ScholarGate
Assistant

Comparer des méthodes

Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.

Modèle de Markov à base d'agents×Simulation à événements discrets (DES)×
DomaineSimulationSimulation
FamilleProcess / pipelineProcess / pipeline
Année d'origine2000s1960s (formalized); modern computational form from 1970s onward
Auteur d'origineHybrid approach synthesized from Bonabeau (ABM) and Norris/classical Markov chain literatureBanks, Carson, Nelson & Nicol (textbook lineage); foundational work by Tocher & Conway (1960s)
TypeHybrid simulation — agent-based modeling with Markov state transitionsStochastic process simulation
Source fondatriceBonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99(Suppl 3), 7280-7287. DOI ↗Banks, J., Carson, J.S., Nelson, B.L. & Nicol, D.M. (2010). Discrete-Event System Simulation (5th ed.). Pearson. ISBN: 978-0136062127
AliasABMM, Agent-Based Markov Chain Model, ABM-Markov hybrid, Agent Markov simulationDES, event-driven simulation, Ayrık Olay Simülasyonu (DES)
Apparentées54
RésuméThe Agent-Based Markov Model (ABMM) is a hybrid simulation framework that embeds Markov chain state-transition logic inside individual autonomous agents. Each agent independently samples its next state from a probability transition matrix, enabling the model to capture both micro-level heterogeneity across agents and the tractable probabilistic structure of Markov chains. The approach is widely used in health economics, epidemiology, social science, and operations research.Discrete-Event Simulation (DES) is a computational modeling paradigm in which the state of a system changes only at a countable sequence of points in time — the events. Between events nothing changes, so the simulation clock jumps directly from one event to the next. Formalized through the foundational textbooks of Banks, Carson, Nelson and Nicol and of Law in the 1960s–2000s, DES has become the standard tool for analyzing queuing systems, healthcare patient flows, manufacturing lines, and logistics networks where entities move through resources over time.
ScholarGateJeu de données
  1. v1
  2. 2 Sources
  3. PUBLISHED
  1. v1
  2. 2 Sources
  3. PUBLISHED

Aller à la recherche Télécharger les diapositives

ScholarGateComparer des méthodes: Agent-based Markov model · Discrete-Event Simulation. Consulté le 2026-06-17 sur https://scholargate.app/fr/compare