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    Haladir builds simulations to help Nomos AI map logistics operations

    A Haladir team member says the simulations are tuned for third-party logistics, distributor and retail operations.

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    TLDR

    A Haladir team member says the company built simulations covering fulfillment, sortation and transportation. The aim is to give Nomos AI data from live management systems and simulations so it can learn to tune an optimizer’s constraints to better match changing operations. The team member says optimization can produce unrealistic answers when its model doesn’t match the operation.

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    2 Sources, first seen 6h ago

    Combined views

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    2 Sources, first seen 6h ago

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    6h ago
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    2 Sources

    @thehutchmannAt @Haladirofficial, when building and deploying Nomos AI across complex logistics operations, we realized that the one thing holding back both the logistics and physical AI industries writ large was accuracy: how accurate can a company map its own operation. To aid in both development and deployment across our existing customer relationships, we've built several discrete event simulations tuned for 3PL, distributor, and retail operations, spanning fulfillment, sortation, transportation, and more. The reason? To make Nomos AI smarter, not by training it solely on the outcomes of the decisions it makes, but to train it on the dynamic mapping of the operation itself. By giving Nomos data from live management systems and a live simulation based on those systems, Nomos can learn how to tune a global optimizer’s constraints to more closely resemble the actual conditions of the operation. MILP solvers and other methods of global optimization are great at coming up with optimal answers, but they can easily produce inaccurate or unrealistic solutions when the model doesn’t match the operation. By teaching Nomos to continuously tune the model to accurately represent the evolving ground truth, decision making becomes a lot easier to trust. See more here: https://www.haladir.com/demo7h
    @JoshuabrowderRT @thehutchmann: At @Haladirofficial, when building and deploying Nomos AI across complex logistics operations, we realized that the one t…1h
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    2 Sources

    @thehutchmannAt @Haladirofficial, when building and deploying Nomos AI across complex logistics operations, we realized that the one thing holding back both the logistics and physical AI industries writ large was accuracy: how accurate can a company map its own operation. To aid in both development and deployment across our existing customer relationships, we've built several discrete event simulations tuned for 3PL, distributor, and retail operations, spanning fulfillment, sortation, transportation, and more. The reason? To make Nomos AI smarter, not by training it solely on the outcomes of the decisions it makes, but to train it on the dynamic mapping of the operation itself. By giving Nomos data from live management systems and a live simulation based on those systems, Nomos can learn how to tune a global optimizer’s constraints to more closely resemble the actual conditions of the operation. MILP solvers and other methods of global optimization are great at coming up with optimal answers, but they can easily produce inaccurate or unrealistic solutions when the model doesn’t match the operation. By teaching Nomos to continuously tune the model to accurately represent the evolving ground truth, decision making becomes a lot easier to trust. See more here: https://www.haladir.com/demo7h
    @JoshuabrowderRT @thehutchmann: At @Haladirofficial, when building and deploying Nomos AI across complex logistics operations, we realized that the one t…1h