An integrated methodology based on machine-learning algorithms for biomass supply chain optimisation

Duy Nguyen Duc and Narameth Nananukul

International Journal of Logistics Systems and Management, 2023, vol. 46, issue 1, 47-75

Abstract: This paper presents an integrated methodology for biomass supply chain planning, using a stochastic optimisation model and machine-learning algorithms. A methodology that integrates machine-learning algorithms with the optimisation process was proposed in order to generate solutions for large-scale supply chain optimisation problems. Models based on artificial neural network (ANN) and Bayesian network were developed by using the knowledge from previously-solved problems, to define good starting points for the search for solutions in the optimisation process. With this novel approach, the search space can be reduced and optimal solutions found with a shorter runtime. The applicability of the proposed approach was evaluated with a case study relating to biomass supply chain planning in the Central Vietnam region. The results from the proposed framework reveal that the optimal biomass plan for biomass supply chain can be determined with accuracy up to 96%, with a decrease in runtime by 37.19% on average.

Keywords: machine learning; optimisation; relaxation induced neighbourhood search; biomass supply chain planning; artificial neural network; Naïve Bayes; Bayesian network. (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijlsma:v:46:y:2023:i:1:p:47-75

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