Knowledge that Transforms

The gap between brilliant management science solutions and the translation of these solutions into practical and profitable management action has, of course, long been recognized. Certainly TIMS has been concerned about this problem since the organization was founded. Despite the persistence of the problem and its increasing importance, there has been little hard-fact research on either causes or cures. For the most part, the discussions in the professional literature has been conceptual, and usually begin by complaining about how little research has been done on the problem of implementation.

A transportation queueing process in which taxis arrive in a Poisson process and customers arrive as a renewal process independent of taxi-arrival process is controlled by calling extra taxis whenever the total number of customers lost to the system reaches a certain predetermined number. Transient and steady state behavior of this process is studied using renewal theoretic arguments. The optimum value of the control variable is also obtained so as to minimize the total cost to the system due to the waiting taxis and lost customers.

By decision-making in a fuzzy environment is meant a decision process in which the goals and/or the constraints, but not necessarily the system under control, are fuzzy in nature. This means that the goals and/or the constraints constitute classes of alternatives whose boundaries are not sharply defined. An example of a fuzzy constraint is: “The cost of A should not be substantially higher than α,” where α is a specified constant. Similarly, an example of a fuzzy goal is: “x should be in the vicinity of x 0 ,” where x 0 is a constant. The italicized words are the sources of fuzziness in these examples. Fuzzy goals and fuzzy constraints can be defined precisely as fuzzy sets in the space of alternatives. A fuzzy decision, then, may be viewed as an intersection of the given goals and constraints. A maximizing decision is defined as a point in the space of alternatives at which the membership function of a fuzzy decision attains its maximum value. The use of these concepts is illustrated by examples involving multistage decision processes in which the system under control is either deterministic or stochastic. By using dynamic programming, the determination of a maximizing decision is reduced to the solution of a system of functional equations. A reverse-flow technique is described for the solution of a functional equation arising in connection with a decision process in which the termination time is defined implicitly by the condition that the process stops when the system under control enters a specified set of states in its state space.

The purpose of this note is to suggest some improvements in current statistical analysis of computer-simulated data. To be concrete, we will take a recent paper by Roser T. Nelson [Nelson, Rosser T. 1967. Labor and machine limited production systems. Management Sci. 13(9, May) 648–671.] which appears in this journal as the vehicle for our comments.

The Linear Decision Rules (LDR) proposed by Holt, Modigliani, Muth, and Simon for the production planning problem determine an optimum plan in terms of an aggregate production rate and work force level. The criteria of the LDR assume we wish to make decisions so as to minimize costs over a specified time horizon, given estimates of future aggregate demand. This paper extends the LDR to a multi-item formulation (MDR) which solves directly for the optimum sales, production, and inventory levels for individual items in future periods. To remove the restriction of specified demand, revenue curves are estimated for each item in each time period. The MDR model then seeks a solution to maximize profit for the firm over the time horizon by an application in a firm producing a line of electric motors. The results of the MDR are compared to management's proposed plan and some important differences are detected.

Errata to Garfinkel, R. S., G. L. Nemhauser. Optimal political districting by implicit enumeration techniques. Management Sci. 16(8, April), page B-507.