Large-scale order batching in parallel-aisle picking systems
This paper proposes an order batching formulation and heuristic solution procedure appropriate for a large-scale order picking situation in a parallel-aisle ...
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
A route-selecting order batching model with the S-shape routes in a parallel-aisle order picking system
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...
Batch picking in narrow-aisle order picking systems with consideration for picker blocking
This paper delves into strategies controlling picker blocking delay of the batch picking in a narrow-aisle order picking system by proposing an integrated ba...
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