Part 3: Allocation Algorithms — Greedy vs. Mixed-Integer Linear Programming
← Previous Chapter: Part 2: Real-Time Inventory | Series Hub | Next Chapter: Part 4: Anticipatory Shipping → Prerequisite: Familiarity with linear algebra, combinatorial optimization, graph theory (bipartite matching), and production Go microservice architectures. Answer-first: Selecting optimal fulfillment nodes across multi-facility omnichannel networks requires moving beyond myopic nearest-warehouse heuristics toward rigorous Mixed-Integer Linear Programming formulations. Solvers like HiGHS and Google OR-Tools formulate order routing as a Multi-Choice Knapsack Problem, factoring in split shipment penalties, labor throughput caps, and carrier cutoff times to achieve mathematically optimal allocations in under 35 milliseconds. ...