Short-term manpower management in manufacturing systems: new requirements and DSS prototyping

The short-term planning and scheduling of discrete manufacturing systems has mostly focused in the past on the management of machines, implicitly considered as the critical resources of the workshops. Some of the present schedulers claim to also manage human resources, but perform most of the time a local allocation of operators to machines, these operators having regular working hours. However, it seems clear that the workforce has a specificity that should be better taken into account by short-term planning facilities. Moreover, the variability of the weekly working hours through the year will shortly become a rule and not anymore an exception. On the base of a questionnaire answered by 19 French companies of different sizes and industrial sectors, we have tried to identify more precisely some industrial requirements concerning the short-term management of human resources. The growing interest in annualised hours together with the lack of software tools that allow to implement it practically is one of the results of this questionnaire. We suggest in this article the specification of a decision support system for short-term manpower management under annualised hours, taking into account the competence of the operators. A software prototype has been developed according to these specifications; the results of a simple but representative example are described

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

Progressive Shape Models

International audienceIn this paper we address the problem of recovering both the topology and the geometry of a deformable shape using temporal mesh sequences. The interest arises in multi-camera applications when unknown natural dynamic scenes are captured. While several approaches allow recovery of shape models from static scenes, few consider dynamic scenes with evolving topology and without prior knowledge. In this nonetheless generic situation, a single time observation is not necessarily enough to infer the correct topology of the observed shape and evidences must be accumulated over time in order to learn this topology and to enable temporally consistent modelling. This appears to be a new problem for which no formal solution exists. We propose a principled approach based on the assumption that the observed objects have a fixed topology. Under this assumption, we can progressively learn the topology meanwhile capturing the deformation of the dynamic scene. The approach has been successfully experimented on several standard 4D datasets and we believe that it paves the way to more general multi-view scene capture and analysis.Dans cet article nous nous concentrons sur un problème récurrent des systèmes d'acquisition 4D : l'apprentissage de la géométrie et de la topologie d'une scène déformable à partir d'une séquence temporelle de maillages. Il s'agit d'une étape fondamentale dans le traitement de scènes naturelles et dynamiques. Tandis que de nombreux travaux ont été menés pour la reconstruction de scènes statiques, assez peu considèrent le cas de scènes dynamiques dont la topologie évolue et sans connaissances \apriori. Dans cette situation, une simple observation à un unique instant de temps n'est souvent pas suffisante pour retrouver entièrement l'information de topologie propre à la scène observée. Il semble ainsi évident que les indices sur la forme doivent être accumulés intelligemment sur une séquence complète afin d'acquerir une information aussi complète que possible sur la topologie de la scène et permettre l'apprentissage d'un modèle cohérent à la fois spatialement et temporellement. A notre connaissance cela semble un problème nouveau pour lequel aucune solution formelle n'a été proposée. Nous formulons dans cette thèse un principe de solution basé sur l'hypothèse que les objets composant la scène observée possèdent une topologie fixe. A partir de cette hypothèse de base nous pouvons progressivement apprendre la topologie et en parallèle capturer les déformations d'une scène dynamique. Les travaux présentés dans cette partie visent à retrouver une information de basse fréquence sur la géométrie de la scène. En l'état actuel, la méthode que nous proposons ne peut pas être directement utilisée pour accumuler les informations de bas niveau (détails de la surface) sur une séquence de maillages

Learning from Positive and Unlabeled Examples

International audienceIn many machine learning settings, labeled examples are difficult to collect while unlabeled data are abundant. Also, for some binary classification problems, positive examples, that is examples of the target class, are available. Can these additional data be used to improve accuracy of supervised learning algorithms? We investigate in this paper the design of learning algorithms from positive and unlabeled data only. Many machine learning and data mining algorithms, such as decision tree induction algorithms and naive Bayes algorithms, only use examples in order to evaluate statistical queries (SQ-like algorithms). Kearns designed the Statistical Query learning model in order to describe these algorithms. Here, we design an algorithm scheme which transforms any SQ-like algorithm into an algorithm based on positive statistical queries (estimates for probabilities over the set of positive instances) and instance statistical queries (estimates for probabilities over the instance space). We prove that any class learnable in the Statistical Query learning model is learnable from positive statistical queries and instance statistical queries only if a lower bound on the weight of any target concept $f$ can be estimated in polynomial time. Then, we design a decision tree induction algorithm POSC4.5, based on C4.5, that uses only positive and unlabeled examples and we give experimental results for this algorithm. The case of imbalanced classes in the sense that one of the two classes (say the positive class) is heavily underrepresented compared to the other class remains open. This problem is challenging because it is encountered in many real-world applications

Shared resources scheduling using a multi-agent model: the DSCEP framework

Recently, multi-agent systems have been successfully applied to the scheduling problem. A new multi- agent framework, called DSCEP (distributed, supervisor, customers, environment, producers), is suggested in this paper. This framework is developed base on the subsistent SCEP models, especial for shared resources scheduling activities. It introduces a dialogue between three kinds of evolved SCEP models leading to a high level of co-operation. It provides a more efficient control of the consequences generated by the local decisions than usual systems for each SCEP model. It also provides different algorithms in order to handle the disturbances occurring at different ranks in manufacturing process. As a consequence, the DSCEP framework can be adapted for various scheduling/planning problems. This model is applied to the shared resources scheduling problem of complex systems, and provide a natural cohabitation between infinite capacity scheduling processes, performed by the multi-site manufacturing orders, and finite capacity scheduling processes, performed by local or remote machines

Planification des activités de transport d’une entreprise 3PL par une approche multi-agent

La répartition et l'éloignement des sites de production d'entreprises en réseau, l'éloignement et la multiplicité des centres de distribution ou l'explosion du commerce en ligne ont entrainé une augmentation croissante du nombre de demandes de transport dans le monde. Cette augmentation du volume de transports de biens et de marchandises, ajoutée au nombre croissant de déplacements de passagers a conduit à une augmentation du nombre de moyens de transport (véhicules, avions, bateaux, etc.) avec pour conséquence une augmentation de la capacité des voies de communication arrivées à saturation (autoroutes, lignes aériennes, voies de navigation), un élargissement des zones de stockage (ports, aéroports, entrepôts, etc.) et une augmentation de la pollution impactant durablement l'environnement. Dans ce contexte, l'organisation, la gestion et la planification des transports, devenues cruciales, ont favorisé l'apparition de nombreuses sociétés spécialisées (3PL) proposant une mutualisation des moyens de transport et une gestion centralisée. L'objectif de cet article est de présenter une architecture distribuée de planification des activités de transport visant à mieux utiliser les ressources de transport par le regroupement en fonction du contexte de planification de plusieurs ordres de transport