Addressing the complex issue of multi-attribute decision-making within a probabilistic dual hesitant fuzzy context, where attribute weights are unknown, a novel decision-making method based on cumulative prospect theory is proposed, named the probabilistic dual hesitant fuzzy multi-attribute decision-making method based on cumulative prospect theory. Firstly, a decision matrix is formulated, representing probabilistic dual hesitant fuzzy information. Secondly, according to the decision maker’s authentic preference and non-membership information sensitivity, a comprehensive score function suitable for probabilistic dual hesitant fuzzy elements is proposed. The attribute weights are then determined using the entropy method. Next, the value function and decision weight function from the cumulative prospect theory are employed to compute the cumulative prospect value attributed to each available scheme. In addition, a cumulative prospect matrix is constructed, enabling the establishment of scheme rankings based on the comprehensive cumulative prospect value. Finally, the analysis of specific cases and a comparative assessment of methods pertaining to the selection of emergency response schemes collectively demonstrate the rationality and efficacy of the decision-making method presented in this study.

Addressing the complex issue of emergency resource distribution center site selection in uncertain environments, this study was conducted to comprehensively consider factors such as uncertainty parameters and the urgency of demand at disaster-affected sites. Firstly, urgency cost, economic cost, and transportation distance cost were identified as key objectives. The study applied fuzzy theory integration to construct a triangular fuzzy multi-objective site selection decision model. Next, the defuzzification theory transformed the fuzzy decision model into a precise one. Subsequently, an improved Chaotic Quantum Multi-Objective Harris Hawks Optimization (CQ-MOHHO) algorithm was proposed to solve the model. The CQ-MOHHO algorithm was shown to rapidly produce high-quality Pareto front solutions and identify optimal site selection schemes for emergency resource distribution centers through case studies. This outcome verified the feasibility and efficacy of the site selection decision model and the CQ-MOHHO algorithm. To further assess CQ-MOHHO’s performance, Zitzler-Deb-Thiele (ZDT) test functions, commonly used in multi-objective optimization, were employed. Comparisons with Multi-Objective Harris Hawks Optimization (MOHHO), Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Multi-Objective Grey Wolf Optimizer (MOGWO) using Generational Distance (GD), Hypervolume (HV), and Inverted Generational Distance (IGD) metrics showed that CQ-MOHHO achieved superior global search ability, faster convergence, and higher solution quality. The CQ-MOHHO algorithm efficiently achieved a balance between multiple objectives, providing decision-makers with satisfactory solutions and a valuable reference for researching and applying emergency site selection problems.

Space technology, a frontier of global scientific innovation, is crucial for competitive edges and national tech innovation. Amid intensified international competition and rapid technological change, scientifically evaluating a country’s Scientific and Technological Strength in Space Technology (STSST) is vital. A model is innovatively proposed in this study called “Analytic Hierarchy Process-Maximum Entropy-Induced Ordered Weighted Average (AHP-ME-IOWA)” for the assessment of STSST. First, an STSST assessment indicator system is developed with four sub-dimensions: scientific research, industrial operation, innovation output, and policy resources. Second, the AHP model is used to convert experts’ qualitative judgments on indicator importance into initial individual weight vectors. Subsequently, the IOWA operator is employed to aggregate these individual weight vectors, thereby mitigating the impact of outliers and enhancing the robustness of the weights. Specifically, the weights are reordered using the cosine similarity between each expert’s weight vector and the temporary group mean as the induced value. Position weights are then determined via the ME method, and consensus weights are derived through re-aggregation. A systematic evaluation of the United States’ STSST was conducted using this method. The results show that the United States achieved a comprehensive STSST score of 8.73 (out of 10), which is in line with the actual situation, thereby providing empirical validation for the proposed method.