This historical account of operations research at Bell Laboratories was drafted in the late 1970s when the authors were part of the Operations Research Center within Bell Laboratories at AT&T; it has not previously appeared in the open literature. We have added a few references to later publications that describe particular aspects of the period covered. Discussions of technological practices and organizational arrangements expressed in the present tense represent a viewpoint of about 1980, before “divestiture” split up the Bell System. The principal topics in this issue of Operations Research are the organizational history, and teletraffic theory and engineering. Upcoming issues of Operations Research will include parts II and III of this account, comprising capacity expansion in telephone networks, operations analysis, applied statistics, and related OR activities. A comprehensive table of contents for parts I, II, and III can be found in the Online Collection of the Operations Research Home Page, at www.informs.org/pubs .

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Operations research models, although common in industry, government, and education, have not been widely used in manufacturing. That is changing, however, as operations research is brought to bear on today's extremely complex problems. Manufacturing managers can no longer depend solely on experience, but need the help of quantitative decision support tools. This article illustrates the importance of operations research in manufacturing. It provides examples of the use of operations research in factories and in manufacturing planning and support. It discusses future trends of operations research in manufacturing.

An important network optimization problem i s t o determine the routing of circuits and construction of additional arc capacity i n a comnications network so as t o satisfy forecasted circuit requirements a t minimum cost. single-time-period version of the problem formulated as a linear program i n the arc-chain form. Zinear program i s expZoited t o develop an e f f i c i e n t solution procedure. I n particular, the generalized upper bounding technique devised by Dantzig and Van Slyke i s applied. implementation of the procedure i s discussed and computational experience i s reported. Brief mention i s also made of certain extensions that are being pursued. This paper considers theThe special structure of t h i s The computer 1. 3NI"TODUCTION Consider a communications network in which the nodes might be interpreted as switching points and the arcs as transmission links. Given are forecasts of circuit requirements between specified pairs of nodes. These requirements are called pointto-point demands or simply demands and must be met by routing circuits along certain acceptable circuit paths or designs in the network. If an arc has insufficient capacity to provide for these demands, then its capacity can be augmented by the construction of new facilities.The problem then is to determine the routing of circuits on designs and the construction of new capacity so as to meet the demands at minimum cost. Cost will be defined as the sum of routing costs and construction costs. Though the treatment of a planning horizon consisting of several time periods i s the ultimate objective of t h i s work, i n i t i a l e f f o r t s have been confined t o the single-time-period problem. One p a r t i c u l a r approach t o t h i s communications network planning problem i s t o formulate it a s a l i n e a r program.* E a r l i e r e f f o r t s t o solve t h i s l i n e a r program have proceeded along two lines. Heuristic approaches have led t o e f f i c i e n t techniques but these cannot guarantee the optimality of the f i n a l solution. General LP codes produce optimal solutions but unfortunately a r e unable t o take advantage of our special "network" s t r u c t u r e and so cannot promise t h e efficiency required, f o r example, i n dealing w i t h large-scale networks.The object of t h i s work i s t o combine efficiency with a guarantee of optimality by exploiting the special structure of the l i n e a r program. I n p a r t i c u l a r , the generalized upper bounding technique of Dantzig and V a n Slyke 111 i s applied. As w i l l be seen, t h i s technique i s especially appropriate f o r our network planning problem. Actually, the procedure t h a t has been developed contains a number of other features, most notably a compact storage scheme f o r the data, a product form representation of the (working) b a s i s inverse, a special pricing strategy f o r the nonbasic variables, and a reinversion scheme which i s called periodically t o reduce the storage requirement f o r the representation of the workin...

This t e x t on l i n e a r and i n t e g e r programming i s a welcome addition t o t h e l i t e r a t u r e . The objectives of t h e t e x t , in-