For businesses, the effective management of cold supply chains is critical to minimizing food losses and ensuring customer satisfaction. Identifying and prioritizing the obstacles that disrupt these processes is therefore a strategic necessity. However, existing literature largely addresses cold supply chain challenges in a fragmented manner, lacking systematic prioritization frameworks that account for the inherent uncertainty and subjective judgments present in real-world operations. To address this deficiency, this study proposes a structured decision framework based on the q-Rung Orthopair Fuzzy (q-ROF) Subjective Weighting Approach. This method effectively captures uncertainty and integrates expert evaluations to determine the relative importance of key cold chain barriers. Through an empirical application involving logistics managers, the framework ranks the identified obstacles to support operational and strategic decision-making. The findings reveal that Time Constraint is the most critical obstacle, directly impacting operational efficiency and customer satisfaction. In contrast, Temperature-Controlled Vehicle Cost is identified as a lower-priority factor in strategic resource allocation. These results offer a clear prioritization scheme that enables managers to focus resources on the most impactful areas, enhancing resilience and efficiency in cold chain operations. This study contributes a robust, uncertainty-aware methodology for barrier prioritization, providing actionable insights for supply chain practitioners and establishing a foundation for future research in cold chain management.
Steel surface defect detection is a critical task in intelligent manufacturing, where high accuracy and real-time performance are required for reliable quality inspection. However, existing deep learning-based approaches often rely on complex architectures, leading to increased computational burden and limited applicability in industrial environments with constrained resources. To address these challenges, a lightweight detection framework is developed to improve feature representation while maintaining computational efficiency. The proposed method integrates adaptive sampling with attention-guided feature refinement to enhance multi-scale feature extraction and contextual representation. In addition, an improved regression strategy is introduced to achieve more stable localization for irregular and low-contrast defects. The network structure is further optimized through lightweight design to reduce redundant parameters and support efficient inference. Experimental results on the Northeastern University surface defect detection (NEU-DET) dataset demonstrate that the proposed approach achieves improved detection accuracy with reduced model size and computational cost compared with baseline models. The results indicate that the method provides a practical solution for real-time industrial inspection, offering a balance between accuracy and efficiency in steel surface defect detection.
Intelligent warehousing has become a key component of Industry 4.0-driven logistics systems, where the coordination of autonomous robots directly affects operational efficiency and system responsiveness. This study addresses the joint optimization of task allocation and path planning for warehouse robots in e-commerce fulfillment environments. A grid-based model is first established to represent the warehouse space, and the scheduling objective is formulated to minimize total travel distance while maintaining balanced workload distribution. An improved genetic algorithm is developed for task allocation, incorporating a multi-layer encoding scheme to represent complex task relationships, along with a simulated annealing mechanism to improve solution quality and prevent premature convergence. For path planning, an enhanced A* algorithm is proposed by introducing a turning cost term into the evaluation function, which effectively reduces unnecessary directional changes and improves path smoothness. Simulation results show that the proposed method significantly outperforms conventional approaches, achieving faster convergence and notable reductions in both travel distance and turning frequency. Specifically, the convergence speed is improved by over 70%, while the total travel distance and the number of turning maneuvers are reduced by approximately 48% and 78%, respectively. The proposed framework enables coordinated decision-making for multi-robot systems and provides a scalable and practically applicable solution for intelligent scheduling in smart logistics environments.
This paper addressed the computational challenges of solving large-scale fuzzy 0–1 Integer Linear Programming (ILP) problems by proposing a Fuzzy-Hybrid Quantum-Classical Optimization Algorithm (F-HQCOA). The novel approach employed $\alpha$-cut based transformations to convert fuzzy ILP models into a family of crisp Quadratic Unconstrained Binary Optimization (QUBO) problems, which were then solved using hybrid quantum-classical techniques. Unlike multi-objective formulations, the proposed framework performed a parametric analysis over $\alpha$-levels to generate a spectrum of solutions that reflected trade-offs between objective optimality and feasibility satisfaction under uncertainty. The degree of satisfaction ($\mu$) was adopted as an evaluation metric to assess solution quality rather than as an independent optimization objective. The method was evaluated on fuzzy Multi-Dimensional Knapsack and Set Covering problems. Results showed that the proposed approach could efficiently handle larger instances and provided a diverse set of solutions across $\alpha$-levels. While quantum-assisted methods demonstrated reduced Time-to-Solution (TTS) in certain settings, comparisons were presented with careful consideration of differences in computational paradigms. The proposed framework offers a practical decision-support tool for optimization under uncertainty and establishes a foundation for future extensions toward true multi-objective fuzzy quantum optimization.
The vibrations are part of people’s everyday lives. In mechanical engineering, vibrations are considered an undesirable phenomenon that can have a detrimental effect on machine structures. Vibrations cause fatigue and wear. They are often responsible for the failure that can occur in a machine. Experimental research was conducted, familiarization with measuring equipment and a model of a rotating machine, and identification of characteristic frequencies of rolling bearings in the vibration signal. Timely identification of localized damage, responsible for over 90% of all bearing defects, is crucial to prevent catastrophic downtime. This paper presents an experimental and analytical vibration study conducted on a laboratory model featuring a 6003 2ZR bearing. The presented methodology is simple, practical, and suitable for implementation in industrial condition monitoring systems, providing a reliable basis for preventive and predictive maintenance of rotating machinery.
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