Assistant Professor of Business Administration
Nikos Trichakis is an assistant professor of business administration in the Technology and Operations Management Unit, teaching the Technology and Operations course in the MBA required curriculum and the Operations Management course in the doctoral curriculum.
In his research, Professor Trichakis investigates the interplay of fairness and efficiency in resource allocation problems and operations, together with the inherent tradeoffs that arise in balancing these objectives. He is interested in diverse industry applications ranging from health care to airlines to finance. His work has been published in Management Science and Operations Research.
Professor Trichakis received his Ph.D. in operations research from the Massachusetts Institute of Technology. He also holds MS degrees from Stanford University and Imperial College (UK), and a BS degree from Aristotle University (Greece), all in electrical and computer engineering. Before his doctoral studies, Professor Trichakis worked in software development at Sungard APT in London.
Pareto Efficiency in Robust Optimization
This paper formalizes and adapts the well-known concept of Pareto eﬃciency in the context of the popular robust optimization (RO) methodology for linear optimization problems. We argue that the classical RO paradigm need not produce solutions that possess the associated property of Pareto optimality and illustrate via examples how this could lead to inefficiencies and sub-optimal performance in practice. We provide a basic theoretical characterization of Pareto robustly optimal (PRO) solutions, extend the RO framework by proposing practical methods that verify Pareto optimality, and generate solutions that are PRO. Critically important, our methodology involves solving optimization problems that are of the same complexity as the underlying robust problems, hence the potential improvements from our framework come at essentially limited extra computational cost. We perform numerical experiments drawn from three diﬀerent application areas (portfolio optimization, inventory management, and project management), which demonstrate that PRO solutions have a signiﬁcant potential upside compared with solutions obtained via classical RO methods.
Keywords: robust optimization;
Fairness, Efficiency and Flexibility in Organ Allocation for Kidney Transplantation
We propose a scalable, data-driven method for designing national policies for the allocation of deceased donor kidneys to patients on a waiting list, in a fair and efficient way. We focus on policies that have the same form as the one currently used in the United States. In particular, we consider policies that are based on a point system, which ranks patients according to some priority criteria, e.g., waiting time, medical urgency, etc., or a combination thereof. Rather than making specific assumptions about fairness principles or priority criteria, our method offers the designer the flexibility to select his desired criteria and fairness constraints from a broad class of allowable constraints. The method then designs a point system that is based on the selected priority criteria and approximately maximizes medical efficiency, i.e., life year gains from transplant, while simultaneously enforcing selected fairness constraints. Among the several case studies we present employing our method, one case study designs a point system that has the same form, uses the same criteria, and satisfies the same fairness constraints as the point system that was recently proposed by U.S. policymakers. In addition, the point system we design delivers an 8% increase in extra life year gains. We evaluate the performance of all policies under consideration using the same statistical and simulation tools and data as the U.S. policymakers use. Other case studies perform a sensitivity analysis (for instance, demonstrating that the increase in extra life year gains by relaxing certain fairness constraints can be as high as 30%) and also pursue the design of policies targeted specifically at remedying criticisms leveled at the recent point system proposed by U.S. policymakers.
health care policy;
Health Care and Treatment;
On the Efficiency-Fairness Trade-Off
This paper deals with a basic issue: How does one approach the problem of designing the "right" objective for a given resource allocation problem? The notion of what is right can be fairly nebulous; we consider two issues that we see as key: efficiency and fairness. We approach the problem of designing objectives that account for the natural tension between efficiency and fairness in the context of a framework that captures a number of resource allocation problems of interest to managers. More precisely, we consider a rich family of objectives that have been well studied in the literature for their fairness properties. We deal with the problem of selecting the appropriate objective from this family. We characterize the trade-off achieved between efficiency and fairness as one selects different objectives, and we develop several concrete managerial prescriptions for the selection problem based on this trade-off. Finally, we demonstrate the value of our framework in a case study that considers air traffic management.
Keywords: resource allocation;
Cost vs Benefits;
Air Transportation Industry;
The Price of Fairness
In this paper we study resource allocation problems that involve multiple self-interested parties or players and a central decision maker. We introduce and study the price of fairness, which is the relative system efficiency loss under a "fair" allocation assuming that a fully efficient allocation is one that maximizes the sum of player utilities. We focus on two well-accepted, axiomatically justified notions of fairness, viz., proportional fairness and max-min fairness. For these notions we provide a tight characterization of the price of fairness for a broad family of problems.
Bertsimas, Dimitris, Vivek F. Farias, and Nikolaos Trichakis. "The Price of Fairness
." Operations Research
59, no. 1 (January–February 2011): 17–31.
Infection Control at Massachusetts General Hospital
The case explores the challenges facing Massachusetts General Hospital concerning the adoption of a new infection control policy, which promises to improve operational performance, patient safety, and profitability. The new policy requires coordination between different departments within the hospital, namely the Emergency Department, the Infection Control Unit, and Admission Services. Students are initially asked to assess the operational, financial and clinical implications of the new policy. They are then asked to examine different approaches to its implementation.
The case allows readers to examine a setting where internal coordination across different departments provides significant aggregate benefits for an organization. Coordination in this case, however, also leads to inequitable allocation of costs and benefits across the different departments, which then provides students with an opportunity to explore various implementation challenges and strategies.
Organizational Change and Adaptation;
Health Care and Treatment;
Huckman, Robert S., and Nikolaos Trichakis. "Infection Control at Massachusetts General Hospital." Harvard Business School Case 614-044, November 2013.