Vol. 5 No. 1 (2025): Issue 5
Articles

A Numerical Study on Supply Chain Optimization with Dynamic Bayesian Networks

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  • Elin Andersson,
  • Olof Gustafsson,
  • Astrid Nilsson
Elin Andersson
School of Business and Engineering, Halmstad University, Halmstad, 301 18, Sweden
Olof Gustafsson
Department of Computer Science and Engineering, Blekinge Institute of Technology, Karlskrona, 371 79, Sweden
Astrid Nilsson
Logistics and Transport Research Group, University of Skövde, Skövde, 541 28, Sweden
DOI: https://doi.org/10.71070/oaml.v5i1.84

Published 2025-01-13

Keywords

  • Supply Chain,
  • Optimization,
  • Dynamic Bayesian Networks,
  • Framework Development,
  • Performance Enhancement

How to Cite

Andersson, E., Gustafsson, O., & Nilsson, A. (2025). A Numerical Study on Supply Chain Optimization with Dynamic Bayesian Networks. Optimizations in Applied Machine Learning, 5(1). https://doi.org/10.71070/oaml.v5i1.84
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Abstract

This paper presents a numerical study on supply chain optimization using Dynamic Bayesian Networks. The optimization of supply chains is crucial in today's complex and dynamic business environment. However, existing research faces challenges in effectively modeling and optimizing supply chain processes due to their dynamic and uncertain nature. This study addresses this gap by introducing Dynamic Bayesian Networks as a novel approach to model the relationships and uncertainties in supply chain operations. The innovative aspect of this work lies in the development of a framework that integrates Dynamic Bayesian Networks with optimization algorithms to enhance supply chain performance. The findings of this research provide valuable insights for practitioners and researchers seeking to improve supply chain efficiency and resilience.

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