Discrete Event Dynamic Systems Research Articles

Robust Stability Analysis for Autonomous Min-Max Systems

Min-max systems are natural extensions of the well known (max,plus) linear discrete event dynamic system (DEDS) models. They are motivated by recent research on digital circuits and communication networks. In this paper, we study the robust stability of a family of min-max systems whose parameters are perturbed in intervals. In the spirit of Kharitonov, we first reduce the robust stability test to the check of stability on the vertex set of parameter space. We then further reduce the test to a subset derived from the structure of the system family.

Towards a Holistic Approach for Intelligent Manufacturing Systems Synthesis

To face the great six challenges, that has been identified during last two workshops on Intelligent Manufacturing System, Filos et al., (2007) a synergetical complex multidisciplinary research program is a must to aim at well balancing the plethora of paradigms, methodology software products, a.s.o. The present paper main concerns with developing an appropriate IMS synthesis methodology, based on sound concepts of General Systems Theory. To sustain a “grand unification”, the 4–dimensions perspective: Discrete Event Dynamical Systems representation, Intelligent Information Systems representation, Collaborative Production and Advanced Control Systems representation, and Business Informatics System representation. Finally, a 3 phases Top-Down/ Bottom-up / Cross services approach services methodology applied for developing a complex IMS universitary pilot within University POLITEHNICA of Bucharest campus is briefly described.

Orders dispatching game for a multi-facility manufacturing system

This paper proposes a novel decision algorithm on higher-level work orders dispatching problems under a holistic manufacturing configuration of multiple facilities. The multi-facility work order dispatching problems have been formulated as a stochastic nonautonomous Lotka–Volterra difference game. A closed loop control scheme with discrete event dynamical system is proposed based on the setup of evolutionary game. Our results show the viability and advantage of proposed dispatching scheme.

A new stochastic Petri net model and its applications to logistics systems

This is a summary of the author’s PhD thesis supervised by Lionel Amodeo and Hoaxun Chen and defended on 29 November 2005 at the Universite de Technologie de Troyes (France). The thesis is written in French and is available from the author upon request. This work deals with a new stochastic Petri net model and its applications for modeling and studying logistics systems and more generally discrete event dynamic systems.

Modeling and Analysis of Multi-agent Systems using Petri Nets

The development of theoretical-based methods for the assessment of multi-agent systems properties is of critical importance. This work is a preliminary investigation on methodologies for modeling, analysis and design of multi-agent systems. Multi-agent systems are regarded as discrete-event dynamic systems and Petri nets are used as a modeling tool to assess the structural properties of the multi-agent system. Our methodology consists of defining a simple multi-agent system based on the abstract architecture for intelligent agents. The abstract architecture is modeled as a discrete-event system using Petri nets and structural analysis of the net provides an assessment of the communication and coordination properties of the multi-agent system. Deadlock avoidance in the multi-agent system is considered as an initial key property, and it is evaluated using liveness and boundedness properties of the Petri net model.

Diagnosability of fuzzy discrete event systems

In this paper, discrete event systems (DESs) are reformulated as fuzzy discrete event systems (FDESs) and fuzzy discrete event dynamical systems (FDEDSs). These frameworks include fuzzy states, events and IF–THEN rules. In these frameworks, all events occur at the same time with different membership degrees. Fuzzy states and events have been introduced to describe uncertainties that occur often in practical problems, such as fault diagnosis applications. To measure a diagnoser’s fault discrimination ability, a fuzzy diagnosability degree is proposed. If the diagnosability of the degree of the system yields one a diagnoser can be implemented to identify all possible fault types related to a system. For any degree less than one, researchers should not devote their time to distinguish all possible fault types correctly. Thus, two different diagnosability definitions FDEDS and FDES are introduced. Due to the specialized fuzzy rule-base embedded in the FDEDS, it is capable of representing a class of non-linear dynamic system. Computationally speaking, the framework of diagnosability of the FDEDS is structurally similar to the framework of diagnosability of a non-linear system. The crisp DES diagnosability has been turned into the term fuzzy diagnosability for the FDES. The newly proposed diagnosability definition allows us to define a degree of diagnosability in a class of non-linear systems. In addition, a simple fuzzy diagnosability checking method is introduced and some numerical examples are provided to illustrate this theoretical development. Finally, the potential applications of the proposed method are discussed.

