Transportation planning with parallel optimization

A computer implemented transportation planning method, comprising:

• accessing a set of orders;
• accessing a transportation planning model;
• selectively splitting into two or more shipments orders whose size exceeds a threshold size, each of the shipments being less than the threshold size;
• selectively consolidating into one shipment two or more shipments having a same source and a same destination, the one shipment being less than the threshold size;
• assigning a shipment to a direct load;
• determining a lowest rate for the direct load;
• for orders that are not associated with a load, performing in parallel two or more sequences that include two or more of:
• {
• selecting orders to be considered for loads influenced by local pooling;
• determining first multi-stop loads that satisfy one or more orders, where the first multi-stop loads visit a pooling point;
• selecting orders to be considered for loads influenced by global pooling;
• determining second multi-stop loads that satisfy one or more orders, where the second multi-stop loads visit a pooling point;
• selectively consolidating into one shipment two or more shipments that have at least two common points;
• selecting a primary carrier for a load;
• and
• using the primary carrier to rate the load;
• };
• identifying a set of loads that satisfies a threshold amount of orders;
• selectively removing a load from the set of loads upon determining that the load provides duplicate coverage for an order;
• determining continuous move loads from loads in the set of loads;
• selectively locally repairing a carrier selection for a load;
• scheduling the set of loads into a scheduled set of loads;
• and
• providing an actionable plan of loads from the scheduled set of loads.

A computer implemented transportation planning method, comprising:

• accessing a set of orders, where an order includes data concerning one or more of, a commodity to be shipped, an earliest pickup time, an earliest delivery time, a latest pickup time, a latest delivery time, a source, and a destination;
• accessing a transportation planning model;
• selectively splitting into two or more shipments orders whose size exceeds a threshold size, each of the shipments being less than the threshold size;
• selectively consolidating into one shipment two or more shipments having a same source and a same destination, the one shipment being less than the threshold size;
• assigning a shipment to a direct load based, at least in part, on a bin-picking heuristic;
• determining a lowest rate for the direct load;
• for orders that are not associated with a load, performing in parallel two or more sequences that include two or more of:
• {
• selecting orders to be considered for loads influenced by local pooling based, at least in part, on an order size, a distance from an origin to a pooling point, or a distance from a destination to a pooling point;
• determining first multi-stop loads that satisfy orders based, at least in part, on an origin neighborhood or a destination neighborhood, where the first multi-stop loads visit a pooling point;
• selecting orders to be considered for loads influenced by global pooling, based, at least in part, on a mixed integer problem (MIP) model;
• determining second multi-stop loads that satisfy orders, where the second multi-stop loads visit a pooling point;
• selectively consolidating into one shipment two or more shipments that have at least two common points;
• selecting a primary carrier for a load, based, at least in part, on considering one or more carrier related constraints including carrier availability, carrier discount, carrier commitments, and carrier/product compatibility;
• and
• using the primary carrier to rate the load;
• };
• identifying a set of loads that satisfies a threshold amount of orders;
• selectively removing a load from the set of loads upon determining that the load provides duplicate coverage for an order;
• determining continuous move loads from loads in the set of loads, where determining a continuous move load includes:
• upon identifying that an additional move will improve a utility measure for a continuous move load, adding the additional move to the continuous move load;
• and
• continuing to evaluate additional moves until no additional move can be identified that would improve the utility measure for the continuous move load beyond a threshold improvement amount;
• selectively locally repairing a carrier selection for a load by identifying a carrier related constraint that is violated for a load or a carrier commitment rule that is broken for a load and repairing the violated constraint or broken rule;
• scheduling the set of loads into a scheduled set of loads, where scheduling a load includes one or more of, manipulating the timing of a load based on one or more of, a carrier, a service, a mode, a lane, an order pickup window, an order drop-off window, a precedence relationship between order legs, a facility loading speed, a facility unloading speed, a facility required flow thru time, and a federal layover regulation rule;
• and
• providing an actionable plan of loads from the scheduled set of loads, where the actionable plan of loads satisfies a threshold percentage of the orders once and only once and is based, at least in part, on a mixed integer problem (MIP) set partition model;
• where splitting orders, consolidating shipments, assigning shipments to loads, selecting orders to participate in pooling, determining loads, selecting carriers, repairing loads, and scheduling loads is performed by one or more of, linear programming processes, simplex method processes, dynamic programming processes, greedy algorithm processes, look ahead processes, divide and conquer processes, branch and bound processes, savings-based processes, and heuristic-based processes.

A computer implemented transportation planning method, comprising:

• in parallel, generating a set of candidate loads to satisfy a set of orders;
• selecting a set of final loads from the set of candidate loads;
• and
• in parallel, manipulating the set of final loads to reduce a transportation cost associated with satisfying the set of orders.

A set of application programming interfaces embodied on a computer-readable medium for execution by a computer component in conjunction with performing parallel optimization in transportation planning, comprising:

• a first interface for communicating an order data;
• a second interface for communicating a shipment data;
• a third interface for communicating a load data;
• and
• a fourth interface for communicating a selection data.

A system, comprising:

• means for selecting, in parallel, candidate loads to satisfy a set of orders;
• means for selecting final loads from the candidate loads;
• and
• means for selectively optimizing the final loads into a transportation plan that reduces a transportation cost, the optimizing being performed in parallel.