A literature review of supply chain analyses integrating discrete simulation modelling and machine learning

A literature review of supply chain analyses integrating discrete simulation modelling and machine learning

Článek WoS

Simulation and machine learning offer advanced methods to analyse complex flows, risks, and disruptions in supply chains. This literature review, based on a novel classification framework, traces the development of the research area from 2013 to 2025 and confirms intensified publication activities over the past 5 years. A majority of the analysed models merge discrete event simulation with reinforcement learning to cover an operational planning horizon and detailed to intermediate abstraction level. The comprehensive synthesis of 18 review articles, 72 research and conference papers, and 43 related studies explains integration approaches, discusses the current state of the art, and identifies research gaps. Existing individual limitations of discrete simulation and machine learning can be overcome by integrating those essential methods for supply chain analyses. This sets the stage for a new generation of models to plan, design, operate, control, and monitor supply chains in a sustainable, smart, and resilient way.

Simulation and machine learning offer advanced methods to analyse complex flows, risks, and disruptions in supply chains. This literature review, based on a novel classification framework, traces the development of the research area from 2013 to 2025 and confirms intensified publication activities over the past 5 years. A majority of the analysed models merge discrete event simulation with reinforcement learning to cover an operational planning horizon and detailed to intermediate abstraction level. The comprehensive synthesis of 18 review articles, 72 research and conference papers, and 43 related studies explains integration approaches, discusses the current state of the art, and identifies research gaps. Existing individual limitations of discrete simulation and machine learning can be overcome by integrating those essential methods for supply chain analyses. This sets the stage for a new generation of models to plan, design, operate, control, and monitor supply chains in a sustainable, smart, and resilient way.

discrete event simulation; agent-based simulation; supply chain management; logistics; transportation; supervised, unsupervised, and reinforcement learning

discrete event simulation; agent-based simulation; supply chain management; logistics; transportation; supervised, unsupervised, and reinforcement learning

KOGLER, C.; MAXERA, P.

20.05.2025

Journal of Simulation

20

01

Spojené království Velké Británie a Severního Irska

1

25

25

https://www.tandfonline.com/doi/full/10.1080/17477778.2025.2500393

http://hdl.handle.net/11012/251017