# US 2021/0279831 A1 · Managing optimization of a network flow > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0279831 A1 - Document ID: US20210279831A1 - Status: Published application - Kind: A1 - Application number: 17/179,219 - Filed: 2021-02-18 - Published: 2021-09-09 - Earliest claimed: 2020-03-04 - Assignees: ProvisionAI, LLC - Applicants: ProvisionAI, LLC - Inventors: Thomas Moore; Jeffrey H. Schutt; Robert F. Schneider; Keith D. Moore - Web page: https://patentgrep.com/patent/US20210279831A1 ## CPC - G06Q 10/08: Logistics, e.g. warehousing, loading or distribution; Inventory or stock management - G06Q 10/06315: {Needs-based resource requirements planning or analysis} - G06Q 10/067: Enterprise or organisation modelling - G06Q 10/0674: {using process simulation} - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR ## Abstract A method, system and/or computer usable program product for managing optimization of a transportation network system including identifying a set of items for transportation between a set of origins and a set of destinations during a set of time periods, prioritizing each of the set of items, utilizing a set of network constraints and costs, utilizing a set of loading constraints for a set of transportation units, the loading constraints limiting the quantity and placement of items for each transportation unit, optimizing a quantity of the set of transportation units for transporting the set of items between the set of origins and the set of destinations during the set of time periods based on the prioritization of each of the set of items, the network constraints and costs, and the loading constraints for each transportation unit, and adjusting the optimized quantity of transportation units to a discrete quantity based on the prioritization of each of the set of items and the network constraints and costs. ## Claims ### Claim 1 A method of managing optimization of a transportation network system comprising: identifying a set of items for transportation between a set of origins and a set of destinations during a set of time periods; prioritizing each of the set of items; utilizing a set of network constraints and costs; utilizing a set of loading constraints for a set of transportation units, the loading constraints limiting the quantity and placement of items for each transportation unit; optimizing a quantity of the set of transportation units for transporting the set of items between the set of origins and the set of destinations during the set of time periods based on the prioritization of each of the set of items, the network constraints and costs, and the loading constraints for each transportation unit; and adjusting the optimized quantity of transportation units to a discrete quantity based on the prioritization of each of the set of items and the network constraints and costs. ### Claim 2 (depends on 1) The method of claim 1 further comprising: identifying a set of transportation lanes, each transportation lane having at least one origin and one destination; wherein optimizing the quantity of the set of transportation units includes optimizing a quantity of the set of transportation units for each transportation lane based on the prioritization of each of the set of items, the network constraints and costs, and the loading constraints for each transportation unit ### Claim 3 (depends on 1) The method of claim 1 wherein adjusting the optimized quantity of transportation units further comprises: rounding the quantity of transportation units to a first discrete quantity of transportation units and a second discrete quantity of transportation units, the second discrete quantity of transportation units larger than the first discrete quantity of transportation units; and selecting the quantity of transportation units from the first discrete quantity and the second discrete quantity based on the prioritization of each of the set of items and the network constraints and costs. ### Claim 4 (depends on 3) The method of claim 3 wherein adjusting the optimized quantity of transportation units further comprises: rounding the quantity of transportation units for each transportation lane to a first discrete quantity of transportation units and a second discrete quantity of transportation units, the second discrete quantity of transportation units larger than the first discrete quantity of transportation units; and selecting the quantity of transportation units from the first discrete quantity and the second discrete quantity based on the prioritization of each of the set of items and the network constraints and costs. ### Claim 5 (depends on 1) The method of claim 1 wherein the identified set of items are identified for transportation between the set of origins and the set of destinations for a specific time period; and wherein the set of network constraints and costs includes network rewards and penalties for transporting the set of items between the set of origins and the set of destinations during time periods other than the identified specific time period. ### Claim 6 (depends on 1) The method of claim 1 further comprising identifying which of the optimized quantity of transportation units are required to be committed for utilization. ### Claim 7 (depends on 1) The method of claim 1 further comprising simulating loading of the transportation units with the set of items in accordance with the loading constraints. ### Claim 8 (depends on 7) The method of claim 7 further comprising utilizing a trained learning system utilizing rewards and penalties to simulate loading of the transportation units with the set of items in accordance with the loading constraints. ### Claim 9 A computer program product for managing optimization of a transportation network system, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions processed by a processing circuit to cause the device to perform a method comprising: identifying a set of items for transportation between a set of origins and a set of destinations during a set of time periods; prioritizing each of the set of items; utilizing a set of network constraints and costs; utilizing a set of loading constraints for a set of transportation units, the loading constraints limiting the quantity and placement of items for each transportation unit; optimizing a quantity of the set of transportation units for transporting the set of items between the set of origins and the set of destinations during the set of time periods based on the prioritization of each of the set of items, the network constraints and costs, and the loading constraints for each transportation unit; and adjusting the optimized quantity of transportation units to a discrete quantity based on the prioritization of each of the set of items and the network constraints and costs. ### Claim 10 (depends on 9) The computer program product of claim 9 further comprising: identifying a set of