The method selects which tasks are given to which picker, a process which is called order batching. Pickers have different strengths and weaknesses: some are more skilled in driving the truck others excel in stacking items or are able to lift heavy items with ease. Statistical data analysis can be used to extract the skills of the pickers from process data, and to form mathematical models that can in turn be used to assign the right picker to the right job.
In order-picking the pickers drive around the warehouse and pick items according to customer orders. The machines are driven by humans, however humans are basically computer controlled through voice commanding system. It is a very labour intensive operation and can cause as much as 50% of the total warehousing operating costs. Furthermore, most of the order processing time is spent only on driving around the warehouse.
From a three months dataset from an order picking warehouse, recomputing and optimizing all the routes for the pickers using our method, resulted in average resulted 15% of savings. The other way to put the savings is that annually the savings is about 18000km, which means that for the given warehouse you would have to hire two tow-truck drivers just to drive around the warehouse to fulfill the saved kilometers. Additionally, if the skills of the pickers are taken into account, our method saves 9% in total order picking time when compared to the state-of-the-art solutions in optimizing order-picking processes
Marek Matusiak ja Pekka Forsman, Aalto University
Rocla Oy, Navitec Systems Oy, Konecranes Oyj, Aalto, TTY, VTT, FIMA Forum for Intelligent Machines ry