Optimizing Warehouse Forklift Dispatching Using a Sensor Network and Stochastic Learning

The authors report on a successful deployment of an inexpensive mobile wireless sensor network in a commercial warehouse served by a fleet of forklifts. The aim is to improve forklift dispatching and reduce the costs associated with the delays of loading/unloading delivery trucks. To that end, an integrated system including both hardware and software is constructed. First, the forklifts are instrumented with sensor nodes that collect an array of information, including the forklifts´ physical location, usage time, bumping/collision history, and battery status. The hardware´s capability is enhanced with a theoretically sound hypothesis testing technique to capture the rather elusive location information, and the collection of the data is done in an efficient event-driven manner. The information acquired combined with inventory information is fed into a sophisticated actor-critic type stochastic learning method to generate dispatching recommendations. Because noise is inevitable in such wireless sensor networks, the performance of the algorithm is investigated under different noise levels. In combining wireless sensing with state-of-the-art decision theory, this work extends beyond the standard use of wireless sensor networks as monitoring devices.