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

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

Symbolic Supervisory Control of Resource Allocation Systems

<p>Supervisory control theory (SCT) is a formal model-based methodology for verification and synthesis of supervisors for discrete event systems (DES). The main goal is to guarantee that the closed-loop system fulfills given specifications. SCT has great promise to assist engineers with the generation of reliable control functions. This is, for instance, beneficial to manufacturing systems where both products and production equipment might change frequently.</p> <p>The industrial acceptance of SCT, however, has been limited for at least two reasons: (i) the analysis of DES involves an intrinsic difficulty known as the state-space explosion problem, which makes the explicit enumeration of enormous state-spaces for industrial systems intractable; (ii) the synthesized supervisor, represented as a deterministic finite automaton (FA) or an extended finite automaton (EFA), is not straightforward to implement in an industrial controller.</p> <p>In this thesis, to address the aforementioned issues, we study the modeling, synthesis and supervisor representation of DES using binary decision diagrams (BDDs), a compact data structure for representing DES models symbolically. We propose different kinds of BDD-based algorithms for exploring the symbolically represented state-spaces in an effort to improve the abilities of existing supervisor synthesis approaches to handle large-scale DES and represent the obtained supervisors appropriately.</p> <p>Following this spirit, we bring the efficiencies of BDD into a particular DES application domain -- deadlock avoidance for resource allocation systems (RAS) -- a problem that arises in many technological systems including flexible manufacturing systems and multi-threaded software. We propose a framework for the effective and computationally efficient development of the maximally permissive deadlock avoidance policy (DAP) for various RAS classes. Besides the employment of symbolic computation, special structural properties that are possessed by RAS are utilized by the symbolic algorithms to gain additional efficiencies in the computation of the sought DAP. Furthermore, to bridge the gap between the BDD-based representation of the target DAP and its actual industrial realization, we extend this work by introducing a procedure that generates a set of "guard" predicates to represent the resulting DAP.</p> <p>The work presented in this thesis has been implemented in the SCT tool Supremica. Computational benchmarks have manifested the superiority of the proposed algorithms with respect to the previously published results. Hence, the work holds a strong potential for providing robust, practical and efficient solutions to a broad range of supervisory control and deadlock avoidance problems that are experienced in the considered DES application domain.</p

Process Completing Sequences for Resource Allocation Systems with Synchronization

This paper considers the problem of establishing live resource allocation in workflows with synchronization stages. Establishing live resource allocation in this class of systems is challenging since deciding whether a given level of resource capacities is sufficient to complete a single process is NP-complete. In this paper, we develop two necessary conditions and one sufficient condition that provide quickly computable tests for the existence of process completing sequences. The necessary conditions are based on the sequence of completions of � subprocesses that merge together at a synchronization. Although the worst case complexity is O(2�), we expect the number of subprocesses combined at any synchronization will be sufficiently small so that total computation time remains manageable. The sufficient condition uses a reduction scheme that computes a sufficient capacity level of each resource type to complete and merge all � subprocesses. The worst case complexity is O(�⋅�), where � is the number of synchronizations. Finally, the paper develops capacity bounds and polynomial methods for generating feasible resource allocation sequences for merging systems with single unit allocation. This method is based on single step look-ahead for deadly marked siphons and is O(2�). Throughout the paper, we use a class of Petri nets called Generalized Augmented Marked Graphs to represent our resource allocation systems

Confusion Control in Generalized Petri Nets Using Synchronized Events

The loss of conflicting information in a Petri net (PN), usually called confusions, leads to incomplete and faulty system behavior. Confusions, as an unfortunate phenomenon in discrete event systems modeled with Petri nets, are caused by the frequent interlacement of conflicting and concurrent transitions. In this paper, confusions are defined and investigated in bounded generalized PNs. A reasonable control strategy for conflicts and confusions in a PN is formulated by proposing elementary conflict resolution sequences (ECRSs) and a class of local synchronized Petri nets (LSPNs). Two control algorithms are reported to control the appeared confusions by generating a series of external events. Finally, an example of confusion analysis and control in an automated manufacturing system is presented

Computationally Improved Optimal Control Methodology for Linear Programming Problems of Flexible Manufacturing Systems

Deadlock prevention policies are used to solve the deadlock problems of FMSs. It is well known that the theory of regions is the efficient method for obtaining optimal (i.e., maximally permissive) controllers. All legal and live maximal behaviors of Petri net models can be preserved by using marking/transition-separation instances (MTSIs) or event-state-separation-problem (ESSP) methods. However, they encountered great difficulties in solving all sets of inequalities that is an extremely time consuming problem. Moreover, the number of linear programming problems (LPPs) of legal markings is also exponential with net size when a plant net grows exponentially. This paper proposes a novel methodology to reduce the number of MTSIs/ESSPs and LPPs. In this paper, we used the well-known reduction approach Murata (1989) to simply the construct of system such that the problem of LPPs can then be reduced. Additionally, critical ones of crucial marking/transition-separation instances (COCMTSI) are developed and used in our deadlock prevention policy that allows designers to employ few MTSIs to deal with deadlocks. Experimental results indicate that the computational cost can be reduced. To our knowledge, this deadlock prevention policy is the most efficient policy to obtain maximal permissive behavior of Petri net models than past approaches

Discrete events: Perspectives from system theory

Systems Theory;differentiaal/ integraal-vergelijkingen