Agostino Marcello Mangini

On-line fault detection in discrete event systems by Petri nets and integer linear programming ☆

The paper addresses the fault detection problem for discrete event systems in a Petri Net (PN) framework. Assuming that the structure of the PN model and the initial marking are known, faults are modelled by unobservable transitions. Moreover, we assume that there may be additional unobservable transitions associated with the system legal behaviour and that the marking reached after the firing of any transition is unknown. The proposed diagnoser works on-line: it waits for the firing of an observable transition and employs an algorithm based on the definition and solution of some integer linear programming problems to decide whether the system behaviour is normal or exhibits some possible faults. The results characterize the properties that the PN modelling the system fault behaviour has to fulfill in order to reduce the on-line computational effort.

Real time identification of discrete event systems using Petri nets

The paper defines the identification problem for Discrete Event Systems (DES) as the problem of inferring a Petri Net (PN) model using the observation of the events and the available output vectors, that correspond to the markings of the measurable places. Two cases are studied considering different levels of the system knowledge. In the first case the place and transition sets are assumed known. Hence, an integer linear programming problem is defined in order to determine a PN modelling the DES. In the second case the transition and place sets are assumed unknown and only an upper bound of the number of places is given. Hence, the identification problem is solved by an identification algorithm that observes in real time the occurred events and the corresponding output vectors. The integer linear programming problem is defined at each observation so that the PN can be recursively identified. Some results and examples characterize the identified PN systems and show the flexibility and simplicity of the proposed technique. Moreover, an application to the synthesis of supervisory control of PN systems via monitor places is proposed.

A First-Order Hybrid Petri Net Model for Supply Chain Management

A supply chain (SC) is a network of independent manufacturing and logistics companies that perform the critical functions in the order fulfillment process. This paper proposes an effective and modular model to describe material, financial and information flow of SCs at the operational level based on first-order hybrid Petri nets (PNs), i.e., PNs that make use of first-order fluid approximation. The proposed formalism enables the SC designer to choose suitable production rates of facilities in order to optimize the chosen objective function. The optimal mode of operation is performed based on the state knowledge of the obtained linear discrete-time, time-varying state variable model in order to react to unpredictable events such as the blocking of a supply or an accident in a transportation facility. A case study is modeled in the proposed framework and is simulated under three different closed-loop control strategies.

A Three-Level Strategy for the Design and Performance Evaluation of Hospital Departments

The efficient management of hospital departments (HDs) has recently become an important issue. Indeed, the increased demand and design for hospital services have saturated the capacity of HD that requires suitable tools for the efficient use of resources and flow of patients, staff, and drugs. This paper proposes a model based on a three-level strategy to design at the tactical level in a concise and effective way the structure, the resources, and the dynamics of a critically congested HD. The design strategy is composed of three basic elements: the modeling module, the optimization module, and the simulation and decision module. The first module employs a Unified Modeling Language tool and a timed Petri net (PN) model to effectively capture the detailed flow and dynamics of patients, starting from their arrival to the HD until their discharge. The optimization module employs the fluid relaxation to concisely approximate in a continuous PN framework the HD model and optimize suitable performance indices. The simulation module verifies that the optimized parameters allow an effective workflow organization while maximizing the patient flow. In case of inconsistencies due to the fluid approximation between the continuous model used in the design phase by the optimization module and the discrete one used in the subsequent verification phase by the simulation module, the latter module revises the values of some HD model parameters. A real case study on the Emergency Cardiology Department of the General Hospital of Bari (Italy) shows the efficiency and accuracy of the proposed method.

Fault Detection of Discrete Event Systems Using Petri Nets and Integer Linear Programming

Abstract The paper addresses the fault detection problem for discrete event systems on the basis of a Petri Net (PN) model. Assuming that the structure of the PN and the initial marking are known, faults are modelled by unobservable transitions. Moreover, we assume that there may be additional unobservable transitions that are associated with the system legal behaviour and that the marking reached after the firing of a transition is unknown. We propose a diagnoser that works on-line: it waits for the firing of an observable transition and employs an algorithm based on the definition of some integer linear programming problems to decide whether the system behaviour is normal or exhibits some possible faults.

A District Energy Management Based on Thermal Comfort Satisfaction and Real-Time Power Balancing

This paper presents a district energy management strategy devoted to monitor and control the district power consumption in a twofold human-centered perspective: the respect of users comfort preferences and the minimization of the power consumption and costs. The presented district energy management system forwards the power profile determined the day ahead to each building energy management system that, in turn, minimizes its real-time power consumption and costs (based on rewards and penalties), respecting the comfort preferences. Successively, the power is redistributed among the district buildings in order to minimize the penalties by applying two approaches: a centralized approach for public buildings and a distributed methodology for private buildings. Such optimization problems are formalized by defining some linear programming problems: two case studies are solved to show the applicability of the proposed management strategies.

Fault Detection by Labeled Petri Nets in Centralized and Distributed Approaches

This paper addresses the problem of online fault detection and diagnosis in discrete event systems modeled by labeled Petri nets and using Integer Linear Programming Problem (ILPP) solutions. In particular, unobservable (silent) transitions model faults and both observable and unobservable transitions model the nominal system behavior. Furthermore, observable transitions exhibit a kind of non determinism since several different transitions may share the same event label. This paper proposes two diagnosers that work in two different system settings. The first one is a centralized fault detection strategy: the diagnoser waits for an observable event and an algorithm defines and solves some ILPPs to decide whether the system behavior is normal or may exhibit some faults. In the second setting, the system consists of a set of interacting PN modules and each module is monitored by a diagnoser that has local information on the module structure. Moreover, each diagnoser observes and detects the faults of the module it is attached to and shares information in some of its places that are shared with other modules of the system. Some case studies show the two different approaches and point out the peculiarities of the proposed strategies.

A fuzzy programming approach for the strategic design of distribution networks

The paper addresses the optimal design of the last branch of the supply chain, i.e., the Distribution Network (DN). We extend a deterministic optimization model previously proposed by the authors, using fuzzy numbers and fuzzy logic to take into account uncertainty in the DN model and in the DN design. Hence, a procedure employing digraph modeling and fuzzy mixed integer linear programming is presented to select the optimal DN configuration. To show the method effectiveness, the optimization model is applied to a case study.

The vehicle relocation problem in car sharing systems: modeling and simulation in a petri net framework

The paper proposes an user-based solution for the vehicle relocation problem in car sharing systems. In particular, the impact of a mechanism of economic incentives based on a real time monitoring of vehicles distribution among the parking areas is assessed. We consider three different operative conditions that are described by the Unified Modeling Language and modeled in a Timed Petri Net (TPN) framework. In order to show and compare the effectiveness of the adopted management strategies, the real case study of an electric-car sharing system is evaluated and simulated in the TPN environment. The results underline how the proposed solution leads to an improvement of the overall system performances, by highlighting at the same time the limits of such a strategy.

Discrete consensus in networks with constrained capacity

This paper improves a previous result on the multi-agent assignment problem, in which a group of agents has to reach a consensus on an optimal distribution of tasks, under communication and assignment constraints. Indeed, the drawbacks of the previously proposed algorithm are that it must start from an initial feasible assignment state and that an analysis on the goodness of the reached consensus is not given. In this paper, starting from any unfeasible solution, we develop an extended version of the previous gossip algorithm able to iteratively find an initial feasible assignment state in order to reach the consensus state among the agents. Moreover, a study is performed in order to evaluate how the solution obtained by the discrete consensus algorithm is close to the optimal distribution of tasks. Finally, an example shows the application of the distributed discrete consensus algorithm.