An imperialist competitive algorithm with memory for distributed unrelated parallel machines scheduling

Distributed scheduling becomes popular with the shift of production from single factory to multiple factories and reveals new features and the increasing optimisation difficulties. In this study, distributed unrelated parallel machines scheduling problem with makespan minimisation is considered in t...

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Bibliographic Details
Published in:International journal of production research Vol. 58; no. 2; pp. 597 - 614
Main Authors: Lei, Deming, Yuan, Yue, Cai, Jingcao, Bai, Danyu
Format: Journal Article
Language:English
Published: London Taylor & Francis 17.01.2020
Taylor & Francis LLC
Subjects:
Distributed memory
Evolutionary algorithms
Factories
imperialist competitive algorithm
Industrial plants
Lower bounds
Mica
multi-factory production network
Optimization
parallel machines
Production scheduling
Scheduling
ISSN:0020-7543, 1366-588X
Online Access:Get full text
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Summary:Distributed scheduling becomes popular with the shift of production from single factory to multiple factories and reveals new features and the increasing optimisation difficulties. In this study, distributed unrelated parallel machines scheduling problem with makespan minimisation is considered in the heterogeneous production network, which is directly simplified as an extended machine assignment. A novel imperialist competitive algorithm with memory (MICA) is presented, in which a machine assignment string is adopted and four neighbourhood structures and a global search operator are introduced. In each empire, some best colonies learn from a member of memory or imperialist and other colonies move toward imperialist or one of the best colonies, and revolution is newly implemented by using good solutions. Global search of imperialist is added into imperialist competition to avoid the addition of the weakest colony of the weakest empire into the winning empire. Lower bound is provided. Extensive experiments are conducted to test the performance of MICA and computational results show that MICA is a very competitive method for the considered problem.
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ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2019.1598596