다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

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Convex Analysisの二,三の進展について

Stable non-Gaussian random processes , by G. Samorodnitsky and M. S. Taqqu. Pp. 632. £49.50. 1994. ISBN 0-412-05171-0 (Chapman and Hall).

Foundations Of Statistics

Introduction to stochastic programming

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.

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The Price of Fairness

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 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.

This paper was accepted by Yossi Aviv, operations management.

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On the Efficiency-Fairness Trade-off

We develop a theory that shows signaling a firm’s fundamental quality (e.g., its operational capabilities) to lenders through inventory transactions to be more efficient—it leads to less costly ope...

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On the Financing Benefits of Supply Chain Transparency and Blockchain Adoption

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 that 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. policy makers. 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. policy makers 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. policy makers.

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Fairness, Efficiency, and Flexibility in Organ Allocation for Kidney Transplantation

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 we illustrate via examples how this could lead to inefficiencies and suboptimal performance in practice. We provide a basic theoretical characterization of Pareto robustly optimal PRO solutions and 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 using classical RO methods.

This paper was accepted by Gerard P. Cachon, optimization.

Nikolaos Trichakis

Papers

The Price of Fairness

On the Efficiency-Fairness Trade-off

On the Financing Benefits of Supply Chain Transparency and Blockchain Adoption

Fairness, Efficiency, and Flexibility in Organ Allocation for Kidney Transplantation

Pareto Efficiency in Robust Optimization