Overloaded Queuing Systems: Service Capacity Allocation and Incentive Issues

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Service capacity in units such as call centers or health clinics, typically cannot meet the demand. As a result, some customers end up abandoning queues intentionally or passively without being served. A queuing system with a demand that is larger than the service capacity is said to be overloaded. By acknowledging the fact that only a proportion of customers acquire service, the major decision facing an overworked service provider is to develop a system that assigns priority. Since the attributes of customers and servers, and their interactions may not be homogenous, the First-In-First-Out (FIFO) discipline may not suit every situation. For example, a gravely ill person will take precedence over someone else in a hospital waiting room. In such cases, service providers may compromise the FIFO rule in the interests of efficiency metrics, such as health and revenue. This proposal aims to develop methods for rationing the scarce service resources in an overloaded system to achieve the desired efficiency-fairness tradeoff for meeting the needs of the service provider. In particular, I aim to characterize the relationship between the score formula that has been used to rank customers in the queue, and the outcome in relation to specific efficiency and fairness metrics. Moreover, the proposed research investigates how to allocate services compatible with customers' incentives, given that a customer may prefer certain types of services. My anticipated results can be applied to a broad class of overloaded service systems. One promising application is the design of rules for allocating organs to patients on transplant waitlists. Since 2008, the Canadian federal and provincial governments have been actively soliciting recommendations related to implementing an integrated organ and tissue donation and transplantation (OTDT) system. One particularly sensitive issue is the development of a nationally coordinated policy for allocating cadaver organs; one that balances two conflicting objectives, including optimizing transplant outcomes and providing equitable access to patients on the waitlist. My research aims to address this issue.