Abstract: After major natural disasters like super typhoons, available resources are often insufficient to meet all rescue needs within a short timeframe. Affected populations who are not prioritized for assistance may perceive unfair treatment and experience human suffering. This study addresses the issues of post-disaster emergency facility location and resource allocation considering fairness and efficiency. We first construct a human suffering function as a measure of fairness in relief operations. Then, the studied problem is formulated as a mixed integer nonlinear programming model, with its NP-hardness being proved. To effectively solve the problem, an improved adaptive hybrid algorithm (AHA) is developed, which incorporates an adaptive genetic evolution operator and a simulated annealing mechanism to improve the AHA’s performance. Finally, numerical experiments on a case study of Super Typhoon Meranti in Xiamen, China, along with randomly generated simulation instances are conducted to verify the effectiveness of the proposed model and algorithm. Results show that compared to traditional intelligent optimization algorithms, AHA reduces comprehensive rescue costs by 3.22%. Furthermore, our proposed model and algorithm can effectively solve the post-disaster emergency relief problem considering fairness, providing decision-makers with efficient schemes of emergency facility location and resource allocation.