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In this note, we point out an error to the proof of Theorem 3.4 in Zhang and Xu (Int J Intell Syst 2014;29(12):1061-1078) by a counterexample. We find that the inequality (i.e. [ABSTRACT FROM AUTHOR]

A ranking method of intuitionistic trapezoidal fuzzy numbers (ITFNs) is proposed based on the notions of a minimum expectation and a maximum expectation. Considering risk preferences of decision makers, a novel concept of a risk coefficient is introduced to construct improved operational laws of ITFNs. Furthermore, an intuitionistic trapezoidal fuzzy Bonferroni (ITFB) mean operator is proposed based on the improved operational laws, and then the relative properties of the ITFB mean operator are investigated. With respect to a multi-attribute group decision making problem, in which decision-makers are interdependent, attributes are interdependent, and decision-makers' weights and attributes' weights are both unknown, an improved MULTIMOORA approach for group decision making of interdependent ITFNs inputs is proposed. In this approach, firstly, a set of intuitionistic trapezoidal fuzzy decision matrixes is constructed, and then a set of normalized minimum expectation decision matrixes is obtained by calculating the minimum expectation one corresponding to each intuitionistic trapezoidal fuzzy decision matrixes. Secondly, in order to determine decision-makers' weights and attributes' weights, an entropy weight approach for determining the attributes' weights associated with each decision-maker is integrated into an objective and subjective synthetic approach, which has considered interactions of decision-makers' preferences, for obtaining decision-makers' weights based on the combination of a 2-additive fuzzy measure and a Choquet integral. Finally, a weighted ITFB mean operator and a Hamming distance of ITFNs are respectively introduced to improve the traditional MULTIMOORA approach, the improved MULTI-MOORA approach is employed to obtain a ranking of alternatives corresponding to each one of three ordering approach, and then a dominance theory is utilized to summarize the three rankings into a single one. A practical case is used to illustrate the validity and feasibility of the proposed approach. [ABSTRACT FROM AUTHOR]

Uncertainty in information provides the possibility of having and applying preferences, and therefore, it necessitates the study of preferences-involved aggregation theory and techniques under uncertainty information environments. Hence, this paper discusses several rules-based decision-making methods in uncertain decision scenarios with or without fusion processes. Some special averaging aggregation methods and transformations for basic uncertain information vector are proposed. [0, 1]-valued, [ - 1 , 1]-valued, and vector-valued bipolar preferences with some of their relations are discussed. The discussions and analyses of aggregation mainly revolve around Sugeno integral. Finally, multiple aggregation schemes with vector-valued preferences with numerical examples are proposed, and some few related comparisons are provided. [ABSTRACT FROM AUTHOR]

To construct Lorenz curves of high fitting precision, this paper put forward two single-parameter models for Lorenz curves. Furthermore, it constructed a set of extended Lorenz curves by means of ways of such as compound, the weighted product and the convex combination, etc. based on the two proposed models. An illustration example shows that, compared with utilizing the Lorenz curves of trigonometric forms or the Lorenz curves of Pareto distribution separately, the Lorenz curves generated by the combination the Lorenz curves of trigonometric forms and the Lorenz curves of Pareto distribution have higher fitting precision, which verify the Lorenz curves of trigonometric forms are effective extensions of the existing ones. [ABSTRACT FROM AUTHOR]

Supplier selection (SS) has emerged as a critical challenge for companies aiming to enhance the operational management of their supply chains, a task that has grown in complexity with the advent of Industry 4.0 and the ongoing digital transformation. Recognizing the gaps in current literature—specifically, the lack of consideration for stakeholders' expectations in guiding SS, as well as the inadequate handling of epistemic and stochastic uncertainties—this paper introduces a multiple-criteria Quality Function Deployment (QFD)-based model for SS. To address epistemic uncertainty, we put forward a novel subjective judgment representation method, which is named as linguistic term set integrated with discrete subjective probability distribution (LTS-DSPD), to enable decision-makers to express their judgments in a manner that is both simpler and more nuanced. Furthermore, we also give the elicitation methods and computing techniques for LTS-DSPD. Then, we integrate stakeholders' requirements, along with their preferences and expectations for these requirements to inform and guide SS. To effectively operationalize this guidance, we design the QFD-based methods to transform stakeholders' inputs into the assessment criteria for SS, the weights of criteria, and the expectations for the performances of suppliers on each criterion, respectively. To address stochastic uncertainty, we have developed an innovative methodology for characterizing it, and adopt prospect theory to quantify the overall utility of alternative suppliers. The paper concludes with a case study to demonstrate its practical application and effectiveness in streamlining SS process. [ABSTRACT FROM AUTHOR]

