ScholarGate
Assistent
Process / pipelineSimulation / optimization

Agent-Based Integer Programming — Hybrid Simulering-Optimering for Diskrete Beslutningssystemer

Agent-Based Integer Programming (ABIP) kombinerer den adfærdsmæssige rigdom af agent-baseret modellering med den kombinatoriske stringens af heltalsoptimering. Individuelle agenter forfølger lokale mål, mens en global IP-løser håndhæver diskrete feasibility-betingelser, hvilket muliggør realistisk modellering af multi-aktør-systemer, hvor beslutninger skal være heltalsværdier — såsom ressourceallokering, planlægning og netværksdesign under fremvoksende interaktionseffekter.

Åbn i MethodMindSnartVideoSnartDownload slides

Læs hele metoden

Kun for medlemmer

Log ind med en gratis konto for at læse dette afsnit.

Log ind

Method map

The neighbourhood of related methods — select a node to explore.

Kilder

  1. Wooldridge, M. (2009). An Introduction to MultiAgent Systems (2nd ed.). Wiley. ISBN: 9780470519462
  2. Macal, C. M., & North, M. J. (2010). Tutorial on agent-based modelling and simulation. Journal of Simulation, 4(3), 151-162. DOI: 10.1057/jos.2010.3

Sådan citerer du denne side

ScholarGate. (2026, June 3). Agent-Based Integer Programming — Hybrid optimization integrating agent-based modeling with integer programming. ScholarGate. https://scholargate.app/da/simulation/agent-based-integer-programming

Which method?

Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.

Compare side by side
ScholarGateAgent-based integer programming (Agent-Based Integer Programming — Hybrid optimization integrating agent-based modeling with integer programming). Hentet 2026-06-15 fra https://scholargate.app/da/simulation/agent-based-integer-programming · Datasæt: https://doi.org/10.5281/zenodo.20539026