Building green airports can be regarded as among the most promising routes to sustainable development of ecosystems and human health. This study aims at addressing the problem of green airport plan selection under an uncertain context by developing an uncertain multiattribute group decision making (MAGDM) model. In the proposed model, the assessment information is characterized in the form of a proportional hesitant 2-tuple linguistic term set (PH2TLTS), which incorporates in binary form linguistic information that can accurately quantify subjective assessment information provided under uncertainty. The weights of assessment attributes of green airport plans are obtained automatically through a nonlinear programming model, which enhances the robustness of the decision-making method. Subsequently, on the basis of PH2TLTSs, three distance measures are proposed: the proportional hesitant 2-tuple linguistic Jaccard distance (PH2TLJD), the supplementary proportional hesitant 2-tuple linguistic normalized Minkowski distance (SPH2TLNMD) and the cluster-based proportional hesitant 2-tuple linguistic normalized Minkowski distance (CBPH2TLNMD). The TOPSIS-based comparison method proposed here can better determine the priorities of PH2TLTSs. The ranking and selection of green airport plans are derived using the PH2TL-VIKOR model. Finally, a case study accompanied by sensitivity and comparative analyses is performed to verify the rationality and feasibility of the proposed model.

This article focuses on employing the dynamic programming algorithm to solve the large-scale group decision-making problems, where the preference information takes the form of linguistic variables. Specifically, considering the linguistic variables cannot be directly computed, the interval type-2 fuzzy sets are employed to encode them. Then, new distance model and similarity model are respectively developed to measure the relationships between the interval type-2 fuzzy sets. After that, a dynamic programming algorithm-based clustering model is proposed to cluster the decision-makers from the overall perspective. Moreover, by taking both the cluster center and the group size into consideration, a new model is introduced to determine the weights of clusters and decision-makers, respectively. Finally, a centroid-based ranking method is developed to compare and rank the alternatives, and two illustrative experiments are provided to illustrate the effectiveness of the proposed method. Comparisons and discussions are also conducted to verify its superiority.

Congestion recognition is necessary for vehicle routing, traffic control, and many other applications in intelligent transportation systems. Besides, traffic facilities in the three-dimensional road network, which contains the fundamental spatiotemporal features for congestion recognition, provides multi-source traffic information. To exploit these traffic big data, in this paper, we propose an attention mechanism-based digraph convolution network (ADGCN) enabled framework to tackle the congestion recognition problem. It can be divided into two parts, spatial relevance modeling and temporal relevance modeling. At first, the representation incorporates spatiotemporal traffic information with the three-dimensional urban network, and partially decouples the global network topology to a single-knot digraph. Then a digraph-based convolution network is used to capture high-order spatial features. Finally, to proceed with time-series features, the multi-modal attention mechanism is introduced to catch the long-range temporal dependence and the congestion classifier is defined accordingly. This distinguishes the proposed model from the conventional congestion recognition methods. Comprehensive experiments are conducted based on real traffic data. The results demonstrate the advantages of the proposed framework over the existing spatiotemporal analysis methods.

This paper presents an integrated metro service scheduling and train unit deployment with a proximal policy optimization approach based on the deep reinforcement learning framework. The optimization problem is formulated as a Markov decision process (MDP) subject to a set of operational constraints. To address the computational complexity, the value function and control policy are parameterized by artificial neural networks (ANNs) with which the operational constraints are incorporated through a devised mask scheme. A proximal policy optimization (PPO) approach is developed for training the ANNs via successive transition simulations. The optimization framework is implemented and tested on a real-world scenario configured with the Victoria Line of London Underground, UK. The results show that the performance of proposed methodology outperforms a set of selected evolutionary heuristics in terms of both solution quality and computational efficiency. Results illustrate the advantages of having flexible train composition in saving operational costs and reducing service irregularities. This study contributes to real time metro operations with limited resources and state-of-art optimization techniques.

