Scheduling of flexible manufacturing systems with automated guided vehicles using petri net models

In this thesis, Petri net models for Flexible Manufacturing Systems (EMS) are constructed. A firing sequence of the Petri net from the initial marking to the final marking can be seen as a schedule of the modeled FMS. By using the branch-and-bound algorithm, an optimal schedule of the FMS can be obtained. Automated Guided Vehicle Systems (AGVS) are increasingly used for material handling in factories and warehouses. An AGVS can reduce labor costs and is ready to be integrated into an automated factory. This thesis presents two AGVS models (shared and duty) which integrate the control of AGVS with the control of part processing facilities. Both types of AGVS are modeled by Petri nets. We want to compare the two AGVS in terms of systems performance and discuss which application is more suitable for each AGVS type. We also want to consider and solve AGV jam problems. The objective of the AGV jam-free control module is to guarantee a jam-free condition among AGVs in an EMS. Results have been obtained and analyzed

A Timed-Automata Based Middleware for Time-Critical Multicore Applications

International audienceThe goal of our work is to contribute to unification of design methodologies for multi-core time-critical systems. Various models of computation have been proposed in literature for this kind of systems, but lack of coherency between them makes unified coherent design methodology challenging. In addition, there is a significant gap between the models of computation and the real-time scheduling and analysis techniques. To overcome this difficulty, we represent both the models of computation and the scheduling policies by timed automata. While, traditionally, they are only used for simulation and validation, we use the automata for programming. We believe that using the same formal language for different design styles and methods is an important step to close the gap between them. Our approach is demonstrated using a publicly available toolset, an industrial application use case and a multi-core platform

Models for Deterministic Execution of Real-Time Multiprocessor Applications

International audienceWith the proliferation of multi-cores in embedded real-time systems, many industrial applications are being (re-)targeted to multiprocessor platforms. However, exactly reproducible data values at the outputs as function of the data and timing of the inputs is less trivial to realize in multiprocessors, while it can be imperative for various practical reasons. Also for parallel platforms it is harder to evaluate the task utilization and ensure schedulability, especially for end-to-end communication timing constraints and aperiodic events. Based upon reactive system extensions of Kahn process networks, we propose a model of computation that employs synchronous events and event priority relations to ensure deterministic execution. For this model, we propose an online scheduling policy and establish a link to a well-developed scheduling theory. We also implement this model in publicly available prototype tools and evaluate them on state-of-the art multi-core hardware, with a streaming benchmark and an avionics case study

Improving just-in-time delivery performance of IoT-enabled flexible manufacturing systems with AGV based material transportation

Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly

Improving just-in-time delivery performance of IoT-enabled flexible manufacturing systems with AGV based material transportation

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly