Abstract: A method, a data aggregation tool, and a set of instructions are disclosed. A data storage 120 may store a data aggregation tree with at least one entry node, a root node, and an initial entry status level for the at least one entry node. A processor 110 may detect an entry node status level change to a resulting entry status level at the at least one entry node. The processor 110 may aggregate a root node score at a root node based on an entry node score for the at least one entry node.

Abstract: A method, a service optimization system, and a set of instructions are disclosed. A user interface 150 may receive a data request 230 from a user 210. A service plan generation module 410 may generate in real time a preliminary data response plan 300 based upon the data request 230. A service optimization module 412 may create in real time an optimized data response plan 350 by reorganizing the preliminary data response plan 300 into a new order of execution to optimize a response time for the data response 414.

Abstract: The disclosure relates generally to methods and apparatus to optimize a supply plan through a hybrid meta-heuristic approach based on genetic algorithms to optimize inventory and generate a supply plan. The apparatuses include a supply chain planner that interacts with the processes of a supply chain network. To provide a complete optimization for the type of platform being deployed in theater a heuristic algorithm is devised to decompose the supply plan problem into a production center schedule and an asset routing problem, which will be tackled one after the other. The decomposed supply plan problem is solved with different heuristic algorithms. Namely, genetic algorithms are used to optimize the supply plans based on ever changing set of operational demands from in theater and the priority of those demands to the assigned depots, while efficient constructive heuristics are used to deal with footprint and timing constraints.

Abstract: A method, a data aggregation tool, and a set of instructions are disclosed. A data storage 120 may store a data aggregation tree with at least one entry node, a root node, and an initial entry status level for the at least one entry node. A processor 110 may detect an entry node status level change to a resulting entry status level at the at least one entry node. The processor 110 may aggregate a root node score at a root node based on an entry node score for the at least one entry node.

Abstract: A method, a service optimization system, and a set of instructions are disclosed. A user interface 150 may receive a data request 230 from a user 210. A service plan generation module 410 may generate in real time a preliminary data response plan 300 based upon the data request 230. A service optimization module 412 may create in real time an optimized data response plan 350 by reorganizing the preliminary data response plan 300 into a new order of execution to optimize a response time for the data response 414.

Abstract: The disclosure relates generally to methods and apparatus to optimize a supply plan through a hybrid meta-heuristic approach based on genetic algorithms to optimize inventory and generate a supply plan. The apparatuses include a supply chain planner that interacts with the processes of a supply chain network. To provide a complete optimization for the type of platform being deployed in theater a heuristic algorithm is devised to decompose the supply plan problem into a production center schedule and an asset routing problem, which will be tackled one after the other. The decomposed supply plan problem is solved with different heuristic algorithms. Namely, genetic algorithms are used to optimize the supply plans based on ever changing set of operational demands from in theater and the priority of those demands to the assigned depots, while efficient constructive heuristics are used to deal with footprint and timing constraints.

Abstract: The disclosure relates generally to methods and apparatus to optimize a supply plan through a hybrid meta-heuristic approach based on genetic algorithms to optimize inventory and generate a supply plan. The apparatuses include a supply chain planner that interacts with the processes of a supply chain network. To provide a complete optimization for the type of platform being deployed in theater a heuristic algorithm is devised to decompose the supply plan problem in to separate sub-problems, which will be tackled one after the other. The two separated sub-problems are solved with different heuristic algorithms. Namely, genetic algorithms are used to optimize the supply plans based on ever changing set of operational demands from in theater and the priority of those demands to the assigned depots, while efficient constructive heuristics are used to deal with footprint and timing constraints.