In this paper, the problem of optimizing the performances of a distributed supply chain is faced by means of as decentralized architecture. In particular, a general scheme for an Lagrangian algorithm, based on Lagrangian relaxation of the precedence constraints, is presented. In this framework, the trade-off between the production cost of each node of the supply-chain and the cost of the whole network, which consists of earliness and tardiness of the received orders, is taken into account. Finally, a study case is analyzed in detail and some simulation results are presented

This work presents our agent-based architecture for the development of Global Automation Systems. These systems consist of software applications that manage all the processes in a network of enterprises, in distributed, decentralized and autonomous way. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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.

Designing automation systems, as in most engineering endeavors, is primarily an architectural problem, involving hardware and software components. The objective of this paper is to introduce the concept of software architecture and styles in designing automation systems, and to describe an object framework for the development of global automation systems.

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 present an architecture for developing applications which need to integrate legacy business enterprise information systems in a large distributed environment. In particular we address the problem of building virtual factories by interconnecting existing physical factories. The proposed architecture consists of several different frameworks each of them addressing a specific design problem: the communication framework, the security framework, the interface framework and the persistence framework. The communication and security frameworks give solutions to the communication problem based on mobile code and Internet technologies. The interface and persistence frameworks address the problem of the integration of the legacy IS which, in the new system, will be wrapped and presented as a standard business object. Each framework has to be customized for each virtual factory to build.

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.

Introduces a production scheduling problem encountered in COMPAL S.A., a factory located in Lisbon (Portugal) which produces perishable goods for the food market. From the factory specifications the authors show how to derive a scheduling model and a two-step heuristic procedure for its solution.>

The authors propose augmented Lagrangian-based decomposition techniques for solving complex planning and control problems in flexible manufacturing systems. It is shown that the resulting decomposition of the mathematical problem lends itself to control systems with a topological cellular structure, and can be treated very naturally with an object-oriented programming approach. The mathematical formulation of the problem is presented. Its application to a specific example in which all the main features of the method are fully described is presented. Some computational results are presented.>

Object-oriented programming, through the use of a library of standard classes and their inheritance relationships, offers a consistent framework to specify, design, and prototype the software architecture of complex discrete event control systems. This work applies such a framework to computer integrated manufacturing, proposing a unified view to represent the hierarchical control structure of a commonly accepted reference model. The results are a library of classes (called G++ and based on the C++ programming language) to express concurrency and to support an extended client server paradigm, and a new design methodology.>

Some experiments on the integration of algorithmic techniques with knowledge-based ones are discussed. Two case studies are presented: an FMS cell and a press shop. It was found that the algorithmic procedures developed for production scheduling resulted in limiting the ability to cope with the complexity of the real manufacturing world. The scheduling problem, seen as a constraint satisfaction problem, can be approached with rule-based techniques. Nevertheless, algorithmic techniques are found to be valuable for their efficiency and ability to deal with aggregated data. This ability is fundamental for an efficient implementation of hierarchical control systems in general and in the manufacturing context in particular. This suggests that the integration of rule-based techniques with algorithmic ones can increase the efficiency of searching in the space of possible solutions. The ability to deal with aggregated data can have little value when detailed real-time operation scheduling is needed. In this case, simple dispatching rules are often used, and sophisticated operations research methods are not used. In such a dynamic situation, a purely-rule based approach may be more suitable.>

The evolution of Computer Integrated Manufacturing calls for the organizatlon of complex production processes, obtained by the composition of automated job shops. The larye-scale event-driven nature of such systems requires a revision of the techniques for designing production planning and control architectures. Two manufacturing environments are here considered : batch and repetitive. In the former case, productlon batches are closely related to customer orders so that th eplanning problem consists of scheduling and sequencing the lots through the sequence of shops in order to meet due dates. In the latter case, stationary or slowly varying order quantities must be produced. Then production rates and lot sizes are the main control variables. The paper shows a unified approach to the production planning problem for batch and repetitive manufacturing. Starting from a common model and control architecture, specialized algorithms are derived for the two cases. A hierarchical and decentralized architecture is stressed.

The performances of a Flexible Manufacturing System with multiple part classes are evaluated by closed queueing network analysis. The problem considered here refers specifically to the presence into the system of a real time intelligent router. It is well known that this condition sharply contrasts with the classical hypothesis needed for an exact solution of the steady state condition of the network (product form solution). Heuristic load-dependent coefficients have been introduced in the Reiser's recursive algorithm of Mean Value Analysis to model a dispatcher that routes parts to a set of equivalent machines according to the unfinished workload. The method has been extended to the case of unequal machine loads. Numerical results are given to show the achievable accurancy of the approximation.