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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26610%2F26%3A0197890" target="_blank" >RIV/00216305:26610/26:0197890 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.tandfonline.com/doi/full/10.1080/17477778.2025.2500393" target="_blank" >https://www.tandfonline.com/doi/full/10.1080/17477778.2025.2500393</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/17477778.2025.2500393" target="_blank" >10.1080/17477778.2025.2500393</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Simulation

ISSN

1747-7778

e-ISSN

1747-7786

Svazek periodika

20

Číslo periodika v rámci svazku

01

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

25

Strana od-do

1-25

Kód UT WoS článku

001491630900001

EID výsledku v databázi Scopus

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