Solving Matching Problems in Practical Applications Using JuMP

Explore a 13-minute talk on solving matching problems in practical applications using JuMP, presented by The Julia Programming Language. Dive into the complexities of optimization problems in supply chain, logistics, and planning applications, and learn how they can be formulated as matching problems. Discover why traditional approaches like LIFO, FIFO, and spreadsheet tools fall short in incorporating practical constraints and producing optimal plans. Gain insights from real-world partnerships with large customers managing complex supply chains, and see how JuMP was introduced to solve a matching problem in a dense transportation network. Understand the importance of leveraging domain knowledge to formulate problems efficiently, ensuring scalability and optimal/near-optimal solutions while honoring all constraints. Learn about the concept of "connections" in transportation schedules and their critical impact on key supply chain metrics. Explore how the model strategically distributes connection attributes to optimize performance. Discover the advantages of implementing the model in JuMP compared to initial explorations in Python, and gain valuable lessons from this industrial application of matching problems.