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Neural Computing and Applications

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06.01.2021 | Original Article

Optimization of decoupling point position using metaheuristic evolutionary algorithms for smart mass customization manufacturing

verfasst von: C. D. James, Sandeep Mondal

Erschienen in: Neural Computing and Applications | Ausgabe 17/2021

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Abstract

In this paper, we present two metaheuristic evolutionary algorithms-based approaches to position the customer order decoupling point (CODP) in smart mass customization (SMC). SMC tries to autonomously mass customize and produce products per customer needs in Industry 4.0. SMC shown here is from the perspective of arriving at a CODP during manufacturing process flow designs meant for fast moving and complex product variants. Learning generally needs several repetitive cycles to break the complexity barrier. We make use of fruit fly and particle swarm optimization (PSO) evolutionary algorithms with the help of MATLAB programming to constantly search better fitting consecutive process modules in manufacturing chain. CODP is optimized by increasing modularity and reducing complexity through evolutionary concept. Learning-based PSO iterations are performed. The methods shown here are recommended for process flow design in a learning-oriented supply chain organization which can involve in-house and outsourced manufacturing steps. Finally, a complexity reduction model is presented which can aid in deploying this concept in design of supply chain and manufacturing flows.

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Titel: Optimization of decoupling point position using metaheuristic evolutionary algorithms for smart mass customization manufacturing
verfasst von: C. D. James
Sandeep Mondal
Publikationsdatum: 06.01.2021
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 17/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-020-05657-1

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