Abstract: Systems and methods for optimizing delivery of loads with units are provided. A request may be received to provide an optimal match between the units (e.g., vehicles and drivers) and the loads for delivery. An optimization model is provided to receive the request, along with information about the load (e.g., load size, pick-up location, drop-off location, delivery window, etc.), the available units (e.g., capabilities of available delivery equipment, driver hours of service information, etc.), and any constraints to be applied to the request. The model generates a delivery schedule matching the plurality of loads and the plurality of units. In examples, delivery schedule is configured to minimize the overall deadhead miles for the plurality of units in delivering the plurality of loads and/or maximize usage of the plurality of drivers in delivering the plurality of loads.

Abstract: Systems and methods for determining an estimated time of arrival (ETA) and/or an on-time probability (OTP) metric are provided. For example, a request for an estimated time of arrival for a first load is requested. The request may include or reference scheduled delivery data. The scheduled delivery data may include information about the load and the driver and/or equipment scheduled to deliver the load. For example, driver hours of service information for the scheduled driver may be accessed. In addition, external data may be accessed, such as traffic and weather data. A trained machine-learning ETA model may be used to provide an ETA based on the load data, the external data, and information about the scheduled driver. In addition, a trained machine-learning OTP model may be provided to estimate a probability, based on the received information, of the load being delivered within a delivery window.