US 12,481,527 B2
Resource teaming optimization for resource planning
Martin Andersson, Jonsered (SE); Fredrik Altenstedt, Mölndal (SE); Lennart Bengtsson, Gothenburg (SE); Dmitrii Sergejev, Mölndal (SE); Waldemar Kocjan, Gothenburg (SE); Christoffer Sandberg, Gothenburg (SE); Henrik Roos, Mölndal (SE); and Björn Carlsson, Gothenburg (SE)
Assigned to The Boeing Company, Chicago, IL (US)
Filed by THE BOEING COMPANY, Chicago, IL (US)
Filed on Mar. 31, 2022, as Appl. No. 17/709,859.
Claims priority of provisional application 63/169,639, filed on Apr. 1, 2021.
Prior Publication US 2022/0318055 A1, Oct. 6, 2022
Int. Cl. G06F 9/48 (2006.01); G06F 9/50 (2006.01); G08G 5/30 (2025.01); G08G 5/00 (2025.01)
CPC G06F 9/4881 (2013.01) [G06F 9/5038 (2013.01); G06F 9/505 (2013.01); G08G 5/30 (2025.01)] 20 Claims
OG exemplary drawing
 
1. A method for reducing a memory usage and a power usage and improving a computational speed of a computing device forming a team of resources for completing a task, the method comprising:
transforming the computing device to a special purpose computer by adding a teaming optimizer for assigning the team of resources to complete the task comprising: resource requirements, a plurality of subtasks, and rule sets, wherein the teaming optimizer prioritizes whole-task teaming completing the task with the team being formed from rosters of resources completing all the subtasks and preferring partial teaming over no teaming in completing the task;
accessing through a communication interface, by the teaming optimizer, the task and the rosters of resources, wherein each resource comprises, respectively, a state;
accessing through the communication interface, by the teaming optimizer, a resource requirement in the resource requirements for each subtask of the plurality of subtasks;
formatting for each subtask respectively, by the teaming optimizer, a table comprising the resource requirements and a plurality of candidate slices comprising selections of resources from the rosters forming potential assignments forming every potential combination of the resources that satisfies the resource requirement;
transforming the table by the teaming optimizer using the rule sets for assigning a class respectively for each of the resources and curating, using the rule sets, the plurality of candidate slices into curated slice tables comprising at least a first curated slice table comprising resources from a first class and a second curated slice table comprising resources from a second class;
weighting evaluations of the first curated slice table and the second curated slice table separately from one another by the teaming optimizer using teaming metrics;
identifying the team of resources from the curated slice tables having a highest total teaming metric that satisfies all the resource requirements and all the rule sets of the task; and
assigning the combination of resources to complete the task.