Search Results (307)

Multi-axle vehicles are commonly used for heavy-duty special operations, which easily leads to high driving torque demands when adopting distributed electric drive configurations. This study achieves the objective of reducing the driving torque of each in-wheel motor while controlling the stability of multi-axle vehicles. Taking a five-axle distributed drive test vehicle as the research object, a hierarchical control strategy integrating active all-wheel steering and direct yaw moment control is proposed. The upper layer is implemented based on model predictive control, with fuzzy control introduced to dynamically adjust control weights; the lower layer accomplishes the allocation of targets calculated by the upper layer through minimizing the objective function of tire load ratio. A linear parameter varying (LPV) tire model is introduced into the vehicle model to improve the calculation accuracy of tire lateral forces, and a neural network method is employed to solve the real-time performance issue of the model predictive control (MPC) controller. The proposed strategy is verified through a combination of simulation and real vehicle tests. High-speed condition simulations demonstrate that the AWS/DYC strategy significantly outperforms the ARS/DYC approach: compared to the active rear-wheel steering strategy, while the sideslip angle is reduced by 90.98%, the peak driving torque is reduced by 30.78%. Notably, tire slip angle analysis reveals that AWS/DYC maintains relatively uniform slip angle distribution across axles with a maximum of 4.7°, entirely within the linear working region, optimally balancing tire performance utilization with lateral stability while preserving safety margin, whereas ARS/DYC causes slip angles to exceed 11.9° at the rear axle, entering saturation. Low-speed real vehicle tests further confirm the engineering applicability of the strategy. The proposed method is of significant importance for the application of distributed drive configurations in the field of special vehicles. Full article

With the growing emphasis on holistic management throughout the entire product lifecycle, multi-stage production models that integrate distributed manufacturing, transportation, and assembly processes have gradually attracted research attention. However, studies in this area remain relatively scarce. This paper addresses the fuzzy distributed assembly hybrid flow shop scheduling problem (FDAHFSP), comprehensively considering the entire production flow from manufacturing and transportation to final assembly. A mathematical model is first established with the objectives of minimizing the fuzzy total weighted earliness/tardiness and the fuzzy total energy consumption. To effectively solve this problem, a Q-learning-based adaptive NSGA-II (Q-ANSGA) is proposed. The algorithm incorporates a hybrid strategy combining multiple rules to enhance the quality of the initial population. Additionally, a Q-learning-based adaptive parameter adjustment mechanism is designed to dynamically optimize genetic algorithm parameters, thereby improving the algorithm’s search efficiency and convergence performance. Furthermore, eight neighborhood search operators are developed, and an iterative greedy strategy is integrated to guide the local search process. Finally, comprehensive experiments on 45 test instances are conducted to evaluate the effectiveness of each improvement component and the overall performance of Q-ANSGA. Experimental results demonstrate that the proposed algorithm achieves superior performance in solving the FDAHFSP due to its systematic enhancements. Full article

The large-scale integration of electric vehicles (EVs) presents both challenges and opportunities for power grid stability and renewable energy utilization. Vehicle-to-Grid (V2G) technology enables EVs to serve as mobile energy storage units, facilitating peak shaving and valley filling while promoting the local consumption of photovoltaic and wind power. However, uncertainties in renewable energy generation and EV arrivals complicate the scheduling of bidirectional charging in stations equipped with hybrid energy storage systems. To address this, this paper proposes a multi-stage rolling optimization framework combined with a fuzzy logic-based decision-making method. First, a bidirectional charging scheduling model is established with the objectives of maximizing station revenue and minimizing load fluctuation. Then, an EV charging potential assessment system is designed, evaluating both maximum discharge capacity and charging flexibility. A fuzzy controller is developed to allocate EVs to unidirectional or bidirectional chargers by considering real-time predictions of vehicle arrivals and renewable energy generation. Simulation experiments demonstrate that the proposed method consistently outperforms a greedy scheduling baseline. In large-scale scenarios, it achieves an increase in station revenue, elevates the regional renewable energy consumption rate, and provides an additional equivalent peak-shaving capacity. The proposed approach can effectively coordinate heterogeneous resources under uncertainty, providing a viable scheduling solution for EV-aggregated participation in grid services and enhanced renewable energy integration. Full article

