Revolutionizing order fulfillment: A neural network approach to optimal warehouse selection in supply chain simulation

Simulation plays a central role in the strategic planning and operational evaluation of supply chain networks. Within these networks, order fulfillment traditionally requires solving computationally expensive optimization problems in real-time across multiple constraints. For forward-looking simulations evaluating millions of orders, such optimization becomes prohibitively expensive. We develop a neural network-based emulator that approximates optimal fulfillment decisions while maintaining millisecond-level inference speed. Operating at ZIP-code level resolution and incorporating shipping speed constraints, our model handles exponential decision spaces and non-stationary patterns. Empirical results demonstrate 56.75% order-level accuracy, a 20 percentage point improvement over benchmarks. Through novel regularization balancing order-level and network-level efficiency, we achieve 47.13% node-level accuracy while maintaining 50.31% order-level accuracy. Our model captures intricate patterns in historical fulfillment data, enabling efficient forward-looking simulation for strategic planning.