Mustafa (Hayri) Tongarlak

• Ata, B.; Lee, D.; Tongarlak, M. T., (Forthcoming), "A diffusion model of dynamic participant inflow management", Queueing Systems
  Abstract: This paper studies a diffusion control problem motivated by challenges faced by public health agencies who run clinics to serve the public. A key challenge for these agencies is to motivate individuals to participate in the services provided. They must manage the flow of (voluntary) participants so that the clinic capacity is highly utilized, but not overwhelmed. The organization can deploy costly promotion activities to increase the inflow of participants. Ideally, the system manager would like to have enough participants waiting in a queue to serve as many individuals as possible and efficiently use clinic capacity. However, if too many participants sign up, resulting in a long wait, participants may become irritated and hesitate to participate again in future. We develop a diffusion model of managing participant inflow mechanisms. Each mechanism corresponds to choosing a particular drift rate parameter for the diffusion model. The system manager seeks to balance three different costs optimally: (i) a linear holding cost that captures the congestion concerns, (ii) an idleness penalty corresponding to wasted clinic capacity and negative impact on public health, and (iii) costs of promotion activities. We show that a nested-threshold policy for deployment of participant inflow mechanisms is optimal under the long-run average cost criterion. In this policy, the system manager progressively deploys mechanisms in increasing order of cost, as the number of participants in the queue decreases. We derive explicit formulas for the queue length thresholds that trigger each promotion activity, providing the system manager with guidance on when to use each mechanism.

  Link(s) to publication:

  http://dx.doi.org/10.1007/s11134-024-09909-y

• Ata, B.; Tongarlak, M. T.; Lee, D.; Field, J., 2024, "A Dynamic Model for Managing Volunteer Engagement", Operations Research
  Abstract: Non-profit organizations that provide food, shelter, and other services to people in need, rely on volunteers to deliver their services. Unlike paid labor, non-profit organizations have less control over unpaid volunteers’ schedules, efforts, and reliability. However, these organizations can invest in volunteer engagement activities to ensure a steady and adequate supply of volunteer labor. We study a key operational question of how a non-profit organization can manage its volunteer workforce capacity to ensure consistent provision of services. In particular, we formulate a multiclass queueing network model to characterize the optimal engagement activities for the non-profit organization to minimize the costs of enhancing volunteer engagement, while maximizing productive work done by volunteers. Because this problem appears intractable, we formulate an approximating Brownian control problem in the heavy traffic limit and study the dynamic control of that system. Our solution is a nested threshold policy with explicit congestion thresholds that indicate when the non-profit should optimally pursue various types of volunteer engagement activities. A numerical example calibrated using data from a large food bank shows that our dynamic policy for deploying engagement activities can significantly reduce the food bank's total annual cost of its volunteer operations while still maintaining almost the same level of social impact. This improvement in performance does not require any additional resources -- it only requires that the food bank strategically deploy its engagement activities based on the number of volunteers signed up to work volunteer shifts.

  Link(s) to publication:

  http://dx.doi.org/10.1287/opre.2021.0419

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