Improved instance generation for kidney exchange programmes

Kidney exchange programmes increase the rate of living donor kidney transplants, and operations research techniques are vital to such programmes. These techniques, as well changes policy regarding programmes, often tested using random instances created by a Saidman generator. We show that produced generator differ from real-world across number important parameters, including average recipients compatible with certain donor. exploit these differences devise powerful upper lower bounds we demonstrate their effectiveness optimally solving benchmark set in seconds; this could not be solved under thirty minutes previous algorithms. then present new for generating far more consistent UK programme. This process provides accurate base comparisons algorithms models, gives policy-makers better understanding potential leading an improved decision-making process.