In the context of urban distribution, given the complexity of express delivery and the variability of distribution conditions, vehicle routing problems with time-dependent characteristics have received increasing attention. This study incorporates a cross-period travel time estimation method for road segments that accounts for temporal and weather-dependent variations in vehicle speed. Building upon this foundation, this study establishes an multi-objective optimization model for the green vehicle routing problem that systematically incorporates intricate constraints, including time-varing vehicle speed, fuel consumption, carbon emissions, and customer servive time windows. This model aims to achieve three primary objectives: (1) minimizing the fleet size, (2) minimizing the overall delivery expenses, which include fuel consumption and carbon emissions, and (3) maximizing the average customer satisfaction. To solve this model, we develop an improved Non-Dominated Sorting Genetic Algorithm III (INSGA-III). To effectively prevent the algorithm from becoming trapped in local optima, we propose a dual-criteria selection mechanism. Meanwhile, we introduce a destroy-and-repair variable neighborhood search strategy to enhance the algorithm’s optimization capability under complex constraints. Experimental evaluations conducted on Solomon benchmark instances as well as real-world case studies indicate that the proposed INSGA-III algorithm surpasses widely utilized multi-objective optimization methods across all assessed performance metrics. This highlights the significant potential of the presented INSGA-III algorithm for practical applications in urban delivery scenarios, which is closely linked to the sustainable development of cities.

This paper presents a modular and distributed supervisory control integration framework for intelligent micro-manufacturing systems (MMSs) under event-level failures. Addressing the increasing demand for scalable and reliable supervisory control in both micro- and smart manufacturing, the proposed approach equips each subsystem with a detector automaton that classifies runtime states into Strictly robust, Recoverably robust, or Non-robust categories. Distributed supervisors then make real-time local decisions to ensure fault-tolerant evolution of system behaviors. Unlike conventional centralized or Petri net-based methods, the proposed automaton-based framework supports modular design and structural scalability. Quantitative comparisons show that the robustness-detection cost scales approximately linearly with the summed sizes of local graphs, indicating good structural scalability. Simulation studies validate the feasibility and scalability of the framework, demonstrating its effectiveness in maintaining production cycle reachability and its integration potential for micro-electro-mechanical systems (MEMS)-based production lines, micro-fabrication platforms, and smart factory environments. These results confirm that the proposed method can serve as a robust and deployable control layer for next-generation intelligent and micro-manufacturing integration architectures.