Planification des ressources de production

MRP est un sigle signifiant Materials Resources Planning ou Materials Requirements Planning ou encore Manufacturing Resources Planning. L'Office québécois de la langue française (OQLF) propose comme traduction planification des ressources de production. Ancêtre de l'ERP (Enterprise Resource Planning, ou progiciel de gestion intégré en français), le MRP est toujours la partie centrale d'un système de gestion de production informatisé. Mélange de logiciel et de base de données, le MRP a pour rôle principal de permettre la planification de la production en fonction des ressources en personnel, en matières premières, en machines et en temps, par rapport à un besoin daté ou un besoin de stock. On distingue le MRP (Materials Requirements Planning, que l'on peut traduire par « planification des besoins en composants »), né formellement aux États-Unis en 1964/65 et qui ne représente alors qu’une méthode de calcul des besoins en matières, de son évolution, le MRP2

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A Markovian model for the hybrid manufacturing planning and control method ‘Double Speed Single Production Line’

Naoufel Cheikhrouhou, Rémy Glardon, Christoph Hachen

In this paper, the hybrid production planning & control method Double Speed Single Production Line (DSSPL) is presented, modelled and its performances evaluated and compared to classical Production Planning and Control methods (PPC). DSSPL combines JIT/kanban and Material Requirement Planning for the production of different classes of products (A-and B-items based on a market or customer oriented analysis) on one single production line. By the use of a Markovian birth-death queuing model of a single-stage, two-product production system, the performance and the behavior of the basic DSSPL concept are analyzed and compared to the classical MRP concepts. Its capability to cope with limited resources is illustrated with an industrial case study where DSSPL has been implemented to solve coordination problems between a plastic molding feeder shop and the final assembly line of a micromotor producer. (C) 2009 Elsevier Ltd. All rights reserved.

Elsevier2009

Chargement

Resource Trade-Offs in Circuits and Systems: from Neurotechnology to Communications

Arda Uran

This thesis aims to explore and exploit trade-offs in integrated circuits and systems to overcome the fundamental bottlenecks faced by future data acquisition and communication systems. Specifically, we target the emerging implantable neurotechnology and the advancing highspeed communication domains as case studies. The first part of the thesis addresses the extreme resource constraints of implantable neurotechnology. As the number of electrodes for neural recording reaches towards thousands, a single-chip solution is rendered infeasible in terms of both energy and area. To tackle this problem, we present an analog front-end architecture that applies various energy- and areasaving techniques to reduce the hardware resource demand of neural recording. The second challenge has been transferring the massive data throughput over a wireless link to allow neural recording in independent environments. Inspired by the previous ideas for compression and the emerging machine learning applications in neuroscience, we introduce an on-chip adaptive feature extraction framework to reduce the overall data rate to the feasible range. The second part of the thesis focuses on the timing uncertainty problem in high-speed communication systems. The increasing data rates in wireline and wireless transceivers, and the emerging need for multitransceiver architectures necessitate the lowest energy and area for the frequency synthesis, which contradicts the current jitter-cost trade-off paradigm. To allow the use of area-efficient ring oscillators for jitter-sensitive applications under low-power constraints, we introduce a multiphase feedback phase-locked loop architecture, which suppresses the oscillator phase noise over a larger bandwidth.

EPFL2021

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A new formulation of the two-dimensional cutting-stock problem with consideration of demand planning

Omar Ayadi, Naoufel Cheikhrouhou

This paper focuses on the two dimensional rectangular non-oriented guillotine cutting stock problem (TDRCSP) in which many pieces with different dimensions need to be cut with different quantities in order to satisfy customers' orders. In order to maximise the use of raw materials, make-to-order 'MTO' and make-to-stock 'MTS' production strategies are combined; in addition to the (firm) orders that need to be fulfilled, other quantities of pieces are considered when designing cutting patterns in order to satisfy the forecast plan over a fixed time horizon. A new formulation of the optimisation problem is proposed in this paper considering multi-period demand planning as well as inventory management constraints. To solve this new problem, a hybrid heuristic, based on the combination of the Bottom Left and Shelf algorithms, is considered. It is shown that formulating the problem combining the original non-oriented guillotine TDRCSP with demand and inventory planning optimises the raw material use and yields good solutions in very short computational times. Results show that integrating forecast and inventory constraints are much more of an additional way to improve the raw material use than a real constraint. Finally, a discussion on the hybrid heuristic sensitivity and robustness is reported.

2012

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