Pierre Lefrançois

Networked manufacturing:: The impact of information sharing

Abstract In this paper we address the impact of information sharing between firms of a manufacturing network. Firms of the network are selected and scheduled to produce an order based on a price–time evaluation of their bids using an optimal planning model for networked manufacturing. Networking strategies, where business relationships are characterized by different levels of shared information on price and capacity, are classified. An illustrative example sketches the impact of information sharing on networked manufacturing using three different kinds of bidding protocol expressing how firms aggregate their information to conform to networking requirements. Finally, the results show that higher networking flexibility in terms of price–time alternatives can be achieved when firms favor tight partnership relations, offering consequently a better scheduling performance.

AN OBJECT-ORIENTED KNOWLEDGE REPRESENTATION FOR INTELLIGENT CONTROL OF MANUFACTURING WORKSTATIONS

Abstract In this paper, we present the concepts behind die implementation of behavioral and cognitive capabilities in objects used for controlling workstations in manufacturing systems. These capabilities characterize a new class of objects called agents; such objects intuitively play die role of human decision-makers in the system modeled. Three types of reasoning activities are considered: a meta-reasoning level to control the inference/search reasoning activities, die local reasoning level incorporating simple rule-based procedures and an extended reasoning level representing the reasoning activities that cannot be performed using simple procedures and thus require external knowledge agents to assist the decision-making process. Implementation issues are discussed, illustrated by an application of the approach to the modeling of workstations in a rolling-mill facility.

Solving a one-dimensional cutting-stock problem in a small manufacturing firm: a case study

This paper presents an evaluation of four different sequential approaches to solving the one-dimensional cutting-stock problem in an industrial setting where trim loss and pattern changes costs are of importance. The evaluation is made with sample problems generated from data of a manufacturing company as well as with real problems. Triangular distributions, which are representative of many practical environments, are introduced as proxies to the distribution of the lengths required. Conclusions are drawn based on the various problem sizes and distributions tested.

Confidence intervals for non-stationary forecast errors: Some empirical results for the series in the M-competition

Abstract This paper investigates the effectiveness of generating confidence intervals from an adaptive estimate of the variance of forecast errors. The investigation is performed on the sample of 1001 series first analyzed by Makridakis et al. It is shown that the accuracy of the intervals is improved and that the type of series and the forecasting model used are among the factors explaining the accuracy of forecast intervals.

An agent-driven approach to design factory information systems

Abstract This paper presents an agent-driven approach for the design of factory information systems. The approach exploits the object/agent-oriented paradigm to construct a factory information system by networking domain-dedicated responsible manufacturing planning and control software agents. The paper presents the agent-based perspective adopted, and then discusses the generic architecture of agent-based factory information systems. Two illustrative examples are provided, one based on the hierarchical manufacturing planning and control scheme behind the ISO/TC 184 AG N 19–20 reference model shop floor production control, and a second based on a distributed planning and control scheme derived from a commercial implementation by the authors, of an agent-based factory information system.

An organism-oriented modeling approach to support the analysis and design of intelligent manufacturing systems

In this paper a generic macroscopic object, termed an ‘organism’, is introduced. An organism defines a high-level modeling object that has the capabilities for organizational networking, standardization or characteristics specifications, decoupling of editing and visualization as well as temporal management. Organism-oriented models inherit from their parent object-oriented and object/agent-oriented models a simplified representation of the manufacturing entities as well as a capacity for many levels of abstraction. Moreover, the organism-oriented modeling approach enriches these models by not only considering basic manufacturing objects and agents, but also the fact that each of these objects and agents may itself be an organization and also part of one or several organizations. The paper first surveys some of the current approaches used for modeling and analysing manufacturing systems: structured analysis, Petri nets, object and object/ agent models. The object model behind the organism-oriented modeling approach is then presented and its application to a manufacturing case is illustrated.

Agent-Oriented Simulation Model of Manufacturing Activities

This article presents our work on the development of object-oriented simulations of production systems using distributed artificial intelligence DAI. DAI is a subfield of artificial intelligence that deals with interactions of intelligent agents. An agent is a computer entity that, through the use of artificial intelligence techniques, can display a pseudointelligent behavior and interact with other agents to form a cooperative organization. The agents are linked by a communications network that enables them to coordinate their activities and enroll each others help to solve particular production problems. We present an object-oriented implementation of DAI and illustrate it with the simulation of a rolling-mill facility.

A genetic search algorithm to optimize job sequencing under a technological constraint in a rolling-mill facility

This article presents some results from the application of a genetic search algorithm to solve a job scheduling problem where setup costs depend on the order of the jobs. An empirical study shows that, for small problems, the solutions given by the genetic algorithm are as good as those obtained with a mixed-integer linear program. For larger problems that are computationally infeasible, we benchmark the genetic solutions against traditional scheduling heuristics. We also study different population management strategies that can improve the performance of the algorithm. Finally, future research avenues are discussed.ZusammenfassungBetrachtet wird ein dynamisches Problem der Reihenfolgeplanung in einem Walzwerk. Ziel ist die Minimierung der Summe aus Lagerkosten für Halbfertigfabrikate und reihenfolgeabhängigen Rüstkosten. Zur Lösung wird ein genetischer Algorithmus benutzt. Zur Beurteilung der Leistungsfähigkeit des Verfahrens werden für kleinere Probleme exakte Lösungen herangezogen, für größere Probleme erfolgt ein Vergleich mit prioritätsregelbasierten Verfahren.

Modelling and simulation of fabrication and assembly plants: an object-driven approach

This paper presents the technical characteristics and methodologies underlying the computer-aided modelling and simulation of fabrication and assembly plants through an object-driven approach. The paper is organized in four main sections. The first and second sections discuss the needs for model integration and then outline the modelling and simulation approach used. The third and fourth sections illustrate an application of the approach for modelling and simulating a specific fabrication and assembly plant.

Batching annealing operations to optimize queueing times and furnance efficiency: a simulation model

Describes a simulation model representing the annealing operations in a rolling-mill facility. The model was developed using the discrete event simulation language SIMAN. The model integrates a user friendly interface through which the user interacts with the simulation model without programming or recompiling. The simulation model also integrates external subroutines to optimize the batching of jobs and their sequencing so as to minimize flow-times and lateness and maximize the annealing furnace efficiency. The optimization approach relies mainly on a data-driven adaptive estimation of the lot sizes based on a queuing model formulation.<<ETX>>