research-article

A multi-layer perceptron for scheduling cellular manufacturing systems in the presence of unreliable machines and uncertain cost

Authors:

Aidin Delgoshaei,

Chandima GomesAuthors Info & Claims

Applied Soft Computing, Volume 49, Issue C

Pages 27 - 55

https://doi.org/10.1016/j.asoc.2016.06.025

Published: 01 December 2016 Publication History

Abstract

Display Omitted Determine the best trading off values between in-house manufacturing and outsourcing in a restricted capacity cellular manufacturing system.A multi-layer perceptron is used for scheduling dynamic cellular manufacturing systems in the presence of cost uncertainty.Propose a new method for measuring the cell-load variation in cellular manufacturing systems.Develop a new method for minimizing system imbalance in dynamic cellular manufacturing systems.It is proved that the inflation rate can increase the system imbalance in dynamic cellular manufacturing systems. In this paper, a new method is proposed for short-term period scheduling of dynamic cellular manufacturing systems in the presence of bottleneck and parallel machines. The aim of this method is to find best production strategy of in-house manufacturing and outsourcing in small and medium scale cellular manufacturing companies. For this purpose, a multi-period scheduling model has been proposed which is flexible enough to be used in real industries. To solve the proposed problem, a number of metaheuristics are developed including Branch and Bound; Simulated Annealing algorithms; Fuzzy Art Control; Ant Colony Optimization and a hybrid Multi-layer Perceptron and Simulated Annealing algorithms. Our findings indicate that the uncertain condition of system costs affects the routing of product parts and may induce machine-load variations that yield to cell-load diversity. The results showed that the proposed method can significantly reduce cell load variation while finding the best trading off values between in-house manufacturing and outsourcing.

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Cited By

Delgoshaei AAriffin MMaleki SLeman Z(2023)Review evolution of dual-resource-constrained scheduling problems in manufacturing systems: modeling and scheduling methods’ trendsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-023-09304-427:24(18489-18528)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s00500-023-09304-4
Oyewole GThopil G(2022)Data clustering: application and trendsArtificial Intelligence Review10.1007/s10462-022-10325-y56:7(6439-6475)Online publication date: 27-Nov-2022
https://dl.acm.org/doi/10.1007/s10462-022-10325-y
Bertolini MMezzogori DNeroni MZammori F(2021)Machine Learning for industrial applicationsExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.114820175:COnline publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1016/j.eswa.2021.114820
Show More Cited By

A multi-layer perceptron for scheduling cellular manufacturing systems in the presence of unreliable machines and uncertain cost
1. Applied computing
2. Theory of computation
  1. Design and analysis of algorithms
  2. Theory and algorithms for application domains
    1. Machine learning theory
      1. Reinforcement learning

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Published In

cover image Applied Soft Computing

Applied Soft Computing Volume 49, Issue C

December 2016

1313 pages

ISSN:1568-4946

Issue’s Table of Contents

Copyright © Elsevier B.V.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 December 2016

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Cited By

Delgoshaei AAriffin MMaleki SLeman Z(2023)Review evolution of dual-resource-constrained scheduling problems in manufacturing systems: modeling and scheduling methods’ trendsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-023-09304-427:24(18489-18528)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s00500-023-09304-4
Oyewole GThopil G(2022)Data clustering: application and trendsArtificial Intelligence Review10.1007/s10462-022-10325-y56:7(6439-6475)Online publication date: 27-Nov-2022
https://dl.acm.org/doi/10.1007/s10462-022-10325-y
Bertolini MMezzogori DNeroni MZammori F(2021)Machine Learning for industrial applicationsExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.114820175:COnline publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1016/j.eswa.2021.114820
Delgoshaei AAli A(2020)A Hybrid Ant Colony Optimization and Simulated Annealing Algorithm for Multi-Objective Scheduling of Cellular Manufacturing SystemsInternational Journal of Applied Metaheuristic Computing10.4018/IJAMC.202007010111:3(1-40)Online publication date: 1-Jul-2020
https://dl.acm.org/doi/10.4018/IJAMC.2020070101
Sadeghi ASuer GSinaki RWilson D(2020)Cellular manufacturing design and replenishment strategy in a capacitated supply chain systemComputers and Industrial Engineering10.1016/j.cie.2020.106282141:COnline publication date: 1-Mar-2020
https://dl.acm.org/doi/10.1016/j.cie.2020.106282
Nalluri MKannan KGao XRoy D(2019)An efficient hybrid meta-heuristic approach for cell formation problemSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-019-03798-723:19(9189-9213)Online publication date: 1-Oct-2019
https://dl.acm.org/doi/10.1007/s00500-019-03798-7

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