About

Hayriye Ayhan is a Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. Her research interests focus on stochastic (Max,+) linear systems, performance analysis of queueing and production systems via series expansions, and Markov decision processes with applications to admission control, inventory, and production control. Dr. Ayhan has made significant contributions to these areas, combining theoretical and applied approaches to improve understanding and management of complex industrial and systems engineering problems. Dr. Ayhan received her Bachelor's degree in Industrial Engineering from Bosphorous University and earned her Ph.D. in Industrial Engineering from Texas A&M University in 1995. She was honored with the National Science Foundation CAREER Award in 2000, recognizing her promising research trajectory and contributions to the field. She is an active member of the Institute of Operations Research and Management Sciences (INFORMS) and serves as the secretary and treasurer of the INFORMS Applied Probability Society, reflecting her leadership and commitment to the professional community in operations research and applied probability.

Research topics

• Computer Science
• Operations management
• Economics
• Computer Security
• Business
• Medicine
• Engineering
• Microeconomics
• Family medicine
• Computer network
• Nursing
• Data science
• Operations research
• Marketing
• Management science

Selected publications

• Collaboration versus Specialization in Service Systems with Impatient Customers

  ArXiv.org · 2026-01-22

  articleOpen accessSenior author

  We study tandem queueing systems in which servers work more efficiently in teams than on their own and customers are impatient in that they may leave the system while waiting for service. Our goal is to determine the server assignment policy that maximizes the long-run average throughput. We show that when each server is equally skilled at all tasks, the optimal policy has all the servers working together at all times. We also provide a complete characterization of the optimal policy for Markovian systems with two stations and two servers when each server's efficiency may be task dependent. We show that the throughput is maximized under the policy which assigns one server to each station (based on their relative skill at that station) unless station 2 has no work (in which case both servers work at station 1) or the number of customers in the buffer reaches a threshold whose value we characterize (in which case both servers work at station 2). We study how the optimal policy varies with the level of server synergy (including no synergy) and also compare the optimal policy for systems with different customer abandonment rates (including no abandonments). Finally, we investigate the case where the synergy among collaborating servers can be task-dependent and provide numerical results.

  PublisherOA PDF

• Multiserver-job response time under multilevel scaling

  Probability in the Engineering and Informational Sciences · 2026-03-27

  preprintOpen accessSenior author

  We study the multiserver-job setting in the load-focused multilevel scaling limit, where system load approaches capacity much faster than the growth of the number of servers $n$ . We consider the “1 and $n$ ” system, where each job requires either one server or all $n$ . Within the multilevel scaling limit, we examine three regimes: load dominated by $n$ -server jobs, 1-server jobs, or balanced. In each regime, we characterize the asymptotic growth rate of the boundary of the stability region and the scaled mean queue length. We demonstrate that mean queue length peaks near balanced load via theory, numerics, and simulation.

  PublisherOA PDFDOI

• Optimal resident physician supervision when patient care is non-preemptive

  Annals of Operations Research · 2026-05-21

  articleOpen accessSenior authorCorresponding

  Abstract This paper focuses on the assignment of attending physicians between the residents they supervise and their own responsibilities. Unlike prior work that assumes patient care can be interrupted at any time, we consider the more realistic and technically challenging situation when patient care is non-preemptive. Under the assumption that a holding cost is incurred when residents and patients wait for a conference with the attending physician and that rewards are earned whenever the attending physician completes a task (on his own or with his residents), we completely characterize the allocation of the attending physician that maximizes the long-run average profit. Furthermore, we show that the optimality conditions are simple thresholds on the holding cost. We also discuss how the optimal allocation of the attending physician differs from those in systems with preemptions. In particular, we show that the main difference occurs when the holding cost is high and there is no resident waiting for a conference. In this case, the attending physician waits for a resident to be ready for consultation in the non-preemptive case, whereas he works on his own responsibilities in the preemptive model. We conclude with a study of various extensions of our attending physician and residents interaction model and show that the structure of the optimal policy remains the same in these more general settings.

  PublisherOA PDFDOI

• Collaboration versus Specialization in Service Systems with Impatient Customers

  arXiv (Cornell University) · 2026-01-22

  preprintOpen accessSenior author

  We study tandem queueing systems in which servers work more efficiently in teams than on their own and customers are impatient in that they may leave the system while waiting for service. Our goal is to determine the server assignment policy that maximizes the long-run average throughput. We show that when each server is equally skilled at all tasks, the optimal policy has all the servers working together at all times. We also provide a complete characterization of the optimal policy for Markovian systems with two stations and two servers when each server's efficiency may be task dependent. We show that the throughput is maximized under the policy which assigns one server to each station (based on their relative skill at that station) unless station 2 has no work (in which case both servers work at station 1) or the number of customers in the buffer reaches a threshold whose value we characterize (in which case both servers work at station 2). We study how the optimal policy varies with the level of server synergy (including no synergy) and also compare the optimal policy for systems with different customer abandonment rates (including no abandonments). Finally, we investigate the case where the synergy among collaborating servers can be task-dependent and provide numerical results.

