US 12,131,282 B1
Systems and methods for solving multi-objective hierarchical linear programming problems using previously-solved solution information
Vishal Shinde, Bangaluru (IN)
Assigned to Blue Yonder Group, Inc., Scottsdale, AZ (US)
Filed by Blue Yonder Group, Inc., Scottsdale, AZ (US)
Filed on Feb. 24, 2022, as Appl. No. 17/679,871.
Claims priority of provisional application 63/155,936, filed on Mar. 3, 2021.
Int. Cl. G06Q 10/00 (2023.01); G06Q 10/04 (2023.01); G06Q 10/083 (2023.01)
CPC G06Q 10/083 (2013.01) [G06Q 10/04 (2013.01)] 14 Claims
OG exemplary drawing
 
1. A system for reducing a computational runtime to re-run computer solves of one or more-supply chain planning problems modeled as one or more multi-objective hierarchical linear programming problems, comprising:
one or more imaging devices comprising one or more electronic devices configured to receive imaging information from one or more sensors, the one or more imaging devices configured to generate a mapping of one or more items to determine a current location of the one or more items at one or more supply chain entities;
a computer, comprising a processor and memory, and configured to:
receive supply chain input data for a supply chain planning problem;
model the supply chain planning problem as a first multi-objective hierarchal linear programming problem comprising a first Run1 objective and at least one additional Run1 objective, and based, at least in part, on the supply chain input data;
solve the first multi-objective hierarchical linear programming problem for the first Run1 objective and the at least one additional Run1 objective;
store a cumulative list of bound changes in the computer memory during the solve of the first multi-objective hierarchical linear programming problem;
receive one or more changes to the supply chain input data;
model a second supply chain planning problem as a second multi-objective hierarchal linear programming problem based, at least in part, on the one or more changes to the supply chain input data, wherein the second multi-objective hierarchical linear programming problem comprises a first Run2 objective and at least one additional Run2 objective;
identify common variables and non-common variables, wherein an intermediate objective is a minimization of weighted sums of categorized common and non-common variables, wherein the weighted sums of categorized common and non-common variables comprise:
a first category comprising common variables fixed at a lower bound;
a second category comprising common variables fixed at an upper bound;
a third category comprising common variables that are not fixed to an upper bound or to a lower bound; and
a fourth category comprising non-common variables, wherein non-common variables are variables present in the second multi-objective hierarchical linear programming problem and not present in the first multi-objective hierarchical linear programming problem;
derive the intermediate objective based, at least in part, on the cumulative list of bound changes;
increase linear programming problem solution speed using an optimization API by:
solving the second multi-objective hierarchical linear programming problem for the intermediate objective; and
solving the second multi-objective hierarchical linear programming problem, using a basis of the solved intermediate objective, for at least the first Run2 objective;
receive product data from automated robotic machinery comprising at least one sensor, wherein the product data corresponds to an item of the one or more items detected by the automated robotic machinery;
generate a first mapping and a second mapping of the item, the first mapping associated with a first current location of the item, and the second mapping associated with a second past location of the item;
compare the first mapping and the second mapping to determine if the first current location of the item is different from the second past location of the item;
monitor one or more supply chain constraints of the one or more items at the one or more supply chain entities and adjust a current inventory of the one or more supply chain entities by sending instructions to the automated robotic machinery based, at least in part, on the one or more supply chain constraints and one or more differences between the first mapping and the second mapping;
in response to sending the instructions to the automated robotic machinery, automatically locate items to add or remove from the current inventory of the one or more supply chain entities; and
automatically adding or removing by the automated robotic machinery the automatically located items from the current inventory.