Active Learning of Piecewise Gaussian Process Surrogates
New paper has been published
Our simulation models investigate the production logistics.
Our machine learning applications optimize them.
The objective of the Simulation & Production Logistics (SimPL) Laboratory is to develop large-scale simulation models, operational algorithms, machine learning models that will improve the operations and productivity of logistics and related production systems. Active research areas include simulation optimization, material handling and self-organizing operations, and OR applications in warehousing, semiconductor, display, and automobile industries.
Business areas: Material handling and production logistics in distribution centers, container terminals, semiconductor and display fabs, and construction equipment assembly line
OR approaches: Large-scale simulations, machine learning models, and optimization models
Operational strategy: Simulation optimization, self-organizing/-balancing operations
New paper has been published
SimPL lab participated in the KIIE Fall Conference 2025
Professor Soondo Hong Receives the Baekam Academic Award at the 2025 KIIE Fall Conferenc
The book “Production Logistics System Simulation Modeling” by Soondo Hong and Bonggwon Kang has been published.