Gaussian process-based storage location assignments with risk assessments for progressive zone picking systems
New paper has been published in Computers & Industrial Engineering.
Welcome to the Simulation & Production Logistics laboratory!
Our simulation models investigate the production logistics.
Our machine learning applications optimize them.
Mission statement
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
Recent posts
Simulation optimization of collaborative handshake operations for twin overhead shuttle cranes in a rail-based automated container terminal under demand uncertainty
New paper has been published in IEEE Access.
Bayesian optimization for the vehicle dwelling policy in a semiconductor wafer fab
New paper has been published in IEEE Transactions on Automation Science and Engineering.
A simulation-based optimization for scheduling in a fab: comparative study on different sampling methods
New paper has been published in Journal of the Korea Society for Simulation.