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Average flow time estimation and its application for storage relocation in an order picking system
This study proposes flow time estimation model to optimize the storage location assignment.
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
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Sequential Optimization of a Temporary Storage Location for Cooperative Twin Overhead Shuttles in a Rail-based Automated Container Terminal
This paper propose a data-driven optimization approach for determining the handoff location to minimize the maximum workload of twin OSs.
A Study of a Video-based Simulation Input Modeling Procedure in a Construction Equipment Assembly Line
This study proposes a procedure for simulation input modeling using video data when it is difficult to collect enough input data to fit a probability distrib...
Gwanguk Han won an Outstanding Research Award from KSIE
SimPL Lab’s Ph.D. Candidate Gwanguk Han won an Outstanding Research Award from KSIE.