Seyed Iravani
Northwestern University, IL·Engineering
Waiting is part of nearly every service, and how people experience a wait depends as much on what they are told as on how long it actually lasts. Behind the scenes, services also face a matching problem: deciding which worker covers which shift when both supply and demand are uncertain.
- Fields
Engineering
Mathematics & Statistics
Medicine & Clinical Research
- Topics
operations research
queueing theory
delay announcements
- Grants
- 2 known grants, through Jul 2028
What they work on
- Develop behavioral and analytical models of how customers react to delay announcements, combining stochastic dynamic programming and queueing theory with prospect theory and econometrics to decide what wait information to give and when.
- Replace forecast accuracy with criteria that capture human responses to wait forecasts, including loss aversion and the passage of time, as the design objective for delay announcement systems.
- Test and refine the resulting delay announcement policies through field experiments in a network of emergency departments, measuring patient satisfaction and abandonment.
- Formulate nurse-to-shift matching as binary and integer polynomial optimization problems that incorporate nurse availability, shift preferences, no-shows and equity, and develop solution methods for them.
- Design adaptive matching policies for settings where nurse supply and hospital shift demand arrive dynamically, and evaluate them using simulation and real data from an online staffing platform.
- Estimate nurses' preferences over shift attributes from platform data and embed these estimated preference functions in the static and dynamic matching models.
More about the research
Both projects here treat these as engineering problems, using mathematical models of randomness and decision-making — and, in one case, experiments in real hospitals — to design systems that work better for people.
Why it matters
Better-designed wait announcements could keep emergency department patients from leaving before being seen — reducing their risk of return visits, hospitalization and death — at almost no cost, with the same techniques applying to call centers, restaurants, hotels and financial services. The nurse-matching work could help staffing platforms fill vacant hospital shifts more efficiently and equitably, easing nurse shortages, and could transfer to other online labor and service marketplaces.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Pengyi Shi
Purdue University, IN·Engineering
Build a decision-support system that pairs machine learning demand forecasts with stochastic optimization to recommend staffing assignments across multiple hospital facilities in real time.
Rajan Udwani
University of California-Berkeley, CA·Engineering·Early-career
Formulate new models of online resource allocation that capture the complex features, objectives and constraints of applications such as shared mobility, electric-vehicle battery swapping and online advertising.
Miguel Lejeune
George Washington University, DC·Engineering
Advance real options analysis for infrastructure protection investment under multiple sources of uncertainty, building on optimal stopping theory and the Black-Scholes model from finance.
The school
Northwestern University: 71 labs on PI Pages, $1.3B a year on research, 536 research doctorates a year.
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