In the US, colorectal cancer (CRC) is a significant burden on society as the 2^nd most deadly cancer. This burden can be mitigated by early detection or prevention via screening asymptomatic individuals using a screening strategy. The progression of CRC is not known with certainty, exhibiting a challenge for determining an optimal screening strategy for populations. A microsimulation model is utilized to incorporate this uncertainty within a population to estimate the benefits of a given screening strategy. The optimization problem for determining CRC screening strategies is formulated as a combinatorial problem, a challenging problem type for derivative-free optimization (DFO) solvers. We assess ten DFO solvers’ ability to handle combinatorial problems using a test problem. Then, a simulation-optimization approach is used to determine the optimal strategy for the population. The best-identified screening strategies were shown to reduce the societal impact of CRC more so than the currently recommended screening strategies.

在美国，结直肠癌（CRC）是作为2社会显著负担^ND最致命的癌症。通过使用筛查策略筛查无症状个体，可以通过早期发现或预防来减轻这种负担。CRC的进展尚不确定，对确定人群的最佳筛查策略提出了挑战。利用微观模拟模型将这种不确定性纳入人群中，以估算给定筛查策略的收益。用于确定CRC筛选策略的优化问题被表述为组合问题，这是无导数优化（DFO）求解器的具有挑战性的问题类型。我们评估了十个DFO求解器使用测试问题处理组合问题的能力。然后，使用模拟优化方法来确定总体最优策略。