  PublisherDOI

• Optimal pricing and information sharing strategies in a single-server queue

  Queueing Systems · 2025-12-08

  articleSenior author
  PublisherDOI

• Optimal Control of Queueing Systems with Error-Prone Servers

  Stochastic Systems · 2025-08-11 · 1 citations

  articleOpen accessSenior author

  Consider a Markovian tandem line with finite intermediate buffers and an equal number of stations and servers. Servers are flexible but noncollaborative, so that a job can be processed by at most one server at any time. When a job is being processed, it can be damaged and wasted depending on the proficiency of the server. We identify the dynamic server assignment policy that maximizes the long-run average throughput of the system with two stations and two servers. We find that the optimal policy is either a single or a double threshold policy on the number of jobs in the buffer, where the thresholds depend on the service rates and defect probabilities of the two servers at the two stations. For larger systems, we show that the optimal policy may involve server idling and that improving the service rate at any station is always beneficial. Finally, we propose heuristic server assignment policies motivated by experimentation for small systems with finite buffers and analysis of larger systems with infinite buffers. Numerical results suggest that our heuristics yield near-optimal performance. Funding: This research was supported by the National Science Foundation [Grants CMMI-1536990 and CMMI-2127778]. S. Andradóttir was also supported by the National Science Foundation [Grant CMMI-2348409].

  PublisherDOI

• Dual Frame Survey Estimation

  International Encyclopedia of Statistical Science · 2025-01-01

  book-chapter1st authorCorresponding
  PublisherDOI

• Nonprobability Sampling SurveyMethods

  International Encyclopedia of Statistical Science · 2025-01-01

  book-chapter1st authorCorresponding
  PublisherDOI

• Optimal server control with Two Customer Classes and Classification Errors

  European Journal of Operational Research · 2025-12-11

  articleOpen accessSenior authorCorresponding

  • Customer misclassification is a common phenomenon in many applications. • We provide analytical models of service systems with customer misclassification. • We identify the optimal allocation of specialists in systems with customer misclassification. • We investigate how the long-run average profit depends on the misclassification probability. • We identify under what conditions it is more profitable to serve customers with or without service continuity. We consider a Markovian queueing system with two types of customers (basic and advanced) and two types of servers (regular and specialist) in the presence of customer classification errors. We assume that there are always both types of customers waiting for service. When an advanced customer is misclassified as a basic customer, he needs to be served by a specialist after being served by a regular server. Our objective is to determine the dynamic assignment of the specialists between advanced and misclassified customers that maximizes the long-run average profit. We consider two versions of the problem that differ depending on whether the misclassified customers experience service continuity (the regular servers stay with misclassified customers while they wait for specialists, preventing the regular servers from serving other basic customers) or not (the regular servers continue serving other basic customers while misclassified customers wait for specialists). For both versions of the problem, we first characterize the optimal assignment of the specialists and then investigate how the optimal long-run average profit depends on the misclassification probability. We provide examples of systems where the optimal long-run average profit is not monotone in the misclassification probability, which is counter intuitive as one would expect misclassification to have a negative impact on system performance. We conclude our analysis by identifying under what conditions it is more profitable to serve customers with or without service continuity.

  PublisherDOI

• Service Rate Control in Queues with Abandonments

  ArXiv.org · 2025-11-11

  preprintOpen accessSenior author

  We consider a Markovian single server queue with impatient customers. There is a customer abandonment cost and a holding cost for customers in the system. We consider two versions of the problem. In the first version, customers pay a reward at the time of arrival whereas in the second version, reward is received at the time of service completion. Service rate attains values in a compact set and there is a cost associated with each service rate. Under these assumptions, our objective is to characterize the service rate policy that maximizes the infinite-horizon discounted reward and the long-run average reward. We show that for systems with an infinite buffer, the optimal service rate policy is monotone. However, the optimal policy is not necessarily monotone when capacity is finite. Furthermore, we prove that the set of possible optimal actions can be reduced to the lower boundary of the convex hull of the action space and develop an efficient policy iteration algorithm. Finally, we show that the optimal service rate converges as the state goes to infinity which allows us to truncate the state space to numerically compute the optimal service rate when system has infinite buffer space.

  PublisherOA PDFDOI

Recent grants

• Performance Analysis of Closed Queueing Networks with Subexponential Processing Times

  NSF · $120k · 2010–2014

Frequent coauthors

• Sigrún Andradóttir

  Georgia Institute of Technology

  27 shared

• Robert D. Foley

  12 shared

• Douglas G. Down

  McMaster University

  12 shared

• Martin Wortman

  11 shared

• Gerald Midgley

  6 shared

• Serhan Ziya

  University of North Carolina at Chapel Hill

  6 shared

• J. Limon-Robles

  Tecnológico de Monterrey

  4 shared

• Gilbert Laporte

  University of Bath

  4 shared

Awards & honors

• National Science Foundation CAREER Award (2000)
• CIOS Honor Roll Spring 2025
• CIOS Honor Roll Summer 2025
• Alpha Pi Mu Most Approachable Professor Award 2005
• Selected to attend the WELI Leadership Workshop 2005