Documentdetail
ID kaart

oai:HAL:hal-03166166v1

Onderwerp
Reconfigurable Manufacturing Syste... Reconfigurability Optimization Simulated Annealing [SPI]Engineering Sciences [physics...
Auteur
Hashemi Petroodi, Seyyed Ehsan Beauville Dit Eynaud, Amélie Klement, Nathalie Tavakkoli-Moghaddam, Reza
Langue
en
Editor

HAL CCSD

Categorie

CNRS - Centre national de la recherche scientifique

Jaar

2019

vermelding datum

29-09-2023

Trefwoorden
reconfigurable study system manufacturing optimization model simulation
Metriek

Beschrijving

International audience; In this study, we consider a production planning and resource allocation problem of a Reconfigurable Manufacturing System (RMS).

Four general scenarios are considered for the product arrival sequence.

The objective function aims to minimize total completion time of jobs.

For a given set of input parameters defined by the market, we want to find the best configuration for the production line with respect to the number of resources and their allocation on workstations.

In order to solve the problem, a hybridization approach based on simulation and optimization (Sim-Opt) is proposed.

In the simulation phase, a Discrete Event Simulation (DES) model is developed.

On the other hand, a simulated annealing (SA) algorithm is developed in Python to optimize the solution.

In this approach, the results of the optimization feed the simulation model.

On the other side, performance of these solutions are copied from simulation model to the optimization model.

The best solution with the best performance can be achieved by this manually cyclic approach.

The proposed approach is applied on a real case study from the automotive industry.

Hashemi Petroodi, Seyyed Ehsan,Beauville Dit Eynaud, Amélie,Klement, Nathalie,Tavakkoli-Moghaddam, Reza, 2019, Simulation-based optimization approach with scenario-based product sequence in a Reconfigurable Manufacturing System (RMS): A case study, HAL CCSD

Document

Openen

Delen

Bron

Artikelen aanbevolen door ES/IODE AI

Bone metastasis prediction in non-small-cell lung cancer: primary CT-based radiomics signature and clinical feature
non-small-cell lung cancer bone metastasis radiomics risk factor predict cohort model cect cancer prediction 0 metastasis radiomics clinical