Deadlock control approaches based on Petri nets are usually implemented by adding control places and related arcs to the Petri net model of a system. The main disadvantage of the existing policies is that many control places and associated arcs are added to the initially constructed Petri net model, which significantly increases the complexity of the supervisor of the Petri net model. The objective of this study is to develop a two-step robust deadlock control approach. In the first step, we use a method of deadlock prevention based on strict minimal siphons (SMSs) to create a controlled Petri net model. In the second step, all control places obtained in the first step are merged into a single control place based on the colored Petri net to mark all SMSs. Finally, we compare the proposed method with the existing methods from the literature.

The invention of reconfigurable manufacturing systems (RMSs) has created a challenging problem: how to quickly and effectively modify an RMS to address dynamic changes in a manufacturing system, such as processing failures and rework, machine breakdowns, addition of new machines, addition of new products, removal of old machines, and changes in processing routes induced by the competitive global market. This paper proposes a new model, the intelligent colored token Petri net (ICTPN), to simulate dynamic changes or reconfigurations of a system. The main idea is that intelligent colored tokens denote part types that represent real-time knowledge about changes and status of a system. Thus, dynamic configurations of a system can be effectively modeled. The developed ICTPN can model dynamic changes of a system in a modular manner, resulting in the development of a very compact model. In addition, when configurations appear, only the changed colored token of the part type from the current model has to be modified. Based on the resultant ICTPN model, deadlock-free, conservative, and reversible behavioral properties, among others, are guaranteed. The developed ICTPN model was tested and validated using the GPenSIM tool and compared with existing methods from the literature.

Various deadlock control policies for automated manufacturing systems with reliable and shared resources have been developed, based on Petri nets. In practical applications, a resource may be unreliable. Thus, the deadlock control policies proposed in previous studies are not applicable to such applications. This paper proposes a two-step robust deadlock control strategy for systems with unreliable and shared resources. In the first step, a live (deadlock-free) controlled system that does not consider the failure of resources is derived by using strict minimal siphon control. The second step deals with deadlock control issues caused by the failures of the resources. Considering all resource failures, a common recovery subnet based on colored Petri nets is proposed for all resource failures in the Petri net model. The recovery subnet is added to the derived system at the first step to make the system reliable. The proposed method has been tested using an automated manufacturing system deployed at King Saud University.

Petri nets are a useful tool for the modeling and performance evaluation of discrete event systems. Literature reveals that the Petri Net models of real-world discrete event systems are most frequently event graphs (a subclass of Petri nets). Literature also reveals that there are some simple methods for the performance evaluation of event graphs. The general-purpose Petri Net simulator (GPenSIM) is a new simulator that runs on the MATLAB platform. GPenSIM provides a Petri net language, with which Petri net classes and extensions can be developed. GPenSIM also provides functions for performance analysis. Since real-world discrete event systems usually possess a large number of resources, the Petri net models of these systems tend to become huge. Activity-Oriented Petri Nets (AOPN) is an approach that reduces the size of the Petri nets. In addition to the simulator functions, GPenSIM also realizes the AOPN approach on the MATLAB platform. Thus, AOPN is an integral part of GPenSIM. As a running example, a flexible manufacturing system is firstly modeled as an event graph, and then the size of the model is reduced with the AOPN approach. The advantages of GPenSIM and AOPN are discussed in this paper.

Cloud Computing provides on-demand computing services like software, networking, storage, analytics, and intelligence over the Internet (“the cloud”). But it is facing challenges because of the explosion of the Internet of Things (IoT) devices and the volume, variety, veracity and velocity of the data generated by these devices. There is a need for ultra-low latency, reliable service along with security and privacy. Fog Computing is a promising solution to overcome these challenges. The originality, scope and novelty of this paper is the definition and formulation of the problem of smart neighborhoods in context of smart grids. This is achieved through an extensive literature study, firstly on Fog Computing and its foundation technologies, its applications and the literature review of Fog Computing research in various application domains. Thereafter, we introduce smart grid and community MicroGrid concepts and, their challenges to give the in depth background of the problem and hence, formalize the problem. The smart grid, which ensures reliable, secure, and cost-effective power supply to the smart neighborhoods, effectively needs Fog Computing architecture to achieve its purpose. This paper also identifies, without rigorous analysis, potential solutions to address the problem of smart neighborhoods. The challenges in the integration of Fog Computing and smart grids are also discussed.