Reducing congestion is a primary concern in the design and analysis of queueing networks, especially in systems where sources of randomness are characterized by high variability. This paper considers a multiserver first-come, first-served (FCFS) queueing model where we arrange servers in two stations in series. All arrivals join the first service center, where they receive a maximum of T units of service. Arrivals with service requirements that exceed the threshold T join the second queue, where they receive their remaining service. For a variety of heavy tail service time distributions, characterized by large coefficient of variations, analytical and numerical comparisons show that our scheme provides better system performance than the standard parallel multiserver model in the sense of reducing the mean delay per customer in heavy traffic systems. Our model is likely to be useful in systems where high variability is a cause for degradation and where numerous service interruptions are not desired.

How Product Locations Drive Traffic Throughout a Retail Store In “Store-Wide Shelf-Space Allocation with Ripple Effects Driving Traffic,” Flamand, Ghoniem, and Maddah develop a framework for deciding where to place products in a store, in addition to apportioning the shelf space among products, in a way that maximizes impulse profit, a phenomenon that may account for 50% of transactions. By analyzing a large data set of customer receipts from a grocery store in Beirut, the authors develop a regression model that estimates traffic at a shelf based on its location and the “attraction” from products allocated nearby. The traffic model is embedded within a mixed-integer nonlinear program, which they solve via specialized linear approximations. For the store in Beirut, a 65% improvement in impulse profit is anticipated, and the location of products is found to be significantly more important in driving store-wide traffic than the relative shelf-space allocation.