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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 11/2017

02.03.2016 | Original Article

An evolutionary algorithm for supply chain network design with assembly line balancing

verfasst von: Çağrı Koç

Erschienen in: Neural Computing and Applications | Ausgabe 11/2017

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Abstract

This paper investigates the combined impact of assembly line balancing decisions within a supply chain network design. The aim of the problem is to design a supply chain network between manufacturers, assemblers, and customers for specific periods, as well as balancing the assembly lines in assemblers. The main objective is to minimize the sum of transportation costs and fixed costs of stations in assemblers. Solving this problem poses several methodological challenges. To this end, the paper developed a powerful evolutionary algorithm (EA) which was successfully applied to a large pool of benchmark instances. The EA solved instances with up to 140 manufacturers and customers, and with up to 130 assemblers. Computational analyses are performed to empirically calculate the effect of various problem parameters, such as total cost, transportation cost and number of stations. The EA is validated on benchmark instances where it provides competitive solutions. Several managerial insights are also presented.
Vorheriger Artikel Berth and quay crane coordinated scheduling using multi-objective chaos cloud particle swarm optimization algorithm
Nächster Artikel Recipient size estimation for induction heating home appliances based on artificial neural networks

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Metadaten
Titel
An evolutionary algorithm for supply chain network design with assembly line balancing
verfasst von
Çağrı Koç
Publikationsdatum
02.03.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 11/2017
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-016-2238-3

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