Zohar Feldman

Staffing of Time-Varying Queues to Achieve Time-Stable Performance

This paper develops methods to determine appropriate staffing levels in call centers and other many-server queueing systems with time-varying arrival rates. The goal is to achieve targeted time-stable performance, even in the presence of significant time variation in the arrival rates. The main contribution is a flexible simulation-based iterative-staffing algorithm (ISA) for the Mt/G/st + G model---with nonhomogeneous Poisson arrival process (the Mt) and customer abandonment (the + G). For Markovian Mt/M/st + M special cases, the ISA is shown to converge. For that Mt/M/st + M model, simulation experiments show that the ISA yields time-stable delay probabilities across a wide range of target delay probabilities. With ISA, other performance measures---such as agent utilizations, abandonment probabilities, and average waiting times---are stable as well. The ISA staffing and performance agree closely with the modified-offered-load approximation, which was previously shown to be an effective staffing algorithm without customer abandonment. Although the ISA algorithm so far has only been extensively tested for Mt/M/st + M models, it can be applied much more generally---to Mt/G/st + G models and beyond.

Proactive event processing in action: a case study on the proactive management of transport processes (industry article)

Proactive event processing constitutes the next phase in the evolution of complex event processing. Proactive event processing makes it possible to anticipate potential issues during process execution and thereby enables proactive process management. One industry domain that can expect relevant benefits from applying proactive event processing is transportation. Transportation companies face numerous stochastic issues when managing the shipment of goods. One such issue faced in airfreight is the exact volume, weight, and number of pieces that a shipper wants to have shipped. Because of the high cost of air shipments, discrepancies between what has been booked by a shipper and the actual volume that is delivered impose costs that create problems for all participants in a shipment. One potential approach to addressing this problem is to use real-time monitoring and proactive alerting to assist air freight companies in anticipating actual delivered weights, volumes, and piece counts. In this paper we address the issue of cargo shipments by leveraging real-time monitoring data collected from an industry-standard monitoring system of a large freight forwarding company. Our evidence indicates that by using a novel proactive event-driven software engine, prediction about the weight of shipments can be developed and used in a proactive manner to assist air freight planners in making better estimates and plans for the shipment of goods. We demonstrate that through the use of this proactive approach, predictions concerning over and under-weight loads can be made days in advance of a shipment, thus enabling the air freight planner to optimize their load plans and thus maximize the revenue that they generate from shipments.

Creating operational shift schedules for third-level IT support: challenges, models and case study

IT support can be divided into first-level support, second-level support and third-level support. Although there is a large body of existing work regarding demand forecasting and shift schedule creation for various domains such as call centres, very little work exists for second- and third-level IT support. Moreover, there is a significant difference between such support and other types of services. As a result, current best practices for scheduling such work are not based on demand, but rather on primitive rules of thumb. Due to the increasing number of people providing such support, theory and practice is sorely needed for scheduling second- and third-level support shifts according to actual demand. In this work, we present an end-to-end methodology for forecasting and scheduling this type of work. We also present a case study in which this methodology demonstrated significant potential savings in terms of manpower resources.

Monte-Carlo tree search: to MC or to DP?

State-of-the-art Monte-Carlo tree search algorithms can be parametrized with any of the two information updating procedures: MC-backup and DP-backup. The dynamics of these two procedures is very different, and so far, their relative pros and cons have been poorly understood. Formally analyzing the dependency of MC-and DP-backups on various MDP parameters, we reveal numerous important issues that get hidden by the worst-case bounds on the algorithm performance, and reconfirm these findings by a systematic experimental test.