The increasing frequency of extreme weather (EW) events has created significant vulnerabilities in the normal operations of the prefabricated construction supply chain (PCSC). This study aims to enhance the resilience of the PCSC against EW by utilizing smart contracts. The study proposes a prototype smart contract application to mitigate the risks posed by EW to the PCSC. Additionally, it identifies 28 potential barriers affecting smart contract adoption in the PCSC using the Technology-Organization-Environment framework. Furthermore, the paper presents a multi-objective optimization-based group decision-making method to assess the feasibility of smart contract adoption in the PCSC. An online survey was then conducted among 50 stakeholders from various links of the PCSC to gather insights into smart contract adoption. The results indicate that stakeholder awareness of smart contracts and the current corporate level are the most influential factors in decision-making. This research extends the application of smart contracts to risk management within the PCSC, offering valuable insights for stakeholders to enhance resilience and address the adverse effects of EW proactively. [ABSTRACT FROM AUTHOR]

Basic uncertain information is a recently introduced and significant type of uncertainty that proves particularly valuable in decision-making environments with inherent uncertainties. In this study, we propose the concept of uncertainty cognition merging, which effectively combines basic uncertain information granules with probability measures to generate new probability measures within the same probability space. Additionally, we present a degenerated method that merges basic uncertain information granules with unit intervals to create new subintervals. We introduce four distinct uncertainty cognition merging methods and thoroughly compare and analyze their respective properties, limitations, and advantages. To demonstrate the practical application potential of our proposals, we provide numerical examples alongside further mathematical results. [ABSTRACT FROM AUTHOR]

The available method based on statistical principles for aggregating hesitant fuzzy linguistic term set (HFLTS) possibility distribution is the N -two-stage algorithmic aggregation paradigm driven by the K -means clustering (N2S-KMC). Nonetheless, the N2S-KMC method is subject to two significant limitations. (i) The grouping technique is capable of effectively partitioning decision-making information into N groups. However, it does not determine the appropriate placement of members within each group, as the number of computations is dependent on the number of elements present in each group, rather than the elements themselves. (ii) The initial clustering centers of K -means clustering are chosen without adhering to the distribution law within the aggregated hesitant 2-tuple linguistic terms set (H2TLTS) possibility distribution. This may result in a reduction in the clustering performance. In order to address the aforementioned limitations, we suggest two enhancement techniques for the former. Firstly, we propose the utilization of the minimum average difference (MAD) method to ascertain the number of groups. This approach aims to reduce the time required for the initial stage of aggregation following grouping. Secondly, we recommend the implementation of the maximize compactness degree of inter-group grouping (MCDIGG) method. This method enables the identification of group members, resulting in a more concentrated distribution of data subsequent to grouping. The present study suggests the utilization of MAD and MCDIGG techniques as a substitute for the grouping approach in the N2S-KMC model. This leads to the development of a new algorithm, IN2S-DO-KMC, wherein the data is partitioned into K subsets in a descending order to determine the initial center for KMC. Furthermore, with respect to the issue present in the subsequent phase, we propose the utilization of the density canopy (DC) algorithm to perform pre-clustering of the data and produce the initial clustering center and the quantity of clusters for the K -means algorithm. Subsequently, a refined version of the N2S-KMC model, denoted as IN2S-DC-KMC, has been suggested. Ultimately, an empirical study is conducted to assess the validity and practicability of the proposed framework for evaluating failure modes in medical devices. The outcomes are evaluated with regards to the efficacy of the algorithm, the numerical dispersion, and the pragmatic ramifications. • We propose the MAD method that determines the number of groups for the aggregated HFLTS possibility distributions. • We propose the MCDIGG method that determines member of each group with HFLTS possibility distributions. • We apply an improved density canopy clustering algorithm to the K-means clustering algorithm for pre-clustering process. • We propose a measure to predict the degree of compactness after data aggregated in different grouping cases. • We propose two aggregation algorithms, IN2S-DO-KMC and IN2S-DC-KMC, for HFLTS possibility distributions. [ABSTRACT FROM AUTHOR]

In China, traditional buildings have begun to exhibit a range of issues, such as elevated levels of energy consumption and pollution. Consequently, these concerns have led to substantial resource inefficiency and environmental degradation. The evaluation and examination of green buildings are of utmost importance in promoting sustainable development. The current green building assessment framework is intricate and lacks sufficient development in terms of visual representation. Developing a green building strategy during the initial design phase is a multifaceted process that necessitates substantial allocation of human, material, financial, and temporal resources. In this study, we propose an assessment framework that incorporates a 15 s-level and 45 three-level green building indicator system, along with a 4-level classification standard. This framework is developed based on the most recent Chinese Assessment Standard for Green Building and the utilization of a Building Information Modeling (BIM) database. Furthermore, the integration of BIM with Pathfinder software is employed to assess the safety aspects of green buildings. On top of that, the combination of BIM with Ecotect software is utilized to evaluate the environmental aspects of green buildings. In this study, we performed a case study on a teaching building located at a university in central China, specifically focusing on the simulation of green building practices. The sequential calculation involves determining the duration of personnel evacuation, assessing the lighting conditions, evaluating the thermal conditions, analyzing the sound conditions, and examining the wind conditions. In addition, efforts were made to optimize the indicators requiring enhancement in order to enhance the efficacy of green buildings. • Proposes GBA framework with 15 s-level, 45 third-level indicators based on Chinese standards. • Integrates BIM with simulation software for GBA of safety, lighting, thermal, sound and wind. • Applies proposed GBA framework on a university teaching building case in China. • Determines green building score of 74 and grade level 2 for the case building. • Identifies indoor heat/humidity and energy saving as priority optimization areas. [ABSTRACT FROM AUTHOR]

• Examine existing literature on applying AI in the modular construction industry. • Exploring the production, operations and logistics (POL) management. • Analyze the progress and research patterns of AI for POL management. • Provide potential future directions for AI for POL management. Artificial intelligence (AI) has garnered significant attention within the modular construction industry, emerging as a prominent frontier development trend. A comprehensive and systematic analysis is required to gain a thorough understanding of the existing literature on the use of AI in the management of production, operations, and logistics within the modular construction industry. This review delves into the various aspects of AI implementation in this sector, adopting a critical perspective. The objective of this paper is to analyze the progress, suitability, and research patterns in the field of AI for the management of productions, operations, and logistics within the modular construction industry. First, a concise overview of AI technologies pertaining to the contemporary research on the production, operations and logistics management of the modular construction industry is provided. Second, a bibliometric analysis is performed to provide a comprehensive overview of the existing publications pertaining to this subject matter. Subsequently, this paper presents literature reviews and outlines future directions for each component, specifically AI in the context of production management, operations management, and logistics management within the modular construction industry. The review provides a valuable knowledge base and roadmap to guide future research and development efforts in AI-enhanced modular construction management. [ABSTRACT FROM AUTHOR]

This paper introduces a hybrid intelligent framework that combines Bayesian optimization (BO), a random forest (RF) model, and the nondominated sorting genetic algorithm-III (NSGA-III) for the optimization and control of tunnel shield construction parameters. The BO-RF method establishes a nonlinear mapping function between the input variables and three targets, surface settlement, cutter wear, and advance speed, serving as the fitness function for NSGA-III. Model interpretability analysis is conducted using Shapley Additive ExPlanations (SHAP). A multiobjective intelligent optimization model is formulated with NSGA-III, targeting surface settlement, cutter wear, and advance speed. A case study validates the applicability and effectiveness of this approach, leading to the following conclusions: (1) The BO-RF algorithm yields highly accurate prediction results, with R2 values ranging from 0.930 to 0.938, RMSE ranging from 0.138 to 0.172, and MAE ranging from 0.112 to 0.138 for the three targets. (2) The optimization results for surface settlement, cutter wear, and advance speed are outstanding, with an average improvement of 12.56 %. The simultaneous adjustment of the three shield construction parameters leads to the best optimization results, with an average improvement of 19.67 %. (3) The energy consumption of the shield drive system decreases by an average of 10.70 %, and the optimization improvement for the first three objectives decreases by an average of 1.82 %, 1.46 %, and 2.23 %, respectively. By introducing the integrated BO-RF-NSGA-III algorithm, this study contributes to the field of tunnel engineering optimization management. [ABSTRACT FROM AUTHOR]

