Many organizations, such as banks, airlines, telecommunications companies, and police departments, routinely use queueing models to help determine capacity levels needed to respond to experienced demands in a timely fashion. Though queueing analysis has been used in hospitals and other healthcare settings, its use in this sector is not widespread.
This paper analyzes a call center model with m customer classes and r agent pools. The model is one with doubly stochastic arrivals, which means that the m-vector λ of instantaneous arrival rates is allowed to vary both temporally and stochastically. Two levels of call center management are considered: staffing the r pools of agents, and dynamically routing calls to agents. The system manager's objective is to minimize the sum of personnel costs and abandonment penalties.
Objectives: Significant variation in emergency department (ED) patient arrival rates necessitates the adjustment of staffing patterns to optimize the timely care of patients. This study evaluated the effectiveness of a queueing model in identifying provider staffing patterns to reduce the fraction of patients who leave without being seen.
We study how multi-product queueing systems should be controlled so that sojourn times (or end-to-end delays) do not exceed specified leadtimes. The network dynamically decides when to admit new arrivals and how to sequence the jobs in the system. To analyze this difficult problem, we propose an approach based on fluid-model analysis that translates the leadtime specifications into deterministic constraints on the queue length vector.
This paper examines inventory management from an incentive perspective. We show that when a manager has private information about future attainable revenues, the residual income performance measure based on historical cost can achieve optimal (second-best) incentives with regard to managerial effort as well as production and sales decisions. The LIFO (last-in—first-out) inventory flow rule is shown to be preferable to the FIFO (first-in—first-out) rule for the purpose of aligning incentives.
We consider a model of a service system that delivers two nonsubstitutable services to a market of heterogenous users. The first service is delivered subject to a "guaranteed" (G) processing rate, and the second is a "best-effort" (BE) type service in which residual capacity not allocated to the guaranteed class is shared among BE users. Users, in turn, are sensitive to both price and congestion-related effects. The service provider's objective is to optimally design the system so as to extract maximum revenues.
In a year without vaccine shortages, no fewer than 36,000 deaths - twelve times the number of September 11 victims - and 200,000 hospitalizations are attributed to influenza and its complications. In terms of productivity, between $11 and $20 billion is lost. The sudden elimination of one of only two manufacturers and half the national supply was hardly an unforeseeable or rare event, as numerous Senate testimonies and General Accounting Office reports have documented recurring supply problems with this and other critical vaccines.
A patent only protects an innovator from others producing the same product, but it does not protect him from others producing better products under new patents. Therefore, one may divide up the source of competition facing an innovator into within-patent competition, which results from production of the same product, and betweenpatent competition, which results from production of products on other patents.
Motivated by applications in telephone call centers, we consider a service system model with m customer classes and r server pools. The model is one with doubly stochastic arrivals, which means that the m-vector λ of instantaneous arrival rates is allowed to vary both temporally and stochastically.