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The article presents a paper, which shows how to determine the optimal number and routings of containers used to move mail among several cities. This paper extends the above system to more than two cities, with different schedules on different days and with transfer of containers permitted throughout the system. A mathematical formulation is developed that can be readily solved by known linear programming techniques. Critical assumptions are (a) periodic demand patterns and linear transportation costs between each city-pair, and (b) constant fixed cost per container in the system. The optimization problem is transformed into a network flow linear program for which efficient solution methods are known. The researchers have investigated a system where containerized mail would be sent between two cities. The problem solved in reference was that of determining the best number of containers to be sent each day between the two cities, based on (a) the distribution of mail volumes going between the cities each day and (b) the costs for sending containers, full or empty, and for sending mail by other means in case of container shortage. Thus the original convex programming problem has been converted to an especially tractable type of linear program, namely the optimization of a network flow.

The designing of some multi-factor "analytical" studies of survey data is considered in this paper. It is assumed that, for each factor, there are two categories of interest and these are to be compared. The main objective is to allocate the sample so that the desired precision for the specified contrasts is obtained at minimum cost. It is assumed that one may sample independently in each of the sub-populations under investigation. A model is employed to facilitate designing and analyzing such a survey. We discuss the relevance of this model for "analytical studies," and a few examples are presented. Several survey objectives are explored, and an optimal allocation is obtained for each. Two, three and four-factor studies are considered explicitly. [ABSTRACT FROM AUTHOR]