As a combination of q-rung orthopair fuzzy sets (q-ROFSs) and hesitant fuzzy sets (HFSs), q-rung orthopair hesitant fuzzy sets (q-ROHFSs) are more effective, powerful, and meaningful in solving the complexity, ambiguity, and expert hesitancy of membership and non-membership in multi-attribute decision-making (MADM) problems. And so, based on the advantages of q-ROHFSs, we herein propose an improved TOPSIS model in the q-rung orthopair hesitant fuzzy environment. This model can provide more accuracy in expressing fuzzy and ambiguous information. At first, we propose the distance and similarity measures of q-ROHFSs and the properties related to the distance and similarity measures of q-ROHFSs, and secondly, the axiomatized definition and formula for the entropy of q-ROHFSs. Then, for the case where the attribute weights are totally unknown, a combination of subjective and objective attribute weighting model is proposed. This model not only considers the expert's decision preference, but also the objective situation of the attributes. In addition to the above-mentioned outcomes, this paper also improves the relative closeness formula, increases the preference coefficient, and considers the risk-preference of decision makers. Finally, the proposed model is compared with other methods and used to evaluate the effectiveness of military aircraft overhaul. The method is verified to be scientific, reliable and effective for solving MADM problems.INDEX TERMS q-Rung orthopair hesitant fuzzy sets, distance measure, entropy, multi-attribute decisionmaking, TOPSIS method.
Urban built-up areas confront significant environmental challenges and growing demand for enhanced residents’ well-being. Prioritizing urban green infrastructure (UGI) interventions is crucial for sustainable urban renewal. We propose a six-step framework that integrates urgency, synergy, feasibility, and typology to identify UGI intervention priorities. The framework targets detailed planning units (DPUs) and was applied to Xi’an, China. First, we assess the risks of supply–demand mismatches related to four key urban ecosystem services (UESs), namely air purification, temperature regulation, runoff regulation, and recreation. K-means clustering analysis is utilized to classify the risk typology. Next, we use the dynamic weighting method to diagnose the urgency of comprehensive risk, then evaluate the potential for synergy optimization between DPUs using local univariate and bivariate spatial autocorrelation analysis. The proportion of urban renewal land area in DPUs is employed as an indicator to evaluate the feasibility of the method. Lastly, we adopt the TOPSIS method to identify the priority ranking. Our research reveals that 51.7% of DPUs in Xi’an are at high risk of multiple supply–demand mismatches for UESs and that seven risk types need targeted optimization strategies. The DPUs ranked in the top 30 can be selected as priority UGI intervention units based on urgency, synergy, and feasibility. This study provides a scientific basis for decision making on UGI interventions in sustainable urban renewal.
In the modern security system, material support in emergency is an important part. In order to ensure the supply of necessary living materials and proprietary materials in wartime, major natural disasters and emergency epidemics, all countries have introduced corresponding measures. Emergency logistics system is a necessary condition to ensure the smooth and timely distribution of emergency supplies. This paper applies the complex network theory to study the main statistical characteristics of the three-layer emergency logistics system considering the material needs, material manufacturer and raw material resource supplier, which can provide a reference for optimizing the resource allocation and anti destruction ability of the emergency logistics system.
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