Abstract: A device may receive schedule data identifying schedules of appointments for drivers of vehicles and may receive location data identifying geographical locations of the vehicles. The device may receive traffic data identifying traffic conditions, and may process the received data, with a first model, to determine status data identifying estimated statuses of the appointments. The device may process the received data, with a second model, to generate sets of isochrones for destinations of the appointments, and may calculate, based on the sets of isochrones and the location data, estimated travel time data identifying estimated travel times for the appointments. The device may process the status data, the estimated travel time data, and the schedule data, with a third model, to calculate estimated time of arrival data identifying estimated times of arrival for the appointments, and may perform actions based on the estimated time of arrival data.

Abstract: A routing optimization system may determine a plurality of routes based on one or more changes to an initial route. Each change generates a respective route of the plurality of routes. The routing optimization system may store route information for each route. The route information, for each route, includes cost information identifying a cost associated with the route. The route information is stored in a respective entry of a data structure. The routing optimization system may determine that a particular route, associated with a lowest cost out of costs associated with the plurality of routes, is to be selected from the plurality of routes. The routing optimization system may determine whether an entry, of a plurality of entries associated with the plurality of routes, is empty. The routing optimization system may select the particular route based on the plurality of entries after determining that the entry is not empty.

Abstract: A load planning platform may generate, according to one or more loading rules, a preliminary packing solution that simulates placing unpacked items, in a set of items, into a container. The load planning platform may generate a set of packing solutions by applying one or more available moves to the preliminary packing solution. The load planning platform may select a final packing solution from the set of packing solutions based on one or more optimization criteria associated with the container and the set of items. The load planning platform may provide access to a three-dimensional rendering of the final packing solution that differentiates each item in the set of items based on a sequence in which the set of items are to be unloaded from the container.

Abstract: A device may generate a shortest path tree based on a passenger starting point identified in driving data. The device may generate a graph with a first layer and a second layer that correspond to the shortest path tree and may add paths from nodes in the first layer to corresponding nodes in the second layer. The device may identify a first shortest path that starts from a driver starting node in the first layer and ends at a passenger end node in the second layer and a second shortest path that starts from the passenger end node and ends at a driver end node in the second layer. The device may calculate a shared cost associated with the driver and the passenger sharing a ride based on the first shortest path and the second shortest path. The device may generate a recommendation based on the shared cost.

Abstract: In some implementations, a device may receive vehicle data associated with a plurality of vehicles and schedule data associated with a plurality of jobs. The device may determine a set of scores for each job, of the plurality of jobs based on the vehicle data and the schedule data, wherein the set of scores for each job, of the plurality of jobs, indicates a set of likelihoods that each event is associated with each job. The device may determine a set of assignments of events to jobs based on the set of scores for each job. The device may determine a calculated job time period for each job based on the set of assignments. The device may modify, based on the calculated job time periods, the schedule data generate modified schedule data. The device may generate new schedule data for a new job based on the modified schedule data.

Abstract: A system described herein may provide a technique for the identification of solutions, such as routing solutions based on a starting point and a destination, based on Key Performance Indicators (“KPIs”) associated with models that are included in, or are associated with, candidate solutions. A solution request may specify a set of KPI preferences, based on which certain KPIs may be more heavily weighted than others in the identification of a suitable solution. Multiple solutions may be identified, with measures of comparative differences or similarities identified. A requestor may be able to make an informed decision between multiple candidate solutions based on the identification of the comparative differences.

Abstract: A device can receive, from a client device, a request to modify a schedule that includes information identifying the set of deliveries that the fleet of vehicles is to perform. The device can generate a set of modified schedules using information included in the request and one or more routing techniques. The device can determine, for each modified schedule, of the set of modified schedules, one or more scores for ranking the set of modified schedules, such as a projected delivery time score, an operational cost score, and/or an overall score. The device can provide the set of modified schedules that have been scored to the client device. The device can receive a modified schedule that has been selected by the client device. The device can deploy the selected modified schedule to instruct the fleet of vehicles to perform an updated set of deliveries associated with the selected modified schedule.

Abstract: A device may receive schedule data identifying schedules of appointments for drivers of vehicles and may receive location data identifying geographical locations of the vehicles. The device may receive traffic data identifying traffic conditions, and may process the received data, with a first model, to determine status data identifying estimated statuses of the appointments. The device may process the received data, with a second model, to generate sets of isochrones for destinations of the appointments, and may calculate, based on the sets of isochrones and the location data, estimated travel time data identifying estimated travel times for the appointments. The device may process the status data, the estimated travel time data, and the schedule data, with a third model, to calculate estimated time of arrival data identifying estimated times of arrival for the appointments, and may perform actions based on the estimated time of arrival data.

