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Neural Computing and Applications
Published in: Neural Computing and Applications 11/2020

23-05-2019 | Original Article

Network design for resilience in supply chains using novel crazy elitist TLBO

Author: R. Rajesh

Published in: Neural Computing and Applications | Issue 11/2020

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Abstract

A resilient plant location model is proposed in this research and has been evaluated for a case problem. The model considers three major indicators of network resilience, viz. node density, node complexity and node criticality. A resilient design could ensure for cost efficiency, apart from that the likelihood of potential disruptions due to bottlenecks could be minimized. The results were optimized using a novel crazy elitist TLBO algorithm. The algorithm has been presented to solve the case problem and has been pretested for a constrained and unconstrained test function. A multi-objective decision-making model has been constructed with the flow of products as variables and was effectively solved using the meta-heuristic. The solution to the case brings insights into the design of supply network for resilience, and the managers are recommended to incorporate the concepts of resilience from the design phase itself.
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Appendix
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Metadata
Title
Network design for resilience in supply chains using novel crazy elitist TLBO
Author
R. Rajesh
Publication date
23-05-2019
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 11/2020
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04260-3

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