Scheduling uncertain orders in the customer–subcontractor context

Within the customer–subcontractor negotiation process, the first problem of the subcontractor is to provide the customer with a reliable order lead-time although his workload is partially uncertain. Actually, a part of the subcontractor workload is composed of orders under negotiation which can be either confirmed or cancelled. Fuzzy logic and possibility theory have widely been used in scheduling in order to represent the uncertainty or imprecision of processing times, but the existence of the manufacturing orders is not usually set into question. We suggest a method allowing to take into account the uncertainty of subcontracted orders. This method is consistent with list scheduling: as a consequence, it can be used in many classical schedulers. Its implementation in a scheduler prototype called TAPAS is described. In this article, we focus on the performance of validation tests which show the interest of the method

Hybridation of Bayesian networks and evolutionary algorithms for multi-objective optimization in an integrated product design and project management context

A better integration of preliminary product design and project management processes at early steps of system design is nowadays a key industrial issue. Therefore, the aim is to make firms evolve from classical sequential approach (first product design the project design and management) to new integrated approaches. In this paper, a model for integrated product/project optimization is first proposed which allows taking into account simultaneously decisions coming from the product and project managers. However, the resulting model has an important underlying complexity, and a multi-objective optimization technique is required to provide managers with appropriate scenarios in a reasonable amount of time. The proposed approach is based on an original evolutionary algorithm called evolutionary algorithm oriented by knowledge (EAOK). This algorithm is based on the interaction between an adapted evolutionary algorithm and a model of knowledge (MoK) used for giving relevant orientations during the search process. The evolutionary operators of the EA are modified in order to take into account these orientations. The MoK is based on the Bayesian Network formalism and is built both from expert knowledge and from individuals generated by the EA. A learning process permits to update probabilities of the BN from a set of selected individuals. At each cycle of the EA, probabilities contained into the MoK are used to give some bias to the new evolutionary operators. This method ensures both a faster and effective optimization, but it also provides the decision maker with a graphic and interactive model of knowledge linked to the studied project. An experimental platform has been developed to experiment the algorithm and a large campaign of tests permits to compare different strategies as well as the benefits of this novel approach in comparison with a classical EA

Distributed machining control and monitoring using smart sensors/actuators

The study of smart sensors and actuators led, during the past few years, to the development of facilities which improve traditional sensors and actuators in a necessary way to automate production systems. In an other context, many studies are carried out aiming at defining a decisional structure for production activity control and the increasing need of reactivity leads to the autonomization of decisional levels close to the operational system. We suggest in this paper to study the natural convergence between these two approaches and we propose an integration architecture dealing with machine tool and machining control that enables the exploitation of distributed smart sensors and actuators in the decisional system

Hybridization of Bayesian networks and belief functions to assess risk. Application to aircraft deconstruction

This paper aims to present a study on knowledge management for the disassembly of end-of-life aircraft. We propose a model using Bayesian networks to assess risk and present three approaches to integrate the belief functions standing for the representation of fuzzy and uncertain knowledge

Integration of experience feedback into the product lifecycle: an approach to best respond to the bidding process

Bidding process allows a client to choose a bidder to realize an embodiment of work, supply or service. From the bidder point of view, there are several obvious risks when responding because he bets on a future development that hasn’t been yet realized. We propose to assist the bidder with decision support tools based on past experiences to detect, report and minimize these potential risks. In this paper, we present the definition of a conceptual architecture to integrate experience feedback into the product lifecycle taking into account all stages of product lifecycle to best respond new bidding processes

