Abstract: A system including a processor and a non-transitory computer-readable media storing computing instructions that, when executed on the processor, cause the processor to perform certain operations: obtaining a distribution of transit miles for inbound loads located within a distance of each facility of multiple facilities; creating, using a partition engine, multiple spatial partitions within the distance of each facility of the multiple facilities; determining, using the partition engine, a multi-partition distance by combining multiple spatial partitions with overlapping distances; determining shipment consolidation options for the inbound loads within the multi-partition distance of the multiple facilities; and generating a transit plan corresponding to the inbound loads located within the multi-partition distance, based on the shipment consolidation options. Other embodiments are described.

Abstract: A system including a processor and a non-transitory computer-readable media storing computing instructions that, when executed on the processor, cause the processor to perform certain operations: receiving input information corresponding to allocating one or more containers in one or more trucks for delivery from a ship point to one or more distribution centers, the one or more containers including one or more items; determining one or more lanes that correspond to one or more delivery routes for the one or more trucks between the ship point and the one or more distribution centers based on the input information; determining a respective priority measurement for each of the one or more trucks; determining a respective route plan for each respective truck of the one or more trucks based on the respective priority measurement, the respective route plan including a respective listing of each of the one or more trucks and an associated lane from the one or more lanes the respective truck is to navigate to one of

Abstract: A method including obtaining information about a batch of loads. The method also can include determining one or more respective alternative assignments that are feasible for each of the loads. The method additionally can include generating an assignment based on selecting a respective selected carrier for each of the loads from among the one or more respective alternative assignments for each of the loads. Generating the assignment can include using respective deviation scores when there are multiple primary carriers among the one or more respective alternative assignments. The method further can include modifying the assignment for a subset of the loads based on a flex rate. The method additionally can include outputting the assignment, as modified. Other embodiments are described.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform: receiving input information for generating one or more routes for one or more vehicles, the input information including one or more group classifiers; processing the input information based on the one or more group classifiers; analyzing, using one or more solving engines, the input information to generate the one or more routes for the one or more vehicles; selecting, from each of the one or more solving engines, a vehicle route from the one or more routes that satisfies a threshold; and transmitting the vehicle route to a dispatcher to facilitate coordinating, by the dispatcher, operation of a vehicle from the one or more vehicles along the vehicle route. Other embodiments are disclosed herein.

Abstract: A system including a processor and a non-transitory computer-readable media storing computing instructions that, when executed on the processor, cause the processor to perform certain operations: splitting a set of candidate loads into subsets of candidate loads, wherein time windows of the subsets of candidate loads align with time ranges of a facility; iteratively reducing, without integer constraints, the subsets of candidate loads into a number of candidate loads; determining multiple candidate loads from the number of candidate loads that optimize an objective function with the integer constraints; and selecting at least one candidate load from the multiple candidate loads corresponding to a mode of transportation. Other embodiments are described.

Abstract: Systems and methods of generating a stacking solution for a three-dimensional base element are disclosed. A stacking solution generate generation request is received identifying a base element and a set of item containers and a plurality of bundles are generated. Each bundle of the plurality of bundles includes one or more of the containers selected from the set of item containers. Each of the plurality of bundles is assigned to a position within a stacking window of the base element and a stacking solution is generated including a sequential pick order for each of item containers in the set of item containers based on the assignment of the plurality of bundles within the stacking window.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform certain operations. The operations can include determining an estimated arrival time of a user at a physical store. The operations also can include generating an estimated wait time using a machine learning model and based on input data comprising the estimated arrival time and dynamic wait time data for the physical store. The operations additionally can include sending the estimated wait time to at least one of the physical store or a mobile device of the user. Other embodiments are disclosed.

Abstract: A method including obtaining information about a trailer that has been partially loaded with preloaded stacks in a manner that deviates from an original load design. The trailer is loaded with stacks of pallets including (i) the preloaded stacks that have already been loaded in the trailer and (ii) unloaded stacks that have not yet been loaded into the trailer. The method also can include determining positions of empty floor spots remaining in the trailer. The method additionally can include determining a first portion of an incremental load design for the unloaded stacks using a gap-filling pattern behind an uneven rear edge of the preloaded stacks in the trailer. The method further can include determining a second portion of the incremental load design.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations: generating a preliminary dock-out time boundary based on one or more delivery time windows for one or more deliveries; processing two parallel data flow paths to locate a range of dock-out times associated with one or more time constraints of the one or more delivery time windows; and transmitting the range of dock-out times to a user, wherein a trailer is dispatched to a distribution center between the range of dock-out times to load the trailer and deliver the one or more deliveries within the time constraints of the one or more delivery time windows based on a flexible period of time. Other embodiments are described.

