In this paper we present a novel formulation for analysis and performance optimisation of flexible manufacturing systems. We approximately represent the dynamics of the system with a hybrid model and derive an optimum control strategy for part routing and machine scheduling. We show that the system can be described with a single formalism as a stochastic discrete time, time-varying, state variable model which allows fast and direct design of the system configuration.

In this paper, we present a hybrid formulation for the modelling and control of automated manufacturing systems combining both discrete and continuous dynamics. We derive a hierarchical two-level production control policy based on hybrid automata models and capable of providing closed-loop behaviors which satisfy both logic-based and goal optimization specifications. Our main contribution is the integration of fluid approximations within a hybrid automata model aiming at performance optimization subject to logical constraints. By introducing the notion of macro-states and macro-events, we show that these settings can be achieved through a supervisory controller which specifies a desired macro-event trajectory set, represented by a generated language, upon which the optimization procedure is being performed.

We present an approach to the control of manufacturing systems via agent interactions. Based on a decentralized control strategy the agents can collaborate with each other conveniently. We provide a distributed architecture to model the elementary services (an unreliable machine coupled with a finite buffer) in terms of hybrid automata which are co-operating in a manufacturing system, and we show how the agents can efficiently make their control decisions using their local rules.

Describes a hierarchical two level modeling and control framework for real-time manufacturing processes, based on hybrid Petri nets, and capable of integrating monitoring and fault detection techniques along with performance optimization procedures. At the higher level, first-order hybrid Petri nets are used to generate the asynchronous concurrent events characterizing the nominal optimal system behavior, also providing the machine production rates. At the lower level, a real time scheduler and a discrete Petri net communicate with the field-bus devices to force the machine to produce at the rates provided by the upper level, and monitor the occurring events by keeping track of part movements. Whenever a process failure is detected, the diagnosis procedure is triggered, and a recovery sequence is imposed.

In this paper we discuss a hybrid formulation for the modeling and control of automated manufacturing systems within a framework that distinguishes two levels of aggregation. At the lower level the discrete flows of parts are approximated by first-order fluid models. At the higher level a finite automaton represents the transitions of the system through a sequence of admissible macro-states at the occurrence of the macro-events. We describe both levels with a single formalism by a hybrid automaton and we approach the safety verifcation of the system requirements by addressing the reachability problem in terms of the timing information associated with the verification problem.

We consider in this paper first-order hybrid Petri nets, a model that consists of continuous places holding fluid, discrete places containing a nonnegative integer number of tokens, and transitions, either discrete or continuous. We set up a linear algebraic formalism to study the first-order continuous behavior of this model and show how its control can be framed as a conflict resolution policy that aims at optimizing a given objective function. The use of linear algebra leads to sensitivity analysis that allows one to study of how changes in the structure of the model influence the optimal behavior. As an example of application, we show how the proposed formalism can be applied to flexible manufacturing systems with arbitrary layout and different classes of products.

We present a formulation for the optimal control of discrete event dynamic processes which represent production systems with unreliable machines and buffers of finite capacity. We derive an optimum control strategy that is critically based on the fact that the discrete event dynamic behavior of the system is approximately represented with a hybrid model. We introduce a deterministic fluid network model where the average flow rates through the machines are the control variables and with an original approach we show that the dynamics of the system easily translate into a discrete-time, time-varying state variable model, where the optimum machine production rates can be easily obtained by solving a sequence of linear programming problems.

We analyze discrete event dynamic processes which represent flexible manufacturing systems (FMS) characterized by unreliable machines, buffers of finite capacity, arbitrary service time distributions and deterministic sequencing and routing policies. Our main goal is the design of the FMS configuration embedded with its optimum control policy. The problem is addressed using first and second order fluid approximation obtained by splitting the process in two hierarchical layers and defining what we call micro and macro events. With an original approach this fluid approximation lends itself to a discrete-time linear stochastic state variable model which offers average values and variances of both performance measures and their gradients with respect to the most significant FMS parameters. Finally, we investigate problems concerning the non-differentiability of the performance indices with respect to certain design parameters, specifically the control parameters when they appear as structural elements of the model.

In this paper we present a method for performance evaluation of hybrid stochastic Petri nets based on sensitivity analysis and parametric linear programming techniques and we show how this approach can be used for optimization. The problem is addressed by determining an optimal firing speed allocation for the continuous transitions obtained by solving a sequence of linear programming problems aimed at optimizing a certain performance index. The primary advantage of our approach is that it gives rise to a dynamic firing speeds allocation that is based on global state information rather than local information. This original formulation allows us to easily solve conflicts, evaluate performance measures and perform gradient estimation very efficiently.