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
This paper aims to develop an order batching and sequencing model for zone work balancing (OBZ) in a sequential zone order picking system (OPS) as means of r...
This study introduces an order-assorting system (OAS) in a distribution center which supports assortments with worker-following carts.
This paper proposes an order batching formulation and heuristic solution procedure appropriate for a large-scale order picking situation in a parallel-aisle ...
This paper aims to address a route-set for the S-shape routes and composites a best fit route for batches from the predefined S-shape routes while partitioni...
