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The evaluation presentation and numerical weight are two key factors that can affect final results in a decision-making process. In this article, we introduce the hesitant fuzzy set and the envelopment rate calculation to address these two issues respectively. Unlike the previous decision-making approaches, the newly proposed methods in this article focus on mutual influence relationships of all alternatives and make decisions by achieving the Pareto optimality. To do this, we first define the mutual hesitant fuzzy envelopment rate (MHFER) and then propose the generalized hesitant fuzzy envelopment analysis method and its two desired properties. Based on this, we develop an evolutionary computation by introducing the ideas of cooperative equilibrium and achieving the Pareto optimality of the whole decision-making process from a global perspective. After that, we further improve the evolutionary computation by fusing some inequality constraints to make the calculated MHFERs distinguishable. Then, the modeling steps and the pseudocode algorithm of the evolutionary computation are provided for an understanding of the proposed methods. Also, the computational convergence is proven, which shows the feasibility and effectiveness of the above methods. Finally, we present an example to select a suitable contractor for a green building project and it fully demonstrates the application of the proposed models. Moreover, the comparison calculations and further analysis are also provided.

Generally, when making decisions, decision makers always have their subjective opinions regarding attributes, alternatives or even themselves based on their experience and knowledge. Although some methods, such as weighted aggregation, preference analysis, and fuzzy decision-making, can be used to describe and fuse these opinions, these methods involve two limitations, specifically, inaccurate numerical presentation and stepwise aggregation method. To address these issues, in this paper, we define multiple order relations expressed as several inequalities to describe these subjective opinions, and then propose hesitant fuzzy global fusion models to simultaneously fuse the multiple order relations and make a decision. To this end, we first introduce the hesitant fuzzy envelopment rate and develop partial fusion models for single order relations; subsequently, multiple order relations and global fusion model are proposed. Moreover, to ensure computability, we further derive the linear forms of these models and summarize the improvement schedules for the nonoptimal alternatives. According to these calculations and results, a hesitant fuzzy decision analysis including subjective opinion fusion, evaluation presentation, decision-making calculation, alternative ranking and selection, and nonoptimal improvement, can be performed. Finally, an illustrative example based on a real background is considered to show the effectiveness of the models and decision analysis processes.

A mass spectral library pre-search algorithm, fuzzy-precise positioning (FPP), is described for rapid and accurate identification of chemical compound. This algorithm works on the basis of the molecular ion feature peaks and fragment ion feature peaks of mass spectra. The algorithm mainly includes three stages: feature peaks selection, fuzzy positioning and precise positioning. For query spectra and reference spectra, four molecular ion feature peaks and six fragment ion feature peaks are selected and their corresponding mass-to-charge ratio (m/z) values are recorded, respectively. After that, fuzzy positioning is conducted to skip some unrelated spectra in the reference library by comparing maximum and minimum m/z values of feature peaks of query spectra with those of reference spectra. Finally, in the precise positioning stage, ten m/z values of feature peaks of the query spectra are matched with those of remaining reference spectra one by one. If the matching number is larger than five, this reference spectrum can be remained as a candidate spectrum. After traversing all the reference spectra remained after fuzzy positioning, final pre-search results can be obtained. To evaluate the performace of the the proposed algorithm, a series of experiments are designed by using main library and replicate library of NIST11 as reference library and query library, respectively. Moreover, real application data are used to verify the performance of FPP method. Compared to two-step spectral library pre-search (TSLP) algorithm and "ten-peak" method, the results show that FPP method can obtain higher accuracy, smaller pre-search space, shorter time consumption and less remaining spectra.

With the advent of the era of global high-tech industry and commerce and its associated sedentary lifestyle, opportunities for physical activity are reduced. People's physical fitness and health is deteriorating. Therefore, it is necessary to develop a system that can enhance people's physical fitness and health. However, it is difficult for general physical fitness and healthcare systems to meet individualized needs. The main purpose of this research is to develop a method of intelligent diagnosis and prescription for a customized physical fitness and healthcare system. The proposed system records all processes of the physical fitness and healthcare system via a wireless sensor network and the results of the diagnosis and prescription will be generated by fuzzy logic inference. It will improve individualized physical fitness and healthcare. Finally, we demonstrate the advantages of intelligent diagnosis and prescription for a customized physical fitness and healthcare system.

Physical fitness and health of white collar business person is getting worse and worse in recent years. Therefore, it is necessary to develop a system which can enhance physical fitness and health for people. Although the exercise prescription can be generated after diagnosing for customized physical fitness and healthcare. It is hard to meet individual execution needs for general scheduling of physical fitness and healthcare system. So the main purpose of this research is to develop an intelligent scheduling of execution for customized physical fitness and healthcare system. The results of diagnosis and prescription for customized physical fitness and healthcare system will be generated by fuzzy logic Inference. Then the results of diagnosis and prescription for customized physical fitness and healthcare system will be scheduled and executed by intelligent computing. The scheduling of execution is generated by using genetic algorithm method. It will improve traditional scheduling of exercise prescription for physical fitness and healthcare. Finally, we will demonstrate the advantages of the intelligent scheduling of execution for customized physical fitness and healthcare system.

A new evolution method is proposed in this paper to estimate inland river channel transit capacity. Compared with the conventional methods which usually use Fujii ellipsoid ship domain model, the dynamic quaternion ship domain (DQSD) model is used to determine the safe region of the ships sailing in the inland river channel, which takes ship dynamics, human reliability and circumstance factor into account. Fuzzy logics also are applied to obtain the human factors of DQSD model from the questionnaire investigating data. The transit capacities from Yingong island channel data show that the new evaluation method can reduce the conservation of the transit capacity estimation.