Charles E. Morrison Professor of Decision Sciences, Professor of Operations, Co-Director of MMM Program at Kellogg School of Management
Kellogg School of Management
Achal Bassamboo is the Charles E. Morrison Professor at Kellogg School of Management, Northwestern University. He is also the co-director of the MMM program which is dual degree program between Kellogg and Segal Design at McCormick School. Professor Bassamboo joined the faculty at the Kellogg School of Management in 2005, after completing his Ph.D. in Operations, Information and Technology at the Stanford Graduate School of Business. His research interests lie in the areas of service systems, revenue management and information sharing. His current research involves designing flexible service systems with a focus on capacity planning and effects of parameter uncertainty. He is also studying credibility (or lack thereof) of information provided by a service provider or a retailer to its customers.
His articles have appeared in leading journals, including Management Science, Manufacturing and Service Operations Management, and Operations Research. Professor Bassamboo was award the 2016 "Young Scholar Award" given by the Manufacturing and Service Operations Management Society for his research. He has served on the editorial boards for Management Science, POMS and Naval Research Logistics. Professor Bassamboo teaches courses on operations management, supply chain logistics, decision models and statistics.
Applied probability and stochastic models; stochastic systems: performance analysis and optimal control; revenue management; operations management; Empricial Operations
- Ph.D., 2005, Operations, Information, and Technology, Stanford University
- M.S., 2004, Statistics, Stanford University
- B.Tech., 2000, Mechanical Engineering, Indian Institute Of Technology
- Charles E. Morrison Professor of Decision Sciences, Managerial Economics and Decision Sciences, Operations, Kellogg School of Management, Northwestern, 2016-present
- Professor, Managerial Economics and Decision Sciences, Kellogg School of Management, Northwestern University, 2012-present
- Associate Professor, Managerial Economics and Decision Sciences, Kellogg School of Management, Northwestern University, 2009-2012
- Assistant Professor, Managerial Economics and Decision Sciences, Kellogg School of Management, Northwestern University, 2006-2009
- Donald P. Jacobs Scholar, Managerial Economics and Decision Sciences, Kellogg School of Management, Northwestern University, 2005-2006
- Research and teaching assistant, Stanford University, 2001-2005
- Professor of the year for core course: PGP Class of 2020 Mohali Cohort, Indian School of Business.
- Chair Core Teaching Award
- Finalist for the Service Management SIG Prize, MSOM Society 2019 for "Scheduling Homogeneous Impatient Customers" published at Management Science., MSOM, INFORMS
- Professor of the year for core course: PGPpro Class of 2019 Mohali Cohort, Indian School of Business.
- Professor of the year for core course: PGP Class of 2019 Hyderabad Cohort, Indian School of Business., Indian School of Business
- Professor of the year for core course: PGPpro Class of 2018 Hyderabad Cohort, Indian School of Business., Indian School of Business
- Chair Core Teaching Award, Kellogg School of Business
- MSOM Young Scholar Prize, Manufacturing & Service Operations Management, INFORMS, Researcher under the age of 40
- Second Place, Article, Junior Faculty Interest Group, 2008
- Chairs Core Course Teaching Award, Kellogg School of Management, 2009-2010
- Silver Medal, IIT Delhi
- Third Place, Article, Junior Faculty Interest Group, 2010
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Bassamboo, Achal and Ramandeep S. Randhawa. 2010. On the Accuracy of Fluid Models for Capacity Planning in Queueing Systems with Impatient Customers. Operations Research. 58(5): 1398-1413.
We consider queueing systems in which customers arrive according to a Poisson process and have exponentially distributed service requirements. The customers are impatient and may abandon the system while waiting for service after a generally distributed amount of time. The system incurs customer-related costs that consist of waiting and abandonment penalty costs. We study capacity sizing in such systems to minimize the sum of the long-term average customer-related costs and capacity costs. We use fluid models to derive prescriptions that are asymptotically optimal for large customer arrival rates. Although these prescriptions are easy to characterize, they depend intricately upon the distribution of the customer's time to abandon and may prescribe operating in a regime with offered load (the ratio of the arrival rate to the capacity) greater than 1. In such cases, we demonstrate that the fluid prescription is optimal up to O( 1). That is, as the customer arrival rate increases, the optimality gap of the prescription remains bounded.
Bassamboo, Achal, Ramandeep S. Randhawa and Assaf Zeevi. 2010. Capacity Sizing Under Parameter Uncertainty: Safety Staffing Principles Revisited. Management Science. 56(10): 1668-1686.
We study a capacity sizing problem in a service system that is modeled as a single-class queue with multiple servers and where customers may renege while waiting for service. A salient feature of the model is that the mean arrival rate of work is random (in practice this is a typical consequence of forecasting errors). The paper elucidates the impact of uncertainty on the nature of capacity prescriptions, and relates these to well established rules-of-thumb such as the square-root safety staffing principle. We establish a simple and intuitive relationship between the incoming load (measured in Erlangs) and the extent of uncertainty in arrival rates (measured via the coefficient of variation) that characterizes the extent to which uncertainty dominates stochastic variability or vice versa. In the former case it is shown that traditional square-root safety staffing logic is no longer valid, yet simple capacity prescriptions derived via a suitable newsvendor problem are surprisingly accurate.
Bassamboo, Achal and Sachin Jain. 1999. A Heuristic for Job shop Scheduling under a two class case.
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