A new approach to modelling, control and simulation of knowledgeable manufacturing cell

The running of the knowledgeable manufacturing cell (KMC) is a typical discrete event dynamic process. Methods used to model and analyse discrete event dynamic systems (DEDS) include Petri net and automata theory. In this paper, a new matrix-automaton is proposed to model the KMC and solve the task control problems existing in the KMC. The automaton is of structured property and can be used to analyse the dynamic performance of the KMC. Stochastic dynamic programming is used to derive the optimal task control strategy of the automaton, and a simulation method and program are proposed to simulate the running of the KMC. The matrix-automaton model of an experimental KMC including m manufacturing agents and n classes of workpieces is built and solved by the methods in this paper. Compared with the random control principle, the objective function value of the control strategy in this paper is obviously lower, which testifies the validity and feasibility of the control strategy.

The Evolutionary Programming in the Identification of Discreet Events Dynamic Systems

The present work evaluates the use of the evolutionary programming in problems of Industrial Automation. It is well known that technical of the evolutionary calculation as the Genetic Algorithms have provided satisfactory results when facing problems of automatic control, especially of continuous type, while the study of discrete dynamic have been relegated a little. In this work an identification technique is implemented based on the evolutionary programming for Discrete Events Dynamic Systems (DEDS), using finite state machines (like the machines of Mealy). This way, the identification of systems is approached, but whose dynamics are governed by discrete events. Also, we build a system based on the evolutionary programming to design finite state machines.

ROBUST DYNAMIC CONTROL OF MULTI-PRODUCT JOB-SHOPS

This paper deals with the robustness of Dynamic Discrete Event Systems including time interval constraints. It recalls to mind a method to compute the passive robustness by the decomposition of the P-time Petri Net model into four sets. The computing algorithm of a lower bound for the maximal time disturbances allowed on a given point is presented. The main contribution concerns the active robustness. An algorithm providing a global robustness new lower bound is introduced.

Deadlock Detection and Avoidance Strategies for Automated Storage and Retrieval Systems

This paper focuses on real-time control of automated storage and retrieval systems (AS/RSs) serviced by a rail-guided vehicle system, a widely used solution for material handling in warehouses. The generic multiproduct AS/RS is modeled as a timed discrete event dynamical system, whose state provides the information on the current interactions between users and resources. Moreover, we address the real-time controller that governs resource allocations and scheduling choices by enabling and inhibiting the system events in order to avoid collisions and deadlocks. To this aim, we characterize deadlock in AS/RSs and define two deadlock resolution strategies: a deadlock avoidance and a deadlock detection/recovery policy. The proposed deadlock formulation and characterization have a general validity and can be applied to single unit resource allocation systems where a subset of users may be regarded as resources of other users. We compare the proposed control policies for a large-scale AS/RS presented in the related literature by several discrete event simulation tests.