Background: Cold chain distribution in Agri-Biotech supply chains faces serious challenges due to strict time windows, high temperature sensitivity, and conflict between different operational objectives, and conventional static approaches are unable to address these complexities. Methods: In this study, an integrated decision support framework is presented that combines multi-objective fuzzy modeling and an adaptive digital twin to simultaneously manage logistics costs, product quality degradation, and service time compliance under operational uncertainty. Key uncertain parameters are modeled using triangular fuzzy numbers, and the digital twin dynamically updates the decision parameters based on operational information. The proposed framework is evaluated using real industrial data and comprehensive computational experiments. Results: The results show that the proposed approach is able to produce stable and balanced solutions, provides near-optimal performance in benchmark cases, and is highly robust to demand fluctuations and temperature deviations. Digital twin activation significantly improves the convergence behavior and stability of the solutions. Conclusions: The proposed framework provides a reliable and practical tool for adaptive planning of cold chain distribution in Agri-Biotech industries and effectively reduces the gap between advanced optimization models and real-world operational requirements. Full article

With the rapid development of urban rail transit, train operation control is required to meet increasingly stringent demands in terms of energy consumption, comfort, punctuality, and precise stopping. The optimization and tracking control of speed profiles are two critical issues in ensuring the performance of automatic train operation systems. However, conventional model predictive control (MPC) methods are highly dependent on parameter settings and show limited adaptability, while heuristic optimization approaches such as the whale optimization algorithm (WOA) often suffer from premature convergence and insufficient robustness. To overcome these limitations, this study proposes an optimized model predictive controller using the multi-objective whale optimization algorithm (MPC-MOWOA) for urban rail train tracking control. In the improved optimization algorithm, a nonlinear convergence mechanism and the Tchebycheff decomposition method are introduced to enhance convergence accuracy and population diversity, which enables effective optimization of the initial parameters of the MPC. During real-time operation, the MPC is further enhanced by integrating a fuzzy satisfaction function that adaptively adjusts the softening factor. In addition, the control coefficients are corrected online according to the speed error and its rate of change, thereby improving adaptability of the control system. Taking the section from Lvshun New Port to Tieshan Town on Dalian Metro Line 12 as the study case, the proposed control algorithm was deployed on a TMS320F28335 embedded processor platform, and hardware-in-the-loop simulation experiments (HILSEs) were conducted under the same simulation environment, a unified train dynamic model, consistent operating conditions, and an identical evaluation index system. The results indicate that, compared with the Fuzzy-PID control method, the proposed control strategy reduces the integral of time-weighted absolute error nearly by 39.6% and decreases energy consumption nearly by 5.9%, while punctuality, stopping accuracy, and comfort are improved nearly by 33.2%, 12.4%, and 7.1%, respectively. These results not only verify the superior performance of the proposed MPC-MOWOA, but also demonstrate its capability for real-time implementation on embedded processors, thereby overcoming the limitations of purely MATLAB-based offline simulations and exhibiting strong potential for practical engineering applications in urban rail transit. Full article

Cross-border e-commerce logistics has long prioritized delivery speed; however, the trade-offs between cost-effectiveness, carbon emissions, risk, and financial performance have received relatively little attention. To address this deficiency, this study constructs a fuzzy nonlinear multi-objective optimization framework that integrates the particle swarm optimization (PSO) algorithm and the Sobol sensitivity analysis to capture the uncertainty and nonlinear dynamics of logistics systems. Using operational data from a Taiwanese cross-border e-commerce exporter from 2023 to 2024, empirical results show that extending the standard 25-day delivery time to an acceptable maximum of 32–37 days (i.e., an extension of 7–12 days) can reduce logistics costs per order by 22–38%, carbon emissions by 18–31%, and increase financial returns. Sobol sensitivity analysis further demonstrates that extended delivery time (T) is a significant controllable factor ($S_1=0.62, {ST}_1=0.75$). This study empirically verifies the profitability and sustainability of moderately T, challenges the current “speed-first” model, and provides a transparent, replicable, and scalable decision-making framework for promoting low-carbon, economically viable cross-border e-commerce supply chains. Full article

