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1Korea University Business School, Korea, Republic of (South Korea); 2David Eccles School of Business, University of Utah; 3Marshall School of Business, University of Southern California
Limited resources in the judicial system can lead to costly delays and even failure to deliver justice. Using the Supreme Court of India as an exemplar for such resource-constrained settings, we apply ideas from service operations to study delay. Court dynamics constitute a case-management queue which is known to be intractable. Hence, we employ data-driven simulations and find that even small interventions can improve the system performance dramatically.
Data-driven population tracking in large service systems
Morgan Wood1, Fernando Bernstein2, Bora Keskin2, Adam Mersereau3, Serhan Ziya1
1Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, North Carolina 27599; 2Fuqua School of Business, Duke University, Durham, North Carolina 27708; 3Kenan-Flagler Business School, University of North Carolina, Chapel Hill, North Carolina 27599
We develop asymptotically optimal policies to track queue lengths under different cost structures in a setting with inaccurate arrival and departure sensor data. We propose an idleness detection policy and explore the value of queue inspections. Our model is motivated by queue tracking implemented at a large airport.
An approximate analysis of dynamic pricing, outsourcing, and scheduling policies for a multiclass make-to-stock queue in the heavy traffic regime
Nasser Barjesteh1, Baris Ata2
1University of Toronto; 2University of Chicago Booth School of Business