Distributed Diagnosis of Place-Bordered Petri Nets

This paper studies online fault detection and isolation of modular dynamic systems modeled as sets of place-bordered Petri nets. The common places among the set of Petri nets modeling a system capture coupling of various system components. The transitions are labeled by events, some of which are unobservable (i.e., not directly recorded by the sensors attached to the system). The events whose occurrence must be diagnosed have unobservable transition labels. These events model faults or other significant changes in the system state. The existing theory of diagnosis of discrete-event systems is extended in the context of the above model. The modular structure of the system is exploited by a distributed algorithm for fault diagnosis. A Petri net diagnoser is associated with every Petri net and the diagnosers communicate in real time during the diagnostic process when the token count of common places changes. A merge function is defined to combine the individual diagnoser states and recover the complete diagnoser state that would be obtained under a monolithic approach. Strategies that reduce the communication overhead are presented. The software implementation of the distributed algorithm is discussed. Note to Practitioners-In the last decade, monitoring, fault detection, and diagnosis methodologies based on the use of discrete-event models have been successfully used in a variety of technological systems ranging from document processing systems to intelligent transportation systems. This paper was motivated by the problem of fault diagnosis for modular (distributed) dynamic discrete-event systems (DES). As a DES modeling formalism, Petri nets offer potential advantages in terms of the distributed representation of the system and the ability to represent coupling of the system components. The systems studied in this paper are sets of modules coupled with each other through various system components and modeled using Petri nets. We present a distributed fault diagnosis algorithm which allows each module in the distributed system to diagnose its faults independently unless completion of a task requires the use of coupled components. In the case of coupling, modules communicate with each other to accurately diagnose the fault. The distributed fault diagnosis algorithm recovers the monolithic diagnosis information at the cost of communication and growing communication overhead. To mitigate that problem, we present an improved version of the algorithm that significantly reduces the communication overhead. Finally, we introduce the software toolbox (written in Matlab and integrated with AT&T Graphviz) and we present a case study of an example of a heating, ventilation, and air-conditioning system where we use the software tool for modeling and analyzing the system

A polynomial dynamic system approach to software design for attractivity requirement

Because of the widespread increasing application of Web services and autonomic computing, self-adaptive software is an area gaining increasing importance. Control theory provides a theoretical foundation for self-adaptive software. In this paper, we propose the use of the supervisory control theory of discrete event dynamic systems (DEDS) to provide a rigorous foundation for designing software for reactive systems. This paper focuses in particular on design of software with an attractivity requirement. It studies this problem using the polynomial dynamic system (PDS) model of DEDS. A necessary and sufficient condition for software existence and two algorithms for such software design are presented.

Near-Optimal Online Control of Dynamic Discrete-Event Systems

A class of time-varying discrete-event systems, named dynamic discrete-event systems, is defined. The goal of this paper is to provide a method which is modular and can be applied in real solutions for the optimization of the online control of such systems. First, a simple control algorithm is presented, followed by illustrative examples of different issues that can arise if it is used. Afterward, a more complicated near-optimal online control algorithm with normalization of string values is proposed. The time variability of the systems is accounted for and the average computational time is drastically reduced. This is demonstrated with a set of simulations of the performance of the new algorithm.

Performance modeling of FMS with flexible process plans - a Petri Net approach

This paper presents modeling and performance analysis of FMS when flexible process plans for each part type are available using Petri nets (PN) modeling approach. PN have been applied successfully for modeling of discrete event dynamic systems such as FMS that are characterized by conflicts, concurrency, synchronization and deadlocks. An example FMS consisting of four machines with individual input and output buffer has been taken into consideration and its PN model construction is explained. System performance is evaluated in two manufacturing environments (i.e. virtual batch manufacturing and virtual line manufacturing). The analysis will assist the planner in selecting optimum set of operating parameters (such as dispatching rule, number of pallets released to the system etc.) for a given production order to achieve the desired performance measure. Several performance measures such as makespan, mean flow time, maximum flow time and variance of flow time have been used to evaluate system performance. (Received in September 2005, accepted in July 2006. This paper was with the authors 3 months for 2 revisions.)

