Abstract: After the occurrence of natural disasters like super typhoons, it is difficult to promptly meet all rescue needs with the available resources within a short timeframe, and unprioritized affected people may perceive unfair treatment and experience human suffering. This study investigates an emergency facility location and allocation problem after disasters considering fairness and efficiency. We first construct a human suffering function as a fairness judgment basis, then formulate the studied problem as a mixed integer nonlinear programming model, and prove that the problem is NP-hard. To effectively solve the problem, we develop an improved adaptive hybrid algorithm (AHA). It incorporates an adaptive genetic evolution operator and integrates Metropolis criterion of simulated annealing to improve the AHA’s performance. Finally, numerical experiments on a case of Super Typhoon Meranti in Xiamen, China and randomly generated instances are conducted to verify the effectiveness of the proposed model and algorithm. The computational results show that compared to traditional intelligent optimization algorithms, AHA reduces comprehensive rescue costs by 3.22%. Our proposed model and algorithm can effectively solve the emergency relief problem after disasters considering fairness, and provide decision-makers with efficient post-disaster emergency facility location and distribution schemes.