IP Library › Granted Patent US 10,467,563

Granted Patent B1

US 10,467,563 · App. 16/278,709 · Granted Nov 5, 2019

Systems and methods for computerized balanced delivery route pre-assignment

Inventors: Wenting Mo (Beijing, CN); Ying Qin (Shanghai, CN); Xingzhu Gu (Shanghau, CN); Jin Hyun Kim (Beijing, CN); Xiufang Zhu (Beijing, CN)

Assignee: Coupang, Corp.

G06Q10/063112 G06F16/29 G06Q10/047 G06Q10/06315 G06Q10/08355

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Quick Facts

Patent No.: US 10,467,563
App. No.: 16/278,709
Filed: Feb 18, 2019
Granted: Nov 5, 2019
Kind: B1
Examiner: JARRETT, SCOTT L
Art Unit: 3623
USPC: 705/7.14

External Links

USPTO Patent Center↗ Full Patent PDF↗

Abstract

A system for attendance pre-assignment. The system may include a memory storing instructions and at least processor configured to execute the instructions to perform operations. The operations may include retrieving, from a database, a plurality of delivery routes and a plurality of delivery sub-routes, wherein the delivery sub-routes are part of the delivery routes; calculating, based on the retrieval, a number of packages allocated to the delivery routes and the delivery sub-routes; receive data comprising groups of pre-assigned workers available for deliveries, the workers being classified into a plurality of categories; comparing, based on the received groups, the pre-assigned workers against the delivery routes and the delivery sub-routes; assigning, based on the comparison, the packages, the delivery routes, and the delivery sub-routes to the pre-assigned workers; generating a plurality of candidate routes associated with the pre-assigned workers; and calibrating, based on the assignment and the generated candidate routes, the delivery sub-routes.

Claims (96)

1. A system comprising:

a database comprising geographical data and historical delivery data, the geographical data being stored in pre-defined regions and sub-regions;

an expected delivery efficiency generator implemented in software or hardware, configured to:

receive geographical data from a plurality of the pre-defined regions and a plurality of the sub-regions, wherein the geographical data includes at least one of landscape data, business data, residential data, parking data, or building data;

determine, based on the geographical data, an expected delivery efficiency, the expected delivery efficiency being measured by percentiles of addresses visited by the workers per hour (APH); and

calculate, based on the historical delivery data, the APH for selected individual pre-defined regions and sub-regions;

a cross time generator implemented in software or hardware, configured to:

calculate an expected time for the workers to travel between first and second regions, wherein the expected time includes a cross-region time and a sub-region time based on a median time gap or an average time; and

determine, based on a linear regression and the cross-region time, a driving time between the first and the second regions; and

a route generator implemented in software or hardware, configured to:

determine candidate delivery regions and candidate delivery sub-regions;

combine the candidate delivery regions and the candidate delivery sub-regions into new regions for delivery;

determine a combination of the candidate delivery regions and the candidate delivery sub-regions based on the determined expected delivery efficiency and the calculated cross-region time;

redistribute at least one of candidate delivery regions and candidate delivery sub-regions based on the determined expected delivery efficiency and the calculated cross-region time;

receive data comprising groups of workers available for deliveries, the workers being classified into a plurality of categories;

compare, based on the received groups, the workers against delivery routes and delivery sub-routes, wherein the delivery sub-routes are part of the delivery routes;

assign, based on the comparison, packages, the delivery routes, and the delivery sub-routes to the workers;

generate a plurality of candidate routes associated with the candidate delivery regions, the candidate delivery sub-regions, and the workers;

calibrate, based on the assignment and the generated candidate routes, the delivery sub-routes;

transmit, at least one of the delivery sub-routes to a mobile device associated with a delivery worker, the mobile device capturing data associated with an identifier of the packages upon delivery,

wherein the expected delivery efficiency generator is further configured to:

receive information from the mobile device relating to the delivery, wherein the information is configured to be stored in the database as historical delivery data; and

recalculate, based on the historical delivery data, the APH for the selected individual pre-defined regions and sub-regions; and

wherein the route generator is further configured to:

recalibrate, based on the recalculated APH, at least one of the calibrated delivery sub-routes.

2. The system of claim 1 , wherein the route generator is further configured to:

determine delivery routes and delivery sub-routes based on the stored historical delivery data and optimized map data.

3. The system of claim 1 , wherein the route generator is further configured to:

calculate an average deviation value of the received groups from an average value of packages per worker delivery.

4. The system of claim 1 , wherein the expected delivery efficiency generator is further configured to:

receive a type of worker available for deliveries, wherein the type includes at least one of experience or efficiency.

5. The system of claim 1 , wherein the system is further configured to