Abstract: A method can include training a machine learning module based on training data to determine a load threshold for to-be-dispatched delivery vehicles, wherein the training data is based on historical load thresholds and historical delivery performance data of delivery vehicles. The method further can include generating one or more clusters for nodes based, at least in part, on (a) location information of the nodes from order data and (b) load capacity information of the delivery vehicles. Also, the method can include reassigning the nodes to the one or more clusters. Additionally, the method can include transmitting the one or more delivery routes, as determined and re-determined, to be displayed on one or more user devices. Moreover, the method can include re-training the machine learning module. Other embodiments are disclosed.

Abstract: A method including setting a preferred delivery route as an initial delivery route comprising original delivery stops.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors perform certain functions: obtaining an input comprising a sequence of service locations; iterating through pairs of consecutive service locations of the service locations to determine when a port for the first service location is different from the port for the second service location; adding the port for the first service location and the port for the second service location; and iterating through hours of service (HOS) states of the offshore route; determining whether to add one or more additional HOS states in the offshore route; and resetting a cumulative drive time to zero for each offshore port-to-port service. Other embodiments are disclosed.

Abstract: A system includes one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform: selecting, by a greedy tote reduction algorithm within an infeasible totes loop, items that have a highest volume restriction and a highest weight restriction for each of a plurality of respective totes; iteratively executing a swap of the items; iteratively searching for candidate solutions until a solution of picklists for the plurality of respective totes containing the items, as swapped, is found; executing a minimum trolley loop algorithm on the solution of picklists to create combined picklists; and displaying to a picker on an interface of a computing device, turn-by-turn directions within a pick walk for the combined picklists. Other embodiments are disclosed herein.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations: receiving a load design for loading a trailer to deliver orders; generating an initial grouping comprising stacks of pallets that are configured to be loaded interchangeably among respective floor spot assignments on the trailer; iteratively adjusting at least one of the stacks of pallets within the initial grouping to satisfy at least a center-of-gravity constraint in a final load design for the trailer; and outputting the final load design comprising the initial grouping to cause the stacks of pallets of the initial grouping to be loaded into the trailer according to the final load design. Other embodiments are described.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform: generating a feasible dock-out plan designed to deliver one or more orders to one or more physical stores based on a feasible route plan by: generating a preliminary dock-out time boundary based on one or more delivery time windows; determining an earliest dock-out time based at least in part on the preliminary dock-out time boundary and a backward search; and determining a latest dock-out time based at least in part on the preliminary dock-out time boundary and a forward search; and transmitting a range of dock-out times comprising (i) the earliest dock-out time and (ii) the latest dock-out time to a user. Other embodiments are described.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions, that when executed on the one or more processors, cause the one or more processors, to perform: obtaining a first load plan, wherein the first load plan comprises a set of stacks assigned to floor spots of a trailer; determining that at least one of (a) there is an empty floor spot in the first load plan for the trailer or (b) the first load plan is infeasible; determining a target number of stacks for an updated load plan; iterating, via simulated annealing, through modifications to the updated load plan; terminating the iterating when a predetermined termination criteria is satisfied; and outputting the updated load plan. Other embodiments are disclosed.

Abstract: A method including obtaining an optimization request at a coordinating engine. The method also can include triggering engines to process the optimization request. At least one of the engines divides the optimization request into subproblems. At least a portion of the engines solve the subproblems. Respective instances of at least one of the engines are triggered to handle respective ones of the subproblems. Each of the engines provides a dynamic algorithmic flow using modularized algorithmic solvers. The dynamic algorithmic flow is adjusted based on a respective input to each of the engines. The method additionally can include outputting, from the coordinating engine, one or more results in response to the optimization request, based on results for the subproblems generated by the engines. Other embodiments are described.

Abstract: A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions, that when executed on the one or more processors, cause the one or more processors, to perform functions comprising: receiving, by an application programing interface (API), a change request for a route plan, wherein the change request is subject to a scenario specification and a load feasibility specification; determining an updated route plan based on whether the change request for the route plan is a feasible route based on the scenario specification and the load feasibility specification, and the route stop time is the route start time is flexible; and outputting the updated route plan and, when the route stop time is flexible, outputting the route start time. Other embodiments are disclosed.

Abstract: A system comprising one or more processors; and one or more non-transitory computer-readable media storing computing instructions, that when executed on the one or more processors, cause the one or more processors to perform functions comprising: obtaining a route plan for outbound transport from a distribution center; processing multiple threads in parallel to obtain multiple improvements to the route plan, where each of the multiple threads customizes the route plan using one or more different parameters; selecting a lowest cost route solution from the multiple improvements to the route plan; and using the lowest cost route solution for the outbound transport from the distribution center. Other embodiments are disclosed.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform: receiving historical driver search information corresponding to a first offer publish time criterion, the first offer publish criterion including a driver lag time; building a machine learning model based on the driver search information to determine a first metric and a second metric; analyzing the first metric and the second metric with an optimization model to determine a second offer publish time criterion that reduces the driver lag time; receiving an order for a delivery for an item, the order including a delivery time window; transmitting the order to a driver search platform subject to the second offer publish time criterion to reduce the driver lag time and mitigate delivery outside of the delivery time window. Other embodiments are disclosed herein.