Health-care waste (HCW) management is a major challenge for municipalities, particularly in the cities of developing nations. Selecting the best treatment technology for HCW can be regarded as a complex multi-criteria decision making (MCDM) issue involving a number of alternatives and multiple evaluation criteria. In addition, decision makers tend to express their personal assessments via multi-granularity linguistic term sets because of different backgrounds and knowledge, some of which may be imprecise, uncertain and incomplete. Therefore, the main objective of this study is to propose a new hybrid decision making approach combining interval 2-tuple induced distance operators with the technique for order preference by similarity to an ideal solution (TOPSIS) for tackling HCW treatment technology selection problems with linguistic information. The proposed interval 2-tuple induced TOPSIS (ITI-TOPSIS) can not only model the uncertainty and diversity of the assessment information given by decision makers, but also reflect the complex attitudinal characters of decision makers and provide much more complete information for the selection of the optimum disposal alternative. Finally, an empirical example in Shanghai, China is provided to illustrate the proposed decision making method, and results show that the ITI-TOPSIS proposed in this paper can solve the problem of HCW treatment technology selection effectively.

Failure mode and effect analysis (FMEA), as a proactive reliability management technique, has been widely employed to reduce the risk of systems and assure the quality of products in various industries. Nevertheless, the traditional FMEA method shows many weaknesses when used in real-life scenarios. This causes the dilemma for practitioners that it is not great as expected. Many alternative risk priority models have been developed to enhance the capacity of FMEA, but the majority of them focus on how to acquire a complete ranking of failure modes. In this article, we propose a novel FMEA approach using hesitant uncertain linguistic Z numbers (HULZNs) and density-based spatial clustering of applications with noise (DBSCAN) algorithm to assess and cluster the risk of failure modes. The HULZNs are adopted to represent the uncertain and hesitant risk evaluation information of FMEA team members. The normal DBSCAN algorithm is improved to cluster the recognized failure modes into different risk classes. Moreover, the weights of FMEA experts are dynamically obtained with a weight adjustment method. Finally, a practical case of a geothermal power plant is given to demonstrate the effectiveness and validity of our introduced FMEA approach.

The freight sector is an important component of China’s national economy. It is composed of multiple sub-sectors and has a complex internal structure. This internal structure can hide information on the freight sector’s operational performance. Previous studies on transportation operational performance made measurements based on the whole transportation sector, and all of these studies ignored the impacts that the internal structure of the sub-sectors have on performance, which leaves a gap in the research. To illustrate this structure, this study proposes a parallel slacks-based measure model to measure transportation efficiency, which can represent the freight sector’s operational performance. The efficiencies of transportation operations for the whole freight sector and its three sub-sectors are further measured, by treating the sub-sectors as parallel subunits. Then, the inefficiency sources from the sub-sectors can be identified by the proposed model. To detect the environmental impact on transportation operations, energy consumption and carbon dioxide emissions are also considered in the evaluation. On the basis of the proposed approach, an application of the Chinese freight sector from 2013 to 2017 is provided. The impacts of influential factors on transportation efficiency are also explored. The empirical findings can be illustrated as follows: (1) there exist significant disparities in regional transportation efficiencies in the freight sector and its sub-sectors; (2) the inefficient transportation performance of the Chinese freight sector mainly derives from the poor performance of the waterway sub-sector; and (3) freight volume and population density have positive impacts on the transportation efficiencies of the railway and highway sub-sectors. Finally, some policies for improving transportation efficiency are also provided.

Electric Vehicles (EVs) are considered to be a potential viable technology to address the persistent unsustainable problems in transport sector. In this paper, we focus on analyzing the transition processes of EVs in China because the sustainability of developing countries is essential for the worldwide sustainability. The two-round demonstration programs of EVs in China were analyzed by adopting the strategic niche management (SNM) approach so as to find out what niche protection has been provided and which obstacles hamper the further development of EVs. The results show that the financial subsidy is the most important protective measure. However, the diffusion results of EVs in different pilot cities are greatly different. The main reason lies in the uneven geographical landscape. In addition, some obstacles were exposed during the niche internal processes including low quality of expectations and poor alignment within the network. Based on the analysis results, we develop a list of suggestions that are important to consider when developing EVs.