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OWL - Optimization and Learning for Warehouse Logistics

The OWL project will improve human working conditions in wholesale warehouses and reduce environmental and economic cost across the value chain by developing better tools for optimizing the packing of pallets and internal warehouse layout.

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Photo: Solwr Group AS

In wholesale warehouses items arrive from manufacturers on standard-sized pallets in single-item stacks, which are stowed away in the warehouse. Items are then retrieved and stacked in mixed-item stacks according to customer orders before they are transported to the stores. The retrieval and restacking related activities are co-dependent, and jointly considering up- and down-streams operations is key to aid and/or replace hard human labor with robots. OWL will address the lack of algorithms for solving these complex problems that prevents further development and implementation of digital solutions and feasible and affordable autonomous systems in warehouse operation.

The OWL consortium brings togetherSolwr Group AS a warehouse logistics software provider and developer of innovative warehouse robots, in a collaboration with SINTEF Digital to develop advanced optimization models for tackling challenges in wholesale warehouse management. The enhancement of classical optimization models with use of machine learning will be a new and promising venue for progress. Insights from end users in both warehouses and stores will be collected from Solwr Group AS' customers, and the final methods will be exploited in the warehouse robot and the management software.

Reduction of hard human labor, improved working conditions, increased safety in operations, reduced waste, area usage and emissions are likely impacts, contributing towards UN sustainable development goals, and to the renewal and restructuring of an established industry (warehouse logistics) and its work methods through digitalization.

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