This study develops a power-average-operator-based hybrid multiattribute online product recommendation model that considers the consumer's risk attitude to rank categoric product options as a complement to existing recommender systems. Online production recommendation plays a key role in the development of e-commerce, and can greatly improve consumers' shopping experiences. However, few online shopping sites provide interactive decision aids for consumers such that they can articulate their preferences towards multiple selection attributes with the purpose of mitigating choice difficulty and improving decision quality. Additionally, consumers' risk attitudes to online shopping dramatically impact their product choices. In the model proposed in this paper, the risk attitude-based power average (RAPA) operator is used to integrate the risk attitude of the decision-maker into the information fusion process of multiple attribute decision-making. Subsequently, the risk attitude function, with several basic types, is introduced to quantify the risk attitude of the decision-maker for use in the RAPA operator. A proportional hesitant fuzzy 2-tuple linguistic term set (PHF2TLTS) is constructed by incorporating a binary of linguistic information aiming to comprehensively analyze the hybrid product information. With a focus on the information fusion process, the proportional hesitant 2-tuple linguistic RAPA operator and weighted proportional hesitant 2-tuple linguistic RAPA operator are introduced to aggregate a given set of PHF2TLTSs. The validity of the proposed model is demonstrated using an illustrative example, a comparison with existing approaches and detailed explanations of the performance differences.

For multiple-attribute decision making problems in Pythagorean fuzzy environment, few existing aggregation operators consider interrelationships among the attributes. To deal with this issue, this article extends the Bonferroni means to Pythagorean fuzzy sets (PFSs) to provide Pythagorean Fuzzy Bonferroni means. We first extend t-norm and its dual t-conorm to propose the generalized operational laws for PFSs, which can be considered as the extensions of the known ones. Based on these new laws, Pythagorean fuzzy weighted Bonferroni mean operator and Pythagorean fuzzy weighted geometric Bonferroni mean operator are developed, both of them can capture the correlations among Pythagorean fuzzy input arguments and their desired properties and special cases are also investigated in detail. At last, a novel approach is proposed based on the developed operators with its effectiveness being proved by an investment selection problem.

Linguistic computational techniques based on hesitant fuzzy linguistic term set (HFLTS) have been swiftly advanced on various fronts over the past five years. However, one critical issue in the existing theoretical development is that modeling possibility distribution based semantics involves a relatively strict constraint that linguistic terms are uniformly distributed across an HFLTS. Releasing the constraint of uniform HFLTS through which individual semantics could be customized is challenging yet intriguing for participants interested in this topic. Comparative linguistic expressions (CLEs) generated from context-free grammar facilitate flexible and accurate linguistic elicitation, and in consideration of computational simplicity, are transformed into HFLTSs that are machine manipulatable. It is imperative that the precision of customized individual semantics can be significantly improved with respect to different CLEs. This study proposes a novel possibility computation structure for HFLTS possibility distributions based on the linguistic terms similarity measure. The uniquely established linguistic terms in each and every CLE are initially treated as referential items for comparison. Then, possibilities of linguistic terms in a transformed HFLTS can be calculated as their similarity degrees to the predetermined referential item. Subsequently, the interweaving method in which a consistent inner interweaving matrix needs to be constructed is adopted for attitudinal characters to attain appealing degrees characterized in the unit interval. The generated attitudinal HFLTS possibility distributions provide a solution to the problem of modeling individually the semantic implications of CLEs. Several illustrative examples and comparative analyses further demonstrate that individual semantics endowed with attitudinal character model efficiently individual differences in cognitive styles.

The extended Bonferroni mean (EBM) recently proposed differs from the classical Bonferroni mean, as it aims to capture the heterogeneous interrelationship among the attributes instead of presupposing a homogeneous relation among them. In this study, we generalize the EBM to explicitly and profoundly understand its aggregation mechanism by defining a composite aggregation function. We adopt the approach of optimizing the choice of weighting vectors for the generalized EBM (GEBM) with respect to the least absolute deviation of residuals. We also investigate several desirable properties of the GEBM. Our special interest in this study is to investigate the ability of the GEBM to model mandatory requirements. Finally, the influence of replacing the conjunctive of the GEBM is analyzed to show how the change of the conjunctive affects the global andness and orness of the GEBM. Meanwhile, the aggregation mechanism of the EBM is specified and provided with quite intuitive interpretations for application.

Many multiple attribute decision analysis (MADA) problems are characterized by both quantitative and qualitative attributes with various types of uncertainties. Incompleteness (or ignorance) and vagueness (or fuzziness) are among the most common uncertainties in decision analysis. The evidential reasoning (ER) and the interval grade ER (IER) approaches have been developed in recent years to support the solution of MADA problems with interval uncertainties and local ignorance in decision analysis. In this paper, the ER approach is enhanced to deal with both interval uncertainty and fuzzy beliefs in assessing alternatives on an attribute. In this newly developed fuzzy IER (FIER) approach, local ignorance and grade fuzziness are modeled under the integrated framework of a distributed fuzzy belief structure, leading to a fuzzy belief decision matrix. A numerical example is provided to illustrate the detailed implementation process of the FIER approach and its validity and applicability.