Operations & Supply Chain Management

This paper concerns the modeling of low inventory lines. Currently, most models assume that processing times are independent. We consider the differences in behavior of workers in low- and high-inventory production lines. Using a laboratory experiment we show that workers speed up whent hey are the cause of idle time on the line. This means that processing time distributions are not independent of the size of the buffer, of the processing speed of co-workers, or of the amount of inventory in the system.

We study the Mt/G/∞ queue where customers arrive according to a sinusoidal function λt = λ + A sin(2 π t/T) and the service rate is μ. We show that the expected number of customers in the system during peak congestion can be closely approximated by (λ + A)/ μ for service distributions with coefficient of variation between 0 and 1.

Recent research on business investment decisions suggests that real investment in plant and equipment is quite sensitive to changes in the user cost of capital, pointing to the possibility that long-run changes in tax policy may have a significant impact on an economy's capital stock. Indeed, many countries have at times adopted investment tax incentives to stimulate investment. The prevalence of investment incentives suggests that local policy-makers believe these are effective in increasing investment at a reasonable cost in terms of lost revenue.

We show how a simple normal approximation to Erlang's delay formula can be used to analyze capacity and staffing problems in service systems that can be modeled as M/M/s queues.

The increased complexity of modern manufacturing has led to uncertainties in production processes. Factors such as unplanned machine maintenance, tool unavailability, and complex process adjustments make it difficult to maintain a predictable level of output. To be effective, an appropriate production model must incorporate these uncertainties into the representation of the production process. This paper considers a one-time production of an application-specific product which must follow a fixed routing through the manufacturing system.

Why has the service factory model failed to live up to its original promise? To answer this question, we start with a basic concept: service is doing the work of your customer. As a result, it requires a high level of contact, communication and coordination with your customers. To deliver truly excellent service, therefore, requires a level of customer intimacy. That is, a service provider needs to know individual customers being served in order to deliver service that, in addition to being efficient, is also personal and effective in fulfilling their total service requirements.

We examine some mathematical aspects of learning unknown mappings with the mixture of experts model (MEM). Specifically, we observe that the MEM is at least as powerful as a class of neural networks, in a sense that will be made precise. Upper bounds on the approximation error are established for a wide class of target functions. The general theorem states that ||f-fn||p⩽c/nr d/ for f∈Wpr(L) (a Sobolev class over [-1,1]d), and fn belongs to an n-dimensional manifold of normalized ridge functions.

To improve the efficiency of product distribution for a centralized bakery, I first performed each person's tasks and discovered that constructing optimal minimum-distance routes would not significantly reduce costs but replacing the physical validation of new routes with a manual mathematical computation or simulation would. The trick was getting management to trust the simulation enough to use it.

A major problem in forecasting is estimating the time of some future event. Traditionally, forecasts are designed to minimize an error cost function that is evaluated once, possibly when the event occurs and forecast accuracy can be determined. However, in many applications forecast error costs accumulate over time, and the forecasts themselves may be updated with information that is collected as the expected time of the event approaches. This paper examines one such application, in which flow control managers in the U.S.