Formal design and analysis of a hybrid supervisory control structure for Virtual Production Systems

Virtual Production Systems (VPSs) is a dynamic paradigm for production resources structure, which was proposed to cope with changing and uncertain manufacturing environment. However, the high performance of VPSs relies on a high-quality control in practice. Motivated by both local quick responses and global optimization, a supervisory control structure based on autonomous and coordination mechanisms is proposed for VPSs in this paper. This hybrid supervisory control structure is formally designed and analysed with timed automata on the platform of an integrated tool named UPPAAL. First, the Discrete Events Dynamic Systems (DEDS) model of VPSs is established. Then autonomous and coordinated supervisory controllers are further designed under the guidance of heuristic scheduling rules, such that the desired characteristics of both performance (production flow and time) and activity (overflow-, conflict- and deadlock-free) can be ensured. Finally, system analysis, including deadlock-free analysis, calculation and simulation of time-optimal scheduling, is made through verification. A case study is made to illustrate that control and scheduling can be well integrated into this hybrid supervisory control structure in practice.

Constrained Ordinal Optimization—A Feasibility Model Based Approach

Ordinal Optimization (OO) is a useful simulation-based approach for stochastic optimization problems such as the problems in Discrete Event Dynamic Systems (DEDS). However, OO cannot be applied directly for the problem since many infeasible decisions cannot be excluded from ordinal comparison without extensive computation involving the expectation operation. In this paper, a new approach for solving constrained ordinal optimization (COO) problems is presented. The key idea of our method for constrained OO problems is to estimate the feasibility of decisions and to choose selected subset based on the estimated feasibility. Any crude method such as the one based on rough set theory developed in our previous work can be applied to determine the decision feasibility efficiently. The algorithm for subset selection and the procedure of Blind Picking with Feasibility Model (BPFM) for COO are derived in the paper. The infeasible decisions are excluded by an imperfect feasibility model in the procedure of subset selection. The performance of the new method is evaluated and compared with the regular OO method. Numerical testing with two examples including the planning problem of a practical remanufacturing system shows that to meet the same required alignment probability, BPFM is more efficient than pure Blind Picking in regular OO.

Special Issue of Discrete Event Dynamic System: “Discrete Event Methodologies for Hybrid Systems”

Special Issue of Discrete Event Dynamic System: “Discrete Event Methodologies for Hybrid Systems”

Benchmarking the collaboration in the supply chain

Collaboration is an important issue in supply chain management. As the operations in companies have reached a level of efficiency, researchers have begun to put their emphasis on collaboration between different stages in a supply chain. In this paper, to quantitatively benchmark the effect of collaboration, a new Petri net form, combinatorial Petri net, is put forth by combining a traditional Petri net with inference technologies such as fuzzy logic, etc. This new Petri net form not only simulate the working process in Discrete Event Dynamic Systems (DEDSs), but also model the decision-making process. By this newly developed Petri net form, a simplified supply chain is benchmarked for two situations: with and without collaboration. By comparing the inventory behaviour for these two situations, the contribution of collaboration can be evaluated. In practice, this benchmarking system can be a platform to assess different collaboration schemes before implementing them.

CONTROLLING A MODEL OF TRAFFIC LIGHTS BASED ON DIOIDS

In this paper, we introduce a new model of urban traffic networks by using Petri Nets. Based on dioid algebra, the behavior of the system is described by -linear equations. This will allow to approach the traffic control problems which rise issues of cycle times and of synchronization. As a result, we show how we can model shared resource problems inherent in Discrete Event dynamic System modelling. Moreover, we propose a control law based on Lawler algorithm for the traffic lights to minimize the maximum waiting time of cars. An example is worked out to deal with the control of the crossroad.

ON THE DIAGNOSABILITY OF A CLASS OF HIERARCHICAL STATE MACHINES

This paper addresses the problem of Fault Detection and Isolation for a particular class of discrete event dynamic systems named Hierarchical Finite State Machines (HFSMs). A new version of the property of diagnosability for discrete event systems tailored to HFSMs is introduced. This notion, called L1-diagnosability, captures the possibility of detecting an unobservable fault event using only high level observations of HFSMs. Algorithms for testing L1-diagnosability are presented. In addition, guidelines are presented for studying the diagnosability of HFSMs that are not L1-diagnosable.