With the complex shifts in the international situation, global supply chain networks (SCNs) are now facing disruption risks. The primary objective of this study is to explore the evolutionary mechanisms of multi-level supply chain networks (MLSCNs) and simulate the risk propagation throughout the network. Firstly, this study formulates a MLSCN influenced by the combined preference. It contemplates a cascading risk propagation model that accommodates for node perturbations. Secondly, this study proceeds to examine the effects of parameter variations and capacity dynamics on risk propagation. Finally, the effect of failure probability on the robustness of MLSCNs is considered in the case of node overloading. The conclusions indicate that the developed framework fosters a more robust topology within MLSCNs. Within the context of the cascading risk propagation model, it is observed that parameter variations have a significant impact on the cascading propagation of risk. Moreover, the regulation parameter for load reallocation effectively slows down the rate at which risk cascades to network failure. The study also reveals that dynamic changes in node capacities can intensify network vulnerability, and make it more prone to collapse. Failure probability emerges as a potential countermeasure to prevent network collapse. The outcomes of this research can provide a valuable reference for both researchers and business managers to better understand SCNs and the cascading propagation of risks within them. [ABSTRACT FROM AUTHOR]

In the contemporary enterprise software market, there has been a notable surge in the utilization of SaaS products. This trend has prompted certain upstream suppliers of cloud resources, commonly referred to as cloud providers, to introduce their own SaaS offerings, thereby augmenting the competition within the industry. The matter of optimal releasing strategy encountered by software firms in this industry has been scarcely addressed in prior literature. Therefore, the present study employs the Hotelling model to depict the market segmentation in the context of a software firm's selection among three distinct strategies, namely on-premise, SaaS, and dual version strategies, to effectively manage competition. Through an examination of the optimal pricing and profit for each strategy, and a subsequent analysis of the resulting profits, we have managed to arrive at conclusive recommendations for optimal strategy selection. Based on the analytical findings, it can be inferred that the selection of the optimal strategy can be classified into two distinct scenarios, which are contingent upon the magnitude of the unit cloud rental fee. In a high-cost rental environment, the optimal release strategy shifts from the SaaS strategy and dual version strategy to the on-premise strategy, as the disparity in value between on-premise and SaaS versions increases. In a context of low rental fee and a widening value gap, the optimal strategy choice sequence is as follows: SaaS, on-premise, dual version, on-premise, dual version, and ultimately on-premise. Furthermore, we analyze and reveal the impact of various factors on the maximum profit that software companies can achieve. The findings we have derived have significant implications for software companies' decision-making in the face of the challenge posed by highly centralized public cloud supply. • Cloud providers' entrance intensity the enterprise software market competition. • We propose three releasing strategies, on-premise, SaaS, and dual version. • Hotelling model is adopted to analyze software firms' optimal releasing strategy. • Strategy selection depends on the cloud renting fee and two versions' value gap. • Effects of related factors on profits are identified. [ABSTRACT FROM AUTHOR]

Under the pressure of globalization, environmental protection/resource consumption, and the intensification of market competition, various industries have recognized the significant importance of sustainable supply chain management (SCM), in which reverse logistics (RL) has attracted many concentrations from governments to enterprises and the public. However, it is relatively difficult to operate RL autonomously for most electronic manufacturing companies whose core businesses and competition advantages are not in logistics operations because of limited resources and professional skills. Therefore, it is a good choice to escape from the predicament by selecting an appropriate third-party reverse logistics provider (3PRLP) with professional skills for outsourcing and accomplishing the RL business. Generally, evaluation and selection of a 3PRLP is regarded as a multi-attribute group decision-making (MAGDM) process, in which four aspects, namely, the evaluation attributes system, weight of experts and attributes, preference information representation and alternative ranking method have impacts on the decision-making results. Therefore, this paper constructs an MAGDM framework named generalized TODIM (TOmada de DecisãoInterativa e Multicritério, Portuguese acronym for Interactive multicriteria decision-making) under a hybrid information environment, which includes crisp numbers, interval numbers and fuzzy linguistic terms, for solving the problem. It provides a useful reference for manufacturing enterprises to find out the optimal 3PRLP from many alternatives. In particular, the use of different information forms for different evaluation attributes can not only facilitate decision makers to provide their judgments, but also effectively retain the original decision information as much as possible. Utilization of the generalized TODIM method is helpful for considering the risk attitudes and psychological preference behaviors of decision-makers. Finally, to demonstrate the feasibility and effectiveness of the proposed method, a case study relates to select a sustainable 3PRLP in an electronic manufacturing company was conducted. The findings show that the company should pay more attention to the attributes related to social responsibility, service quality and cost when selecting alternative 3PRLP under the sustainable SCM environment. It also provides useful suggestions for 3PRLP themselves to improve their competitiveness from these aspects. Moreover, the sensitivity and comparative analysis demonstrated the necessity and superiorities of comprehensively considering weights of experts and attributes, adopting hybrid information formats, and taking into account the risk attitudes and psychological preference behaviors of decision-makers during the evaluation and selection process. [ABSTRACT FROM AUTHOR]

