Improving perishable product inventory management using goal programming

The multiple objectives of perishable product inventory management are examined in this paper. These objectives include: (1) satisfying demand by carrying sufficient inventories, (2) holding down inventory carrying costs, (3) keeping the amount of product spoilage (outdating) at an acceptable level, and (4) maintaining quality by using the product while it is still fresh, and (5) keeping the cost of rotation low. Some of the above objectives are in conflict. Thus, certain redistribution policies may help attain one or more of these objectives to a greater extent. Redistribution involves the transfer of the product from outlets where demand is low to outlets where demand is high. A goal programming model for solving redistribution problems is presented. An example is provided and sensitivity analysis is performed in order to determine when redistribution is advantageous. Applicability of the model is discussed.     

The entropies: Some roots of ambiguity

Entropy concepts have developed in four general contexts—thermodynamics, communication theory, statistical information theory and social and life sciences. These concepts have five distinct mathematical forms. Ambiguity and complexity in utilization of the concept has been heightened by this multi-faceted heritage. Effective utilization may be strengthened by a sharper articulation of entropy through combinations of literal, mathematical and graphical modes of expression. Geography and planning as user disciplines have important responsibilities in enhancing cross-disciplinary communication of this important adisciplinary concept. Implications of these arguments for the social sciences include the need for caution in drawing social analogies from the ambiguous entropy concepts of statistical mechanics; and an indication of the potential benefits of social science models based on the unambiguous entropic constructs of statistical information theory.     

Analytic derivation of sociometric dimensions

Dimensional analysis has been defined variously as the synthesis of dimensionless variables and as a way of spatially representing data. While in sociometrics, one may not be able to devise models resembling those of the physical sciences because of the complexity of many social situations, one may in some cases successfully apply the techniques of classic dimensional analysis and get empirically valid results. More often we derive with analytic methods from the social sciences different kinds of dimensional frameworks in which to represent data. This paper will compare these approaches to the study of relationships and give examples of where each might be appropriate. Finally, a classification of the analytical methods will be presented.     

Robustness to the unavailability of data in block designs

We present general results for finding or boundingt _maxB , the maximum number of arbitrary whole blocks of observations which can be removed from a block design, and still leave all of the elementary treatment contrasts estimable. The block sizes may be larger than the number of treatments. The results are applied to BBDs, reinforced BIBDs and BBDs, BTIBDs, and a series of variance balanced incomplete block designs with two block sizes. Also given for most of these designs, are results fort _max, the maximum number of arbitrary, scattered observations that can become unavailable, and still leave all of the elementary treatment contrasts estimable.The work was undertaken while Dr. Whittinghill was visiting Ohio State University, and supported by a grant from the Natural Sciences Division, Colby College, Waterville, Maine.     

Some properties of the length of the shortest half

This paper discusses some properties of the length of the shortest half proposed by Rousseeuw and Leroy (1988) as a robust scale estimator.     

Testing equality of variances in the analysis of repeated measurements

The problem of comparing the precisions of two instruments using repeated measurements can be cast as an extension of the Pitman-Morgan problem of testing equality of variances of a bivariate normal distribution. Hawkins (1981) decomposes the hypothesis of equal variances in this model into two subhypotheses for which simple tests exist. For the overall hypothesis he proposes to combine the tests of the subhypotheses using Fisher's method and empirically compares the component tests and their combination with the likelihood ratio test. In this paper an attempt is made to resolve some discrepancies and puzzling conclusions in Hawkins's study and to propose simple modifications.
The new tests are compared to the tests discussed by Hawkins and to each other both in terms of the finite sample power (estimated by Monte Carlo simulation) and theoretically in terms of asymptotic relative efficiencies.