Isomorphic module systems constitute a distributed discrete-event modeling paradigm for large-scale multi-agent and networked architectures. They are composed of modules, instantiated from a given template, coordinated through global events. A key challenge is that even if the template is nonblocking or deadlock-free, the parallel composition of the modules may fail to satisfy these properties for some number of modules. The fundamental problem is to determine the maximum number of modules that can be safely composed while preserving nonblockingness or deadlock-freeness. We establish the computational complexity of the associated decision problems, proving that both problems are PSPACE-complete, and show that the minimal number m of modules that causes the system to become blocking or deadlocked can be found in time O(m2m), which is bounded by O(n2n), where n is the number of states of the template, improving thus the known complexity of these problems.
Manufacturing industries are facing increasing product variability due to the growing demand for personalized products. Under these conditions, ensuring safety becomes challenging as frequent reconfigurations can lead to unintended hazardous behaviors. Multi-agent control architectures have been proposed to improve flexibility through decentralized decision-making and coordination. However, these architectures are based on predefined task models, which limit their ability to adapt task planning to new product requirements while preserving safety. Recently, large language models have been introduced into manufacturing systems to enhance adaptability, but reliability remains a key challenge. To address this issue, we propose a control architecture that leverages the flexibility of large language models while preserving safety on the manufacturing shop floor. Specifically, the proposed framework verifies large language model-enabled task allocations by using temporal logic and discrete event systems. The effectiveness of the proposed framework is demonstrated through a case study that involves a multi-robot assembly scenario, showing that unsafe tasks can be allocated safely before task execution.
This paper proposes algorithms that incrementally compute control sequences driving the marking of timed Petri nets from an initial value to a reference one, avoiding forbidden and dangerous states with a duration that approaches the minimal value. The proposed algorithms are applicable to a large class of discrete event systems in particular in the domain of flexible manufacturing, communication and computer science or transportation and traffic. To overcome the most burdensome part of the computations, only the sequences encoded in a small area of the reachability graph are worked out. The main contributions are to propose an estimation of the minimal duration of the remaining sequences to the reference based on the computation of the firing count vectors and a progressive search of the forbidden and dangerous states according to a backtracking phase. The approach is suitable for deadlock-free scheduling problems
We consider discrete-event systems (DES) consisting of parallel arrays of machines and buffers. The machines are divided into groups in each of which the members have identical structure, i.e. same state set and isomorphic transitions. In these systems, to avoid the underflow or overflow of the buffers, the controller only needs the information of the total numbers of components at each state and the numbers of workpieces in the buffers. By exploiting the identical structure of each group, we extract such control information from the control functions computed by the state tree structures (STS) to generate abstract control functions. Thanks to the symmetry of the system, we show that all controllable events relabeled to the same symbol share an invariant abstract control function, which is independent of the total number of machines, as long as the buffer sizes are fixed. The approach is illustrated by two examples.
In this paper we show how timed Petri nets can be efficiently used to solve problems related to resource planning in intermodal freight transport terminals. In particular, the tackled issues regard the strategic planning of the number of facilities used to transfer the intermodal transport units and the capacity/frequency of the transportation means. A real case study is considered, namely a rail-road terminal located in southern Italy. Monte Carlo simulations based on the timed Petri net model of the terminal are carried out considering various scenarios, including both the regular behavior based on real data, and situations of potential congestion resulting from increase in the commercial flows.
