Search Results (5)

Desert expressway corridors exposed to strong wind hazards often rely on single high-grade routes, with limited alternatives, high detour costs, and low network redundancy. These constraints make it difficult to maintain traffic operation resilience through route substitution alone. Taking the Hami–Tuyugou section of the G30 Lianhuo Expressway in Xinjiang, China, as a case study, this study investigates the formation and evaluation of traffic operation resilience in a wind-hazard-affected, low-redundancy desert expressway corridor. A hierarchical indicator system was constructed with four first-level, fourteen second-level, and thirty-one third-level indicators. Fuzzy DEMATEL(Decision Making Trial and Evaluation Laboratory)–ISM(Interpretive Structural Modeling) was used to identify causal relationships and hierarchical transmission paths; fuzzy DANP(DEMATEL-based Analytic Network Process)–AHP(Analytic Hierarchy Process) was applied to determine indicator weights; and a cloud model was employed to evaluate the overall resilience level. The results show that institutional adaptability, organizational learning, monitoring and information support, and multi-actor collaboration are the main upstream drivers. The corridor was evaluated as Grade IV, indicating a relatively high resilience level approaching Grade V. Sensitivity analyses confirm the robustness of the substantive conclusion. The findings suggest that, under low-redundancy conditions, resilience depends less on structural redundancy and more on adaptive governance, information support, and coordinated response. Full article

Against the backdrop of digital transformation, this study constructs an analytical framework for the influence mechanism of the data-driven dynamic capabilities of foreign trade SMEs from the perspective of digital intelligence immunity, aiming to clarify the complex relationships among influencing factors and multi-combination paths for capability improvement. The research employs the fuzzy AHP-DEMATEL method to quantify the complex influence relationships among factors and uses fsQCA to analyze the configuration paths of high-level data-driven dynamic capabilities. Results show that digital intelligence management and analysis, digital intelligence supervision and early warning, and digital intelligence ecosystem are key drivers of data-driven dynamic capabilities, with digital intelligence talents serving as a guarantee and digital foundation as a foundation. The study identifies the following two core paths for forming high-level capabilities: “management–talent–ecology collaboration” and “early warning–technology–mechanism enhancement.” It concludes that foreign trade SMEs should strengthen digital intelligence management and ecological construction, improve early warning mechanisms, and adopt multi-pronged approaches to build data-driven dynamic capabilities, providing a theoretical basis for their digital transformation and capability upgrading. Full article

Road freight e-commerce platforms, as a specialized form of e-commerce in the road transportation sector, face complex operational risks due to their unique service positioning and business models. This study employs a comprehensive methodology to examine the risk framework of these platforms. Through the development of three distinct questionnaires, 20 critical risk factors are identified, which were subsequently analyzed using a combination of Decision-making Trial and Evaluation Laboratory (DEMATEL) and Interpretive Structural Modeling (ISM) approaches. Furthermore, a systematic risk assessment is conducted by integrating the Analytic Hierarchy Process (AHP) with Fuzzy Comprehensive Evaluation (FCE) methods. The research reveals that the 20 core risk factors affecting road freight e-commerce platforms exhibit a systematic and hierarchical structure, with clearly defined transmission pathways between different levels of risk factors. This investigation uncovers the internal interaction mechanisms among these risk factors. The study demonstrates that a thorough consideration of risk factor mechanisms, coupled with a systematic risk assessment, can significantly enhance the effectiveness and precision of risk control strategy formulation. This methodological approach not only provides a robust framework for understanding the complex risk landscape of online freight platforms but also offers practical insights for developing targeted risk mitigation strategies in this evolving sector. Full article

Solid waste management is a crucial task for municipalities in disposing of city waste. Overcoming socioeconomic obstacles in finding appropriate landfill sites involves a multifunctional team using a process that includes selecting criteria and alternatives. In this study, the FUZZY Analytical Hierarchy Process (AHP) and FUZZY TOPSIS were used to rank five landfill alternatives based on seven criteria. Additionally, Interpretive Structural Modeling (ISM) was employed to establish hierarchical relationships between criteria. MICMAC analysis identified dominant and dependent factors. The study found that Land Capacity carries the highest weight, and the Central Landfill site is the most suitable location. Land Capacity is the dominant factor, while land surface temperature has minimal impact. Roads and communication networks have the highest driving power. The project’s findings can guide the selection of landfill sites and contribute to the development of new sites based on the criteria discussed and their relationships. Full article

The existing research and practices have shown that the coordinated implementation of lean-green manufacturing can have a positive impact on the economic and environmental benefits, which is an effective means to ensure the environmental protection of the production process of manufacturing without damaging their profitability. Within the field of lean-green research, there is still a lack of research to analyze the driving factors for the collaborative implementation of integrated lean and green integration. Although, some scholars and researchers have studied lean and green integration paradigms, their research has mostly focused on lean-green integration practices and their impact on environmental performance and their respective operations. In the context of Industry 4.0, this article investigates the driving forces behind the collaborative integration implementation of a lean-green manufacturing system from the viewpoint of stakeholders. Specifically addressing the issues of correlation and ambiguity in the identification of driving factors, this manuscript proposes an Interpretation Structure Model (ISM) of fuzzy comprehensive Analytic Hierarchy Process (AHP), based on Decision-Making Trial and Evaluation Laboratory (DEMATEL), to determine the importance of the driving factors. Combined with the complex network theory, the evaluation index system is divided into four levels from eight factor categories, including endogenous lean-green driving factors and exogenous driving factors. The fuzzy AHP-DEMATEL-ISM is used to analyze the relationship between indicators and the structure of the indicator system. The complex network which is composed of the indicator system is divided into different levels. The importance of indicators is analyzed from the perspective of the global network, and key factors affecting the driving of lean-green system is analyzed. The integration of the lean-green manufacturing system and organizational synergy are promoted to jointly lead the enterprise toward sustainable development by paying particular attention to the primary impact indicators and aggressively cultivating the key impact indicators. Full article