transportation lanes, each transportation lane having at least one origin and one destination; wherein optimizing the quantity of the set of transportation units includes optimizing a quantity of the set of transportation units for each transportation lane based on the prioritization of each of the set of items, the network constraints and costs, and the loading constraints for each transportation unit ### Claim 11 (depends on 9) The computer program product of claim 9 wherein adjusting the optimized quantity of transportation units further comprises: rounding the quantity of transportation units to a first discrete quantity of transportation units and a second discrete quantity of transportation units, the second discrete quantity of transportation units larger than the first discrete quantity of transportation units; and selecting the quantity of transportation units from the first discrete quantity and the second discrete quantity based on the prioritization of each of the set of items and the network constraints and costs. ### Claim 12 (depends on 11) The computer program product of claim 11 wherein adjusting the optimized quantity of transportation units further comprises: rounding the quantity of transportation units for each transportation lane to a first discrete quantity of transportation units and a second discrete quantity of transportation units, the second discrete quantity of transportation units larger than the first discrete quantity of transportation units; and selecting the quantity of transportation units from the first discrete quantity and the second discrete quantity based on the prioritization of each of the set of items and the network constraints and costs. ### Claim 13 (depends on 9) The computer program product of claim 9 wherein the identified set of items are identified for transportation between the set of origins and the set of destinations for a specific time period; and wherein the set of network constraints and costs includes network rewards and penalties for transporting the set of items between the set of origins and the set of destinations during time periods other than the identified specific time period. ### Claim 14 (depends on 9) The computer program product of claim 9 further comprising identifying which of the optimized quantity of transportation units are required to be committed for utilization. ### Claim 15 (depends on 9) The computer program product of claim 9 further comprising simulating loading of the transportation units with the set of items in accordance with the loading constraints. ### Claim 16 (depends on 15) The computer program product of claim 15 further comprising utilizing a trained learning system utilizing rewards and penalties to simulate loading of the transportation units with the set of items in accordance with the loading constraints. ### Claim 17 A data processing system for managing optimization of a transportation network system, the data processing system comprising: a processor; and a memory storing program instructions which when processed by the processor perform the steps of: identifying a set of items for transportation between a set of origins and a set of destinations during a set of time periods; prioritizing each of the set of items; utilizing a set of network constraints and costs; utilizing a set of loading constraints for a set of transportation units, the loading constraints limiting the quantity and placement of items for each transportation unit; optimizing a quantity of the set of transportation units for transporting the set of items between the set of origins and the set of destinations during the set of time periods based on the prioritization of each of the set of items, the network constraints and costs, and the loading constraints for each transportation unit; and adjusting the optimized quantity of transportation units to a discrete quantity based on the prioritization of each of the set of items and the network constraints and costs. ### Claim 18 (depends on 17) The data processing system of claim 17 further comprising: identifying a set of transportation lanes, each transportation lane having at least one origin and one destination; wherein optimizing the quantity of the set of transportation units includes optimizing a quantity of the set of transportation units for each transportation lane based on the prioritization of each of the set of items, the network constraints and costs, and the loading constraints for each transportation unit ### Claim 19 (depends on 17) The data processing system of claim 17 wherein adjusting the optimized quantity of transportation units further comprises: rounding the quantity of transportation units to a first discrete quantity of transportation units and a second discrete quantity of transportation units, the second discrete quantity of transportation units larger than the first discrete quantity of transportation units; and selecting the quantity of transportation units from the first discrete quantity and the second discrete quantity based on the prioritization of each of the set of items and the network constraints and costs. ### Claim 20 (depends on 19) The data processing system of claim 19 wherein adjusting the optimized quantity of transportation units further comprises: rounding the quantity of transportation units for each transportation lane to a first discrete quantity of transportation units and a second discrete quantity of transportation units, the second discrete quantity of transportation units larger than the first discrete quantity of transportation units; and selecting the quantity of transportation units from the first discrete quantity and the second discrete quantity based on the prioritization of each of the set of items and the network constraints and costs. ### Claim 21 (depends on 17) The data processing system of claim 17 wherein the identified set of items are identified for transportation between the set of origins and the set of destinations for a specific time period; and wherein the set of network constraints and costs includes network rewards and penalties for transporting the set of items between the set of origins and the set of destinations during time periods other than the identified specific time period. ### Claim 22 (depends on 17) The data processing system of claim 17 further comprising identifying which of the optimized quantity of transportation units are required to be committed for utilization. ### Claim 23 (depends on 17) The data processing system of claim 17 further comprising simulating loading of the transportation units with the set of items in accordance with the loading constraints. ### Claim 24 (depends on 23) The data processing system of claim 23 further comprising utilizing a trained learning system utilizing rewards and penalties to simulate loading of the transportation units with the set of items in accordance with the loading constraints.