MARCH - 2021 9logisticstechoutlook.comgood and very bad ways to assign robots to items. Extra intelligence applied in this context can be the difference between fantastic return on investment and unacceptable returns.Yet, often the controls algorithms applied in these situations look more like simple conveyor controls than intelligent algorithms. Developers rely on heuristics and rules of thumb. A typical rule might be to assign the closest idle robot to the item. That sounds logical enough. But will that simple rule ensure that we avoid congestion, keep all of the pack stations utilized, or minimize the total distance traveled by the robot fleet? Certainly not. Yet, so many multi-agent, multi-task systems deployed in the world today rely on exactly these types of simplistic heuristics. It is well known that humans are great at pattern recognition and instinctively make good decisions on some types of problems. It is also well understood that humans have cognitive biases that cause us to get it wrong at times. Asking a computer to follow rules-of-thumb-based heuristics is not much different than asking a super-fast human to make all of the decisions. Contrast that with a deep learning algorithm that can adjust its strategy slightly with every movement of a tote and every completion of an order.The autonomous mobile robot vendors are farther along this curve. My challenge is to the vendors of systems with multiple ASRS cranes, shuttles, pick stations, and carousels where there are many potential execution paths at any given time. Apply AI to yoursystems and see how much better your performance can be.
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