A few design schemes can be derived using the modularisation strategy, which can effectively reduce the assembling cost and delivery time to the development of product design. Therefore, it is important to select modular architecture design schemes (MADSs) according to evaluation information (fuzzy and uncertain) provided by design experts. As a classic multiple attributes group decision-making problem, the fuzzy theory exhibits a distinct advantage in dealing with the heterogeneous fuzzy information. In this study, an integrated VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) approach was proposed to evaluate MADSs based on a similarity degree model and modified convergent iterative algorithm (MCIA). First, the evaluation measures of MADSs were provided using various types of fuzzy (interval and real) numbers according to the characteristics of the evaluation attributes. Then, the similarity degree between the heterogeneous fuzzy data of individual judgements was used to construct a collective decision matrix, which was obtained by the MCIA. Finally, the weighted VIKOR approach integrated with the Euclidean distance of heterogeneous fuzzy data was presented to derive the compromise solutions of MADSs. A real-world case study on liquid crystal display, method comparisons, and sensitivity analysis demonstrated the effectiveness of the proposed approach for evaluating MADSs under heterogeneous fuzzy environment.

Robotic polishing is crucial for achieving superior surface finishes in manufacturing. However, precise force control presents significant challenges, particularly for curved workpieces exhibiting time-varying stiffness. Traditional methods typically struggle to adapt to these dynamic conditions, often leading to inconsistent results and suboptimal surface quality. This study proposes an Adaptive Impedance Control based on Visual Guidance (AICVG) strategy for robotic polishing. This approach integrates real-time visual feedback for geometric perception and adaptive tool path generation with a fuzzy logic system that dynamically adjusts impedance parameters to account for unforeseen surface stiffness variations. Simulations and experimental validations conducted on a robotic platform demonstrate that the AICVG strategy significantly outperforms both traditional impedance control and conventional fuzzy logic-based adaptive impedance control. Specifically, it maintains force control errors within ±1.5 N under dynamic stiffness conditions and achieves a 60% reduction in workpiece surface roughness compared to the aforementioned alternative methods. This study presents a robust and precise control framework that significantly enhances the adaptability and efficacy of robotic polishing for complex geometries, thereby advancing automated solutions in high-precision manufacturing.

Global container liner shipping networks (GCLSNs) underpin world trade, yet their organization is increasingly reshaped by geopolitical fragmentation. Existing studies often model GCLSNs as single-layer networks, overlooking how carriers’ geopolitical affiliations structure both connectivity and disruption risk. This study constructs a weighted carrier–geopolitical multiplex network in which layers are defined by carriers’ geopolitical affiliations and coupled through shared port calls. Structural analysis reveals pronounced asymmetry in layer size, cohesion, and inter-layer dependence, with overlap concentrated in a limited set of shared hubs. Using the Red Sea crisis as an empirical stress-test scenario, we develop a load–capacity propagation model, incorporating intra-layer load redistribution, rerouting to substitute shared hubs, and inter-layer resource squeeze at same-port layer copies. Results show that direct losses concentrate in corridor-exposed layers, while indirect losses propagate selectively through bridge hubs, especially Singapore, Shanghai, Shenzhen, and Port Klang. Sensitivity analysis indicates nonlinear amplification when low tolerance, strong inter-layer squeeze, and elevated rerouting pressure coincide. These findings show that multiplexity does not imply resilience by itself; cross-layer connectivity buffers disruption only when spare capacity is distributed but amplifies vulnerability when it converges on a narrow set of shared hubs. The paper contributes a carrier–geopolitical perspective to shipping network analysis and a dynamic framework for studying disruption propagation in complex logistics systems.

Modularization fails to adequately meet the diverse customer requirements and the product operational data for complex equipment of port shipping (CEPS). To address this challenge, we propose a module configuration design approach (MCDA) that incorporates module parameter planning (MPP) and service module customization (SMC). Initially, the design ranges and weights of functional requirements are established using fuzzy information derived from customer requirements, facilitated by fuzzy quality function deployment. Subsequently, a multi-objective model of MPP is developed, incorporating the cost utility, information content, and delivery time of module and product based on a probabilistic assessment of module instances from operational data. The non-dominated sorting genetic algorithm II (NSGA-II) is employed to derive the solution set for MPP. The personalized configuration of the Pareto solution set for SMC is derived based on each objective function pair. Finally, we illustrate the effectiveness of the proposed approach through a case study involving a wheel loader and method comparison.