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Neural Computing and Applications

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16-11-2016 | Original Article

A hybrid differential evolution algorithm with estimation of distribution algorithm for reentrant hybrid flow shop scheduling problem

Authors: Bing-hai Zhou, Li-man Hu, Zhen-yi Zhong

Published in: Neural Computing and Applications | Issue 1/2018

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Abstract

This paper proposes a reentrant hybrid flow shop scheduling problem where inspection and repair operations are carried out as soon as a layer has completed fabrication. Firstly, a scheduling problem domain of reentrant hybrid flow shop is described, and then, a mathematical programming model is constructed with an objective of minimizing total weighted completion time. Then, a hybrid differential evolution (DE) algorithm with estimation of distribution algorithm using an ensemble model (eEDA), named DE–eEDA, is proposed to solve the problem. DE–eEDA incorporates the global statistical information collected from an ensemble probability model into DE. Finally, simulation experiments of different problem scales are carried out to analyze the proposed algorithm. Results indicate that the proposed algorithm can obtain satisfactory solutions within a short time.

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Title: A hybrid differential evolution algorithm with estimation of distribution algorithm for reentrant hybrid flow shop scheduling problem
Authors: Bing-hai Zhou
Li-man Hu
Zhen-yi Zhong
Publication date: 16-11-2016
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 1/2018
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-016-2692-y

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