Continuous Improvement Through Knowledge-Guided Analysis in Experience Feedback

Continuous improvement in industrial processes is increasingly a key element of competitiveness for industrial systems. The management of experience feedback in this framework is designed to build, analyze and facilitate the knowledge sharing among problem solving practitioners of an organization in order to improve processes and products achievement. During Problem Solving Processes, the intellectual investment of experts is often considerable and the opportunities for expert knowledge exploitation are numerous: decision making, problem solving under uncertainty, and expert configuration. In this paper, our contribution relates to the structuring of a cognitive experience feedback framework, which allows a flexible exploitation of expert knowledge during Problem Solving Processes and a reuse such collected experience. To that purpose, the proposed approach uses the general principles of root cause analysis for identifying the root causes of problems or events, the conceptual graphs formalism for the semantic conceptualization of the domain vocabulary and the Transferable Belief Model for the fusion of information from different sources. The underlying formal reasoning mechanisms (logic-based semantics) in conceptual graphs enable intelligent information retrieval for the effective exploitation of lessons learned from past projects. An example will illustrate the application of the proposed approach of experience feedback processes formalization in the transport industry sector

Coupling system design and project planning: discussion on a bijective link between system and project structures

This article discuss the architecture of an integrated model able to support the coupling between a system design process and a project planning process. The project planning process is in charge of defining, planning and controlling the system design project. A benchmarking analysis carried out with fifteen companies belonging to the world competitiveness cluster, Aerospace Valley, has highlighted a lack of models, processes and tools for aiding the interactions between the two environments. We define the coupling as the establishment of links between entities of the two domains while preserving their original semantic, thus allowing information to be collected. The proposed coupling is recursive. It enables systems to be decomposed into subsystems when designers consider complexity to be too high, and can also decompose projects into sub-projects. The coupling enables systematically links to be drawn between project entities and system entities. In this paper, we discuss the different possibilities of linking system and project structures during the design and the planning processes. Firstly, after presenting the results of the industrial analysis, the different entities are defined and the various coupling modes are discussed

Technic and Collaboration Breakdown Structures: Drivers of collaborative problem solving approaches in a supply chain context

Problem Solving Methodologies have been par excellence a cornerstone element of the firms’ strategy on achieving effective continuous improvement. But the enterprise evolution towards an extended environment characterized by network-based organization has radically changed the problem solving paradigms. This paper aims to propose a generic and collaborative methodology addressing more complex and distributed problems, dealing with Supply Chain issues and having a key role as a driver for building global competitive advantages and create superior performances at a Supply Chain level

Analyse du cycle de vie du produit par retour d'expérience: proposition d'un outil d'assistance au processus de réponse à appel d'offres

Ce travail a pour objectif d’établir les principes d’un outil d’aide à la décision pour l’instrumentation du processus de réponse aux appels d’offre (PRAO) permettant au maître d’œuvre de conduire efficacement ce processus en minimisant les risques encourus. Le but est de définir un outil interactif utilisant l’expérience acquise dans le déroulement des projets passés pour détecter, rendre compte et minimiser les risques du processus en cours. Pour cela, nous définissons le PRAO et explicitons les différents risques susceptibles d’affecter sa réalisation, puis nous proposons une architecture intégrant ce processus et le retour d’expérience (REX). Enfin, nous définissons une instrumentation de cette méthodologie à partir d’un outil informatique, nommé BP_IAT (Bid Process Interactive Analysis Tool), permettant de prendre en compte les expériences passées pour répondre à un nouvel appel d’offre en minimisant les risques potentiels lors du choix d’un concept de la solution en cours de développement

Proposition d’une architecture composée de multiples processus de retour d’expérience coopérants

Cet article présente les premiers résultats d’une étude réalisée en partenariat avec l’entreprise Turbomeca traitant des problèmes engendrés par l’implémentation de processus de retour d’expérience dans une entreprise étendue. La première partie de l’article est dédiée à la définition et à la description des processus de retour d’expérience et des approches les plus avancées pour faciliter leur implémentation. Dans une seconde partie, nous montrons que dans une entreprise étendue, il est nécessaire de définir de multiples processus de retour d’expérience pour que l’approche soit adaptée aux différents niveaux de décisions et aux différents produits/technologies utilisés dans l’entreprise. Nous proposons une trame générale pour intégrer ces différents aspects et nous présentons une illustration d’un cas concret. Finalement, nous concluons sur l’originalité de notre proposition ses avantages et les perspectives de notre travail