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.

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.