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- W4384789849 abstract "This work explores how artificial intelligence and machine learning (ML) together with rigorous optimization models can help in optimizing electronic bill of materials. We propose a mixed-integer linear programming (MILP) model that considers different real-life restrictions required to complete a customer BoM. Furthermore, we implement a data-driven approach to find a set of alternates for a given component based on data mining and machine learning techniques. With these techniques, the BoM is completed with the most similar alternates that have the same form, fit, and functionality as the given component. The results from a real-life use case illustrate that the proposed AI-enhanced optimization model leads to superior results in terms of solution quality (lower price and shorter lead times) in comparison to the standalone MILP model, with the highlight being a 4.4% improvement in the total cost and a 24% reduction in the lead time." @default.
- W4384789849 created "2023-07-20" @default.
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- W4384789849 date "2023-01-01" @default.
- W4384789849 modified "2023-10-16" @default.
- W4384789849 title "AI-enhanced BoM Optimization with MILP for the EMS Industry" @default.
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- W4384789849 doi "https://doi.org/10.1016/b978-0-443-15274-0.50051-2" @default.
- W4384789849 hasPublicationYear "2023" @default.
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