weights are applied to the inputs, but the outputs are different.

Segmenting Financial Services Market: An Empirical Study of Statistical and Non-parametric Methods

In this paper, we analyze segmentation of financial markets based on the general segmentation bases. In particular, we identify potentially attractive market segments for financial services using a customer dataset. We develop a multi-group discriminant model to classify the customers into three ordinal classes: prime customers, highly valued customers, and price shoppers based on their income, loan activity, and demographics (age). The multi-group classification of customer segments uses both classical statistical techniques and a mathematical programming formulation. For this study we use the characteristics of a real dataset to simulate multiple datasets of customer characteristics. The results of our experiments show that the mathematical programming model in many case consistently outperforms standard statistical approaches in attaining lower Apparent Error Rates (APER) for 100 replications in both high and low correlation cases.