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Chinese Journal of Mechanical Engineering
Erschienen in: Chinese Journal of Mechanical Engineering 2/2017

20.03.2017 | Original Article

Performance Analysis of Intelligent Robust Facility Layout Design

verfasst von: G MOSLEMIPOUR, T S LEE, Y T LOONG

Erschienen in: Chinese Journal of Mechanical Engineering | Ausgabe 2/2017

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Abstract

Design of a robust production facility layout with minimum handling cost (MHC) presents an appropriate approach to tackle facility layout problems in a dynamic volatile environment, in which product demands randomly change in each planning period. The objective of the design is to find the robust facility layout with minimum total material handling cost over the entire multi-period planning horizon. This paper proposes a new mathematical model for designing robust machine layout in the stochastic dynamic environment of manufacturing systems using quadratic assignment problem (QAP) formulation. In this investigation, product demands are assumed to be normally distributed random variables with known expected value, variance, and covariance that randomly change from period to period. The proposed model was verified and validated using randomly generated numerical data and benchmark examples. The effect of dependent product demands and varying interest rate on the total cost function of the proposed model has also been investigated. Sensitivity analysis on the proposed model has been performed. Dynamic programming and simulated annealing optimization algorithms were used in solving the modeled example problems.
Vorheriger Artikel Optimization and Simulation of Plastic Injection Process using Genetic Algorithm and Moldflow
Nächster Artikel Calibration of Discrete Element Heat Transfer Parameters by Central Composite Design

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Metadaten
Titel
Performance Analysis of Intelligent Robust Facility Layout Design
verfasst von
G MOSLEMIPOUR
T S LEE
Y T LOONG
Publikationsdatum
20.03.2017
Verlag
Chinese Mechanical Engineering Society
Erschienen in
Chinese Journal of Mechanical Engineering / Ausgabe 2/2017
Print ISSN: 1000-9345
Elektronische ISSN: 2192-8258
DOI
https://doi.org/10.1007/s10033-017-0073-9

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