The current study centers on the vulnerability of the express network. It involves the development of a composite express network in China that integrates air and high-speed rail transportation, utilizing real-world data. The network’s vulnerability is evaluated through simulation analysis. This study develops a model for calculating the value of node states that takes into account both the topological position and practical utility of said nodes. A node importance calculation model is developed by utilizing the information entropy theory and the pattern of cluster distribution. Two distinct strategies for node attacks have been developed: single-point failure and multiple-point failure. A system for assessing network vulnerabilities has been developed, which utilizes alterations in network structure and functional impairments to simulate and evaluate the vulnerability of the air and high-speed rail composite express network. The findings suggest that nodes exhibiting singular transportation modes and limited external connectivity are more vulnerable to cascading effects. Frequently, these nodal points are affiliated with provinces or self-governing territories that are situated in geographically remote areas and exhibit comparatively lower degrees of economic advancement. Improving the development of urban air and high-speed rail infrastructure, as well as augmenting the connectivity of air and high-speed rail express services, are essential measures to strengthen the self-risk resistance capacity of these hubs. Optimizing the network structure and modifying the internal topological and transportation coupling relationships can enhance the overall performance of the network, thereby bolstering the risk resilience of the air and high-speed rail composite express network holistically. The aforementioned discoveries offer novel perspectives for aviation enterprises and railway departments in their decision-making regarding air-rail intermodal strategies, as well as for the development of all-encompassing transportation network planning. [ABSTRACT FROM AUTHOR]

Traditional buildings will lead to a series of problems such as high energy consumption, high pollution, and serious waste of resources and environmental damage. It is of great significance to construct a green building evaluation system based on the physical building framework to achieve energy saving and consumption reduction and to fit the sustainable development. In this study, a building greenness evaluation system combining BIM data model and prior knowledge of experts is constructed and the applicability of the method has been verified. IFC information is expressed and transmitted in terms of components, materials, equipment, environmental quality impact, etc., and BIM based building performance design data information conversion and greenness evaluation process is constructed; Based on the comparison and summary of the existing green building evaluation systems at home and abroad, the evaluation model of building greenness is determined; Based on the ideas and steps of establishing the index system proposed in the research, the index system of building greenness evaluation is established from the aspects of environmental quality (Q) and building load (L) on the basis of questionnaire, prior knowledge of experts and other methods; Combined with the mathematical model of greenness evaluation, the paper analyzes the indexes of different levels, determines the structural calculation methods of different indexes, and carries on the quantitative classification of the overall greenness evaluation. Finally, taking a citizen's house as an engineering case, the paper makes an empirical analysis on the greenness evaluation of green buildings. The results show that the green degree of the building is excellent(class A), which is consistent with the project positioning, and provides the basis for the efficient and accurate evaluation of green building. [ABSTRACT FROM AUTHOR]

The recycling of retired new energy vehicle power batteries produces economic benefits and promotes the sustainable development of environment and society. However, few attentions have been paid to the design and optimization of sustainable reverse logistics network for the recycling of retired power batteries. To this end, we develop a six-level sustainable dynamic reverse logistics network model from the perspectives of economy, environment, and society. We solve the multi-objective combinatorial optimization model to explore the layout of the sustainable reverse logistics network for retired new energy vehicle power batteries recycling. A case study is implemented to verify the effectiveness of the proposed model. The results show that (a) the facility nodes near the front of the network fluctuate more by opening and closing; (b) the dynamic reverse logistics network is superior to its static counterpart; and (c) cooperation cost changes affect the transaction volume between third-party and cooperative enterprises and total network cost., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)