For the first time, the critical worldwide problem of prioritizing zero-emission last-mile delivery (LMD) solutions for sustainable city logistics is addressed and solved in this article. It not only aims to help city logistics companies sustainably decarbonize urban freight distribution but also provide valuable practical guidelines. To evaluate zero-emission LMD solutions, this article presents a novel multicriteria group decision-making methodology with dual hesitant fuzzy (DHF) sets. First, we propose some improved operations on DHF elements and investigate their vital properties. Second, based on these operations, we develop DHF improved weighted averaging operator to overcome the drawbacks of the existing operators on DHF sets. Third, for measuring the weights of criteria, a new model called the cross-entropy-based optimization model (CEBOM) is developed. Fourth, for the rational aggregation of the preferences, we formulate a new method namely score-based double normalized measurement alternatives and ranking according to the compromise solution (SDNMARCOS). The proposed DNMARCOS method couples the linear and vector normalization techniques. It is composed of the complete compensatory model and the incomplete compensatory model. Thus, SDNMARCOS is more robust compared to the available state-of-the-art approaches. To exhibit the applicability of the proposed DHF-CEBOM-SDNMARCOS methodology in real-world settings, a case study for one of the largest Austrian logistics companies in Serbia is provided. The research findings show that electric light commercial vehicles are the best LMD solution. Also, it is recommended to consider electric cargo bikes as a viable mid-term solution. The superiority of the introduced methodology is demonstrated through the comparative investigation.

Uncertainty is often occurred in real-life decision-making problems due to the lack of complete information, imprecise data, and the vagueness of decision making experts in qualitative judgment, thus, the crisp values of criteria may be insufficient to handle such types of complex real situations. As the extension of fuzzy set, intuitionistic fuzzy set and Pythagorean fuzzy set, the Fermatean Fuzzy Set (FFS) has been demonstrated as a powerful tool to handle the uncertainty arisen in practical decision-making problems. Thus, this study aims to introduce an integrated Fermatean fuzzy information-based decision-making method by combining method based on the removal effects of criteria (MEREC) and additive ratio assessment (ARAS) methods with the application in a food waste treatment technology selection problem. By using Fermatean fuzzy numbers, the suggested approach successfully handle the qualitative data and uncertain information that often occur in practical situations. This study consists of four phases. First, entropy measure is developed for FFS and further utilized for determining the experts’ weights. Second, some Fermatean fuzzy Heronian mean operators and their properties are introduced to aggregate the Fermatean fuzzy information. These operators can provide us a valuable means to handle practical multicriteria decision-making problems on FFSs context. Third, an extended MEREC technique is originated to assess objective criteria weights within FFS context. Fourth, an integrated ARAS method is introduced with the combination of proposed entropy measure, generalized weighted Fermatean fuzzy Heronian mean operator and MEREC technique to evaluate and rank the alternatives. To confirm the reasonableness and practicality of the proposed methodology, an empirical case study of food waste treatment technology selection is discussed on FFSs settings. Further, a comparison with extant models and a sensitivity investigation are performed to confirm the validity and robustness of the obtained outcomes.

Sustainable site selection for electric vehicle charging station (EVCS) is a significant process in the promotion of electric vehicle system development. The assessment and selection of suitable EVCS site is a very critical decision, involving complexity due to the presence of several associated criteria. Furthermore, uncertainty is an inevitable component of the information in the decision-making procedure and its significance in the selection process is relatively high and needs to be cautiously measured. Single-valued neutrosophic set (SVNS) is one of the valuable and flexible tools for handling such type of uncertain information arising in multi-criteria decision-making (MCDM) applications. Thus, the objective of this study is to introduce novel single-valued neutrosophic information-based additive ratio assessment (ARAS) approach for evaluating and prioritizing the sustainable EVCS sites. In this method, novel single-valued subjective and objective weighted integrated approach (SVN-SOWIA) is developed to compute the criteria by aggregating the objective weights resulted from a similarity measure-based procedure and the subjective weights given by the experts. For this purpose, an innovative similarity measure is proposed for SVNSs. To display the performance of the present methodology, a computational study of EVCS sites evaluation is conferred under single-valued neutrosophic environment. Comparative and sensitivity analyses are further performed to verify the strength of the developed approach. The outcome illustrates EVCS site EvUrjaa—Electric Vehicle Charging Station is the most optimal EVCS site in Indore region, India. Also, the environmental (0.324) and social (0.273) criteria are more important than technological (0.236) and economical (0.167) criteria in assessing the EVCS sites. The sensitivity analysis outcomes signify the EVCS option EvUrjaa—Electric Vehicle Charging Station always acquires its highest ranking in spite of how sub-criteria weights fluctuate. The outcome of this study indicates that the developed approach can suggest more realistic performance under uncertain environment and therefore, provides a wide range of applications.

In this paper, we develop a multicriteria group decision-making methodology with probabilistic linguistic q-rung orthopair fuzzy sets (PLqROFSs). The benefit of choosing PLqROFSs is that they consider the simultaneous occurrence of stochastic and nonstochastic uncertainty and so are superior to probabilistic hesitant fuzzy sets, linguistic intuitionistic fuzzy sets, and linguistic Pythagorean fuzzy sets. To develop the methodology, we first propose two types of operators, namely, probabilistic linguistic q-rung orthopair fuzzy weighted Generalized Dombi operator which gives the flexibility of choosing the parameter values and probabilistic linguistic q-rung orthopair fuzzy weighted Generalized Dombi Bonferroni mean operator which can consider the interrelationships between criteria. Since both the subjective and objective weights of experts are hardly considered in the current PLqROFSs-based research, so in our proposed methodology, we deploy the thought of consistency and similarity between the experts to calculate the subjective and objective weights, respectively, of the experts and as a result the evaluation results do not get malformed. For measuring the weights of criteria, the gray correlation coefficient of the assessment value of criteria is used to reflect the similarity between the criteria and its reference value. To exhibit the applicability of the proposed operators, we provide a case study on biomass feedstock selection. Moreover, to make sure that our model is stable, we have investigated a sensitivity analysis of parameters. At the end, we make a comparison of our approach to different existing schemes.