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We describe the industrial project that a “proliferator” would conduct to produce a first, small batch of nuclear weapons. From refining yellowcake ore to final weapons assembly, we highlight the project's tasks and their interactions. The proliferator can choose alternative production technologies that offer quicker completion, but at higher cost in terms of limited resources. The proliferator can also expedite his project by devoting more resources to critical tasks. From physics and chemistry, we determine raw material requirements. From industrial engineering and materials science, we convert these requirements into estimates of the time, manpower, energy, and money required to complete each task under normal and expedited conditions. Using generalized project-management analysis tools, we then estimate the earliest possible completion time of the project, assuming two different levels of resource availability. We also estimate the time required to complete a weapon if some of the project's steps can ...

Maritime Policy & Management, 25, pp. 335-348. A multinational company uses a personal computer to schedule a fleet of coastal tankers and barges transporting liquid bulk products among plants, distribution centres (tank farms), and industrial customers. A simple spreadsheet interface cloaks a sophisticated optimization-based decision support system and makes this system useable via a variety of natural languages. The dispatchers, whose native language is not English, and some of whom presumably speak no English at all, communicate via the spreadsheet, and view recommended schedules displayed in Gantt charts-both internationally familiar tools. Inside the spreadsheet, a highly detailed simulation can generate every feasible alternate vessel employment schedule, and an integer linear set partitioning model selects one schedule for each vessel so that all loads and deliveries are completed at minimal cost while satisfying all operational requirements. The optimized fleet employment schedule is displayed graphically with hourly time resolution over a planning horizon of 2-3 weeks. Each vessel will customarily make several voyages and many port calls to load and unload products during this time.

AIIE Transactions, 6, pp. 135-142. When a MIL-STD-IOSD sampling scheme is used for a long period, some lots will be subjected to normal, some to reduced, and some to tightened inspection. This paper provides for several single sampling plans and various quality levels,'the expected fraction of lots rejected,the expected sample size per lot, and the expected number of lots to be processed before sampling inspection must be discontinued. Eq.uations are given to calculate the long term cost of sampling inspection using these expected values and appropriate cost parameters.

This paper compares two point estimators of fraction defective of a normal distribution when both population parameters are unknown; the minimum variance unbiased estimator, (x), and the maximum likelihood estimator, (x). Using minimum mean squared error as a criterion, it is shown that the choice of estimator depends upon the true value of F(x), and the sample size. In the domain .0005 ≤ F(x) ≤ .50, the maximum likelihood estimator is generally superior even for small sample sizes, except for F(x) less than about 0.01, or greater than 0.25. Furthermore, the bias in the m.l.e. is slight over much of the domain where this estimator has smaller mean squared error. As a practical solution to the estimation problem. it is suggested that the m.v.u.e. be calculated, and if this estimate is between 0.01 and 0.25, it should be replaced with the m.l.e. This combined estimator is shown to be nearly as efficient as the better of the m.v.u.e. and m.l.e. throughout the domain of F(x).