This paper has outlined a method (called OBGP) of extending BGP to support lightpath setup and management across an optical network. The development of OBGP has been discussed by reviewing current BGP behavior and design requirements for OBGP. An implementation of OBGP using simulation tools has been presented, along with initial test results, which have shown that a seamless migration from BGP to OBGP is possible.

Kidney transplant is the preferred method of treatment for patients suffering from kidney failure. However, not all patients can find a donor which matches their physiological characteristics. Kidney exchange programs (KEPs) seek to match such incompatible patient-donor pairs together, usually with the main objective of maximizing the total number of transplants. Since selecting one optimal solution translates to a decision on who receives a transplant, it has a major effect on the lives of patients. The current practice in selecting an optimal solution does not necessarily ensure fairness in the selection process. In this paper, the existence of multiple optimal plans for a KEP is explored as a mean to achieve individual fairness. We propose the use of randomized policies for selecting an optimal solution in which patients' equal opportunity to receive a transplant is promoted. Our approach gives rise to the problem of enumerating all optimal solutions, which we tackle using a hybrid of constraint programming and linear programming. The advantages of our proposed method over the common practice of using the optimal solution obtained by a solver are stressed through computational experiments. Our methodology enables decision makers to fully control KEP outcomes, overcoming any potential bias or vulnerability intrinsic to a deterministic solver.

The privileged treatment for patients suffering from end-stage renal disease is transplantation. In Kidney Exchange Programs (KEPs), each participating patient is registered together with an incompatible donor. Then, KEPs maximize overall patient benefit through donor exchanges. Compatibility requirements can severely restrict these programs, with certain patients waiting long to be selected for an exchange. Thus, the policy governing the selection of exchanges requires a careful balance between fairness in terms of individual access to transplantation and utilitarian criteria (e.g., total number of transplants). In this work, we review several fairness schemes, interpret them in the context of KEPs and describe their weaknesses. To overcome those drawbacks, we propose a novel scheme based on the Nash Social Welfare Program that simultaneously optimizes the fairness and the utilitarian objectives. Because the application of fairness schemes and a utilitarian objective implies solving conic programs of exponential size, we design a decomposition methodology to efficiently run them on KEP instances. Finally, we make an extensive comparison of the different schemes and validate the scalability of our methodology for benchmark instances from the literature.

No abstract

No abstract