Yan Yang

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.

To address the challenge of poor separation performance exhibited by conventional magnetic separation equipment when processing coarse-grained, low-grade magnetite ore, this paper proposes a novel ore recognition method that integrates empirical mode decomposition (EMD) with a convolutional neural network (CNN). First, the original signal undergoes standardization to suppress sensor baseline drift. Then, it is decomposed by using EMD to obtain a series of intrinsic mode functions (IMFs). Subsequently, based on scaling exponents and kurtosis values, IMFs containing significant feature information are selected and fused, resulting in a reconstructed signal with substantially reduced noise. To preserve effective features, the absolute values of the reconstructed signal are taken, followed by normalization and dimensional transformation to convert it into a two-dimensional matrix format, thereby constructing training, validation, and test sets. Finally, a CNN is designed and optimized to automatically extract discriminative features from the preprocessed samples, enabling accurate classification of magnetite ore grades. Experimental results demonstrate that the proposed comprehensive identification method achieves effective and stable classification performance across different ore grades. Specifically, the implementation of standardization and EMD-based denoising has been demonstrated to enhance the accuracy of CNNs in recognizing diverse ores.

To address the challenge of poor separation performance exhibited by conventional magnetic separation equipment when processing coarse-grained, low-grade magnetite ore, this paper proposes a novel ore recognition method that integrates empirical mode decomposition (EMD) with a convolutional neural network (CNN). First, the normalized magnetic induction intensity signals are decomposed using EMD to yield a series of intrinsic mode functions (IMFs). IMFs containing prominent characteristic information are then selected and fused based on their dominant frequency and kurtosis values, resulting in a reconstructed signal with significantly reduced noise. Subsequently, the reconstructed signals undergo inversion, re-normalization, and dimensional transformation into a two-dimensional matrix format to construct the training and testing sample datasets. A convolutional neural network is then designed and optimized to automatically extract discriminative features from these preprocessed samples, enabling accurate classification of magnetite ore grades. Experimental results demonstrate that the proposed EMD-CNN framework achieves effective and stable classification performance across different ore grades. In particular, the application of EMD for noise component removal substantially enhances the CNN’s recognition accuracy for waste rock and medium-grade ore, which are traditionally the most difficult categories to distinguish.

We address the collaborative path planning problem for multi-agent systems with heterogeneous capabilities, subject to uncertainty and operating under complex task specifications. Conventional Probabilistic Signal Temporal Logic (PrSTL) frameworks exhibit significant limitations in describing multi-agent collaborative tasks with temporally cumulative properties. To address this challenge, we extend the PrSTL framework by introducing a Temporal Collective Counting Operator to characterize such spatio-temporal specifications. We then formulate the multi-agent collaborative planning problem under dynamics uncertainty as a Mixed-Integer Second-Order Cone Program. This formulation leverages PrSTL to specify tasks with cumulative temporal properties, while employing Polynomial Chaos Expansion to propagate uncertainty. Finally, we propose a constraint relaxation mechanism to address the conservatism introduced by formula transformations and probabilistic constraints' approximation.

Deadlocks are highly undesirable phenomenons in automated manufacturing systems (AMSs). Although there exist a number of deadlock control polices, resources are assumed to be not failed. In this paper, an adaptive deadlock control policy is proposed for AMSs with unreliable resources based on Petri nets (PNs). Each processing stage in the modeled AMSs is permitted to obtain the different types of multiple quantity of resources. By using a set of linear programming formulas, a deadlock marking is first detected. At the deadlock marking, critical transitions, resources, and places are found to design an inequality constraint which produces a control place to prevent the deadlock from occurring. By exploring the relation between the inequality constraint and unreliable resources, an adaptive controller is developed to dynamically adjust the working modes between a normal mode and a robust mode. Finally, we synthesize an adaptive deadlock supervisor for AMSs with unreliable resources, which ensures the controlled system runs smoothly even if the failures of unreliable resources appear. Comparative discussions are presented to expound our proposed method's effectiveness and superiority.

While a deadlock control problem in complex resource allocation systems (RASs) has been extensively studied in the literature, the corresponding results that are applicable to assembly systems are quite limited, both, in terms of structural analysis of deadlocks and deadlock resolution. Taking Petri nets (PNs) as a modeling and analysis tool, this article focuses on the deadlock control problem for a forward-conflict free net (FCFN), which allows for batch assembly and multiple resource allocations. First, a new structural characterization of deadlocks in FCFN is proposed through two structural objects: 1) circuit and 2) $\omega$ -structure. The starting point for this is motivated by the fact that deadlocks in assembly systems stem not only from the circular wait of resources but also the parts waiting for their assembly with other parts. Subsequently, based on these two objects, a necessary and sufficient condition about FCFNs liveness is obtained: 1) an FCFN is live if and only if no circuit and 2) $\omega$ -structure are saturated at any reachable marking. Finally, in order to prevent each such object from inducing deadlocks, a hierarchical search algorithm appropriate for real-time implementation is developed to avoid its saturation. The proposed algorithm is proven to be capable of ensuring the deadlock-free operation of FCFNs. Moreover, several examples are provided to demonstrate its effectiveness.

Deadlock problem, a research area full of vitality, has been extensively investigated in the framework of Petri nets. Much of the work on deadlock problem mainly focuses on the models with flexible routes, while there are few theoretical results from the models with assembly operations, especially the models with hierarchical assembly operations. In this paper, an innovative distributed control method for assembly systems with complex structures is developed. Before advancing to the next step, a forward search procedure is conducted on the concerned process to check whether there exists a firing sequence from its current place to its nearest global critical place. The movement of its next step is allowed when the search result is positive; otherwise, our method prohibits its one-step movement and the same search method is imposed on the remaining processes until a positive result is returned. During the execution of our control method, because of no demanding of all the states and communication with other unchecked processes, a great reduction of computational complexity can be achieved with our on-line and real-time control strategy.