In Machine Learning

Online retailers are surpassing brick and mortar retail, creating a vast demand for fulfillment warehouses. As described in recent New York Times article, many of these fulfillment warehouses take over abandoned or discarded manufacturing facilities, bringing jobs back to previously economically depressed areas – areas where inexpensive land and labor help keep contribute to a competitive cost structure.

These locations also often have the advantage of close proximity to transportation networks (highway, rail, air, water). Distribution hubs anchor the transportation networks, processing inbound and outbound shipments to and from warehouses. For the transport networks to function smoothly and meet delivery promises, it’s crucial that these hubs don’t become the choke points in the network. For the transport companies operating these networks, these hubs also need to contribute to the value chain’s competitive cost structure. This is where machine learning analytics play an important role.

Reinforcement machine learning analytics ensure that the hubs’ daily operations are simultaneously optimized for both shipments in and out of the hub, keeping the warehouses efficiently stocked with the goods needed to fulfill orders, and at the same time, fulfilling those orders to end customers. Machine learning excels at optimizing massively complex, highly dynamic processes – perfect for the near real-time requirements of organizing the daily operations of a hub’s operations.

The reinforcement learning system finds the most efficient means of routing each package through the hub without explicitly building a specific optimal route into the system. Understanding each shipment’s origin, destination, and service requirements, taking into account the hub’s available resources, and factoring in external constraints such as weather, the system determines the most efficient way of processing every shipment through the hub and onto its final destination. Over time, the system continually improves the hub’s performance as it uses previously-learned efficiencies in the system’s calculations.

Optimizing shipment routing is essential, but not enough. To drive peak performance in a distribution hub, the resources that perform and manage the hub’s operations also must be optimized; again a challenge for which reinforcement learning is up to the task. Worker’s schedules, planned and unplanned leave, union rules,  availability of material handling equipment such as fork lifts, safety compliance regulations, and other predictable and unpredictable variables contribute to a jumble of decisions that reinforcement learning can bring to order, optimize, and continuously improve.

Using a reinforcement learning system, both the operations of a logistics hub and the inflows/outflows of the hub can be optimized. SpaceTime has authored a white paper describing how you can put reinforcement learning to work optimizing your business operations. You may download the paper here (registration required).

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