This study develops a fuzzy linear multi-objective programming (FLMOP) model to optimize Taiwan’s online food delivery (OFD) systems by jointly considering time, cost, quality, and carbon emissions (TCQCE) under strict Hazard Analysis and Critical Control Point (HACCP) safety constraints. By integrating fuzzy set theory with triangular fuzzy numbers (TFN) and employing centroid defuzzification, this model effectively addresses uncertainties in delivery time, cost, and quality. Empirical results demonstrate that controlled delivery-time extension and order batching reduce carbon emissions by 20%, maintain food quality at 89.3%, and lower delivery costs by 15% under large-scale operations. Statistical validation (p = 0.002) and sensitivity analysis confirm robustness and low variability. Comparative benchmarking highlights FLMOP’s superiority over mixed-integer linear programming (MILP) and genetic algorithms/non-dominated sorting genetic algorithm II (GA/NSGA-II), achieving higher hypervolume (0.904 vs. 0.836 and 0.743) and near-optimal solutions within 11 s, making it suitable for real-time decision-making. This study establishes a benchmark for sustainable last-mile OFD and offers practical guidelines for Taiwan’s OFD platforms. Full article

Considering the complexity and hazardous nature of construction jobsites, selecting the effective safety risk control strategies is crucial to prevent accidents, protect labor crews, and achieve project objectives related to cost, schedule, and quality in the construction project. However, the evaluation of different safety strategies involves multiple conflicting criteria and uncertain expert judgments, making it a complex multi-criteria decision-making (MCDM) problem. To address this problem, this study develops a fuzzy-integrated MCDM framework that combines two methods: Fuzzy Analytic Hierarchy Process (FAHP), which systematically captures the relative importance of safety criteria under uncertainty, and ELECTRE III, which ranks alternative strategies by modeling preferences and veto conditions, reflecting real-world “non-compensatory” safety logic. FAHP determines criterion weights based on expert judgments, while ELECTRE III evaluates and ranks alternative safety strategies. The framework is validated through a piping construction case study, where it successfully identified the optimal safety plan. A sensitivity analysis is conducted to confirm the robustness of results, and comparative tests with other MCDM methods further support its reliability. Therefore, the proposed fuzzy-integrated framework offers an effective approach for evaluating safety risk control strategies, enhancing both safety and overall project performance, and advancing systematic safety management in the construction industry. Full article

The Agricultural Park Integrated Energy System (APIES) is a key platform for integrating distributed renewable energy (DRE) with agricultural production. However, its economic operation and the stability of crop growth environments are severely challenged by bidirectional uncertainties from external meteorology. These include the inherent variability of wind-solar generation and critical agricultural loads, such as supplementary lighting and temperature control, a challenge that existing models with static environmental parameters fail to address. To solve this, a bi-level optimization scheduling model for APIES considering meteorological uncertainty is proposed. The upper layer minimizes operation costs by quantifying uncertainties via triangular fuzzy chance constraints, with core constraints on DRE output, energy storage charging-discharging, and load shifting, solved by YALMIP-Gurobi linear programming. The lower layer maximizes crop growth environment satisfaction using a dynamic weight adaptive mechanism and NSGA-II multi-objective algorithm. The two layers iterate alternately for coordination. Using a small agricultural park in Xinjiang, China, as a case study, the results indicate that the proposed two-layer optimal scheduling model reduces costs by 10.8% compared to the traditional single-layer optimization model, and improves environmental satisfaction by 4.3% compared to the fixed-weight two-layer optimization model. Full article

To achieve a scientific evaluation of land ecological resilience in mining areas and promote the green transformation and sustainable development of the mining industry, this study is based on the core concept of Nature-based Solutions (NbS), coupling the “Driving force–Pressure–State–Impact–Response” (DPSIR) framework, and constructs an evaluation system for mine land ecological resilience (MLER) focusing on sustainability. This system covers multiple aspects, including natural ecology, socio-economics, and policy management, comprising 21 secondary indicators that comprehensively respond to NbS’ fundamental principles of “nature-guided, multi-party collaboration, and long-term adaptation.” In terms of evaluation methodology, this study proposes a combined weighting model that integrates AHP-CRITIC game theory with Vague sets. First, subjective expert experience and objective data variance are balanced through combined weighting. Based on game theory, the optimal combination coefficients were determined (α₁ = 0.624, α₂ = 0.376) to reconcile subjective and objective preferences. Subsequently, the three-dimensional interval structure of Vague sets is utilized to effectively accommodate fuzzy information and data gaps. By characterizing the restoration process through interval membership, the model enhances the representational capacity of the evaluation results regarding complex ecological information. Empirical research conducted in the mining areas of Gan Xian, Xing Guo, Yu Du, and Xun Wu in Jiangxi Province effectively identified differences in resilience levels: the resilience of the Xing Guo mining area was classified as I, Gan Xian and Yu Du as II, and Xun Wu as IV. These results are fundamentally consistent with the AHP-Fuzzy Comprehensive Evaluation method, verifying the robustness and reliability of the model. The NbS-guided evaluation system and model constructed in this study provide scientific tools for identifying differences in the sustainability of MLER and key constraints, promoting the transformation of restoration models from “engineering-driven” to “nature-driven, long-term adaptation” in the context of NbS in China. Full article

Switched reluctance motors (SRMs) are promising for applications such as air compressors due to their robust structure and fault tolerance, but suffer from high torque ripple and radial electromagnetic forces that cause vibration and noise. This paper proposes a game-theoretic multi-objective design optimization framework to enhance electromagnetic performance by simultaneously maximizing average torque and minimizing radial force. The optimization problem is transformed into a game model where objectives are treated as players with strategy spaces derived through fuzzy clustering and correlation analysis. Particle swarm optimization (PSO) is employed to solve the payoff functions under both novel cooperative and non-cooperative game scenarios of SRMs’ structural design. Finite element analysis (FEA) validates the optimized motor topology, showing that the cooperative game model achieves a balanced performance with high torque density and reduced vibration, meeting the requirements for air compressor drives. The proposed method effectively resolves the weight selection challenge in traditional multi-objective optimization and demonstrates strong engineering feasibility. Full article

Regional disparities pose persistent challenges for balanced and sustainable development in Türkiye, where provinces exhibit prominently heterogeneous socioeconomic structures, capacities, and investment needs. This study proposes an integrated, data-driven framework for allocating public investments across provinces by jointly addressing development efficiency and spatial equity. A dataset of 109 indicators for 81 provinces was compiled and standardized, and Principal Component Analysis, followed by multiple clustering algorithms (K-Means, Gaussian Mixture Model, Fuzzy C-Means), was used to derive robust provincial development profiles. National policy priorities were quantified through a document-based assessment of the 12th Development Plan (2024–2028), enabling the construction of nine strategic investment categories aligned with national objectives. These components were incorporated into a multi-objective optimization model formulated using the ε-constraint method, where total utility is maximized subject to an adjustable equity constraint based on a Gini-like parameter. Results reveal a clear efficiency–equity trade-off: low inequality tolerance yields uniform but low-return allocations, whereas relaxed equity constraints amplify concentration in high-capacity metropolitan provinces. Intermediate equity levels (G = 0.3–0.5) generate the most balanced outcomes, supporting both development potential and spatial cohesion. The proposed framework offers a transparent, reproducible decision support tool for more equitable and strategy-aligned public investment planning in Türkiye. Full article

Accurate fault risk assessment is essential for maintaining wind turbine reliability. Traditional failure modes and effects analysis (FMEA)-based approaches struggle to handle the fuzziness, uncertainty, and conflicting nature of multi-criteria evaluations, which may lead to delayed fault detection and increased maintenance risks. To address these limitations, this paper proposes an enhanced risk assessment framework that integrates subjective-objective weighting and fuzzy decision-making. First, a combined subjective–objective weighting (CSOW) model with adaptive fusion is developed by integrating the analytic hierarchy process (AHP) and the entropy weight method (EWM). The CSOW model optimizes the weighting of severity (S), occurrence (O), and detection (D) indicators by balancing expert knowledge and data-driven information. Second, a fuzzy decision-making model based on interval-valued intuitionistic fuzzy numbers and VIKOR (IVIFN-VIKOR) is established to represent expert evaluations and determine risk rankings. Notably, the overlap rate between the top 10 failure modes identified by the proposed method and a fault-tree-based Monte Carlo simulation incorporating mean time between failures (MTBF) and mean time to repair (MTTR) reaches 90%, substantially higher than other methods. This confirms the superior performance of the framework and provides enterprises with a systematic approach for risk assessment and maintenance planning. Full article

The Ulansuhai Basin stands as the most crucial ecological and economic zone in northern China. Resource and environmental planning serves as a core strategy, aimed at mitigating the consumption of environmental resources induced by economic expansion within the Ulansuhai Basin and facilitating the synergistic development of the economy and the environment. In this paper, by taking the data of the economy, resource and water environment of the Ulansuhai Basin during the period from 2010 to 2022 as the research basis, a fuzzy multi-objective programming model for the resource–environment and socio-economic system was constructed. The results showed that within the planting industry, giving priority to the cultivation of sunflowers and corn will enable the model results to remain in an optimal state. In the field of animal husbandry, the quantity ratio of cows to pigs should be maintained at 1.5:1, and the quantity ratio of sheep to cows should be controlled at approximately 20:1; these ratio settings were conducive to ensuring the model remains in an optimal state. When the ratio of planting industry to animal husbandry was set at 13.16:1 (with the unit of “head” for livestock quantity and “hm²” for planting area), the model arrived at the optimal solution. This study, by virtue of its analysis of the coordination mechanism of economic development with environmental protection in typical watersheds, can provide meaningful policy references for realizing the synergistic enhancement of ecological quality and economic benefits in arid and semi-arid basins, fragile ecological carrying capacity, and the balance between agricultural production expansion and environmental pollution control in these regions. Full article

Unmanned Surface Vehicles (USVs) are increasingly employed in maritime operations requiring high levels of autonomy, safety, and energy efficiency. However, traditional path planning techniques struggle to simultaneously address multiple conflicting objectives such as fuel consumption, trajectory smoothness, and obstacle avoidance in dynamic maritime environments. To overcome these limitations, this paper introduces a Deep Q-Learning (DQN) framework and a novel Fuzzy Deep Q-Learning (F-DQN) algorithm that integrates Mamdani-type fuzzy reasoning into the reinforcement-learning (RL) reward model. The key contribution of the proposed approach lies in combining fuzzy inference with deep reinforcement learning (DRL) to achieve adaptive, interpretable, and multi-objective USV navigation—overcoming the fixed-weight reward limitations of existing DRL methods. The study develops a multi-objective reward formulation that jointly considers path deviation, curvature smoothness, and fuel consumption, and evaluates both algorithms in a simulation environment with varying obstacle densities. The results demonstrate that the proposed F-DQN model significantly improves trajectory optimality, convergence stability, and energy efficiency, achieving over 35% reduction in path length and approximately 70–80% lower fuel consumption compared with the baseline DQN, while maintaining comparable success rates. Overall, the findings highlight the effectiveness of fuzzy-augmented reinforcement learning in enabling efficient and interpretable autonomous maritime navigation. Full article