Abstract: A load planning platform may generate, according to one or more loading rules, a preliminary packing solution that simulates placing unpacked items, in a set of items, into a container. The load planning platform may generate a set of packing solutions by applying one or more available moves to the preliminary packing solution. The load planning platform may select a final packing solution from the set of packing solutions based on one or more optimization criteria associated with the container and the set of items. The load planning platform may provide access to a three-dimensional rendering of the final packing solution that differentiates each item in the set of items based on a sequence in which the set of items are to be unloaded from the container.

Abstract: A device may generate a shortest path tree based on a passenger starting point identified in driving data. The device may generate a graph with a first layer and a second layer that correspond to the shortest path tree and may add paths from nodes in the first layer to corresponding nodes in the second layer. The device may identify a first shortest path that starts from a driver starting node in the first layer and ends at a passenger end node in the second layer and a second shortest path that starts from the passenger end node and ends at a driver end node in the second layer. The device may calculate a shared cost associated with the driver and the passenger sharing a ride based on the first shortest path and the second shortest path. The device may generate a recommendation based on the shared cost.

Abstract: A load planning platform generates a preliminary packing solution based on loading constraints for a three-dimensional container and parameters for a set of items to be loaded into the three-dimensional container, wherein the preliminary packing solution simulates placing unpacked items into the three-dimensional container according to one or more loading rules based on a sequence in which the set of items are to be unloaded from the three-dimensional container. The load planning platform generates a set of packing solutions by applying one or more available moves changing a simulated placement for one or more items in the set of items. The load planning platform selects a final packing solution from the set of packing solutions based on one or more optimization criteria and provides access to a three-dimensional rendering of the final packing solution and instructions for implementing the final packing solution.

Abstract: A load planning platform generates a preliminary packing solution based on loading constraints for a three-dimensional container and parameters for a set of items to be loaded into the three-dimensional container, wherein the preliminary packing solution simulates placing unpacked items into the three-dimensional container according to one or more loading rules based on a sequence in which the set of items are to be unloaded from the three-dimensional container. The load planning platform generates a set of packing solutions by applying one or more available moves changing a simulated placement for one or more items in the set of items. The load planning platform selects a final packing solution from the set of packing solutions based on one or more optimization criteria and provides access to a three-dimensional rendering of the final packing solution and instructions for implementing the final packing solution.

Abstract: A device can receive, from a client device, a request to modify a schedule that includes information identifying the set of deliveries that the fleet of vehicles is to perform. The device can generate a set of modified schedules using information included in the request and one or more routing techniques. The device can determine, for each modified schedule, of the set of modified schedules, one or more scores for ranking the set of modified schedules, such as a projected delivery time score, an operational cost score, and/or an overall score. The device can provide the set of modified schedules that have been scored to the client device. The device can receive a modified schedule that has been selected by the client device. The device can deploy the selected modified schedule to instruct the fleet of vehicles to perform an updated set of deliveries associated with the selected modified schedule.

Abstract: A method for finding an approximation to the all-pairs shortest travel path between a number of predetermined nodes, comprising clustering nodes of an original road network into a plurality of subgroups so that the number of nodes in each subgroup is no greater than a first predetermined threshold and the maximum distance between any two nodes of a subgroup is no greater than a second predetermined threshold; adding information of intra group shortest paths for all pairs in a same subgroup to a newly created higher level road network; adding information of inter group shortest paths for the plurality of subgroups to the same road network; and searching the same road network for the shortest travel path. In those cases in which the path returned is not exact, the path represents one in the original map, even if not necessarily the best one.

Abstract: A method for finding an approximation to the all-pairs shortest travel path between a number of predetermined nodes, comprising clustering nodes of an original road network into a plurality of subgroups so that the number of nodes in each subgroup is no greater than a first predetermined threshold and the maximum distance between any two nodes of a subgroup is no greater than a second predetermined threshold; adding information of intra group shortest paths for all pairs in a same subgroup to a newly created higher level road network; adding information of inter group shortest paths for the plurality of subgroups to the same road network; and searching the same road network for the shortest travel path. In those cases in which the path returned is not exact, the path represents one in the original map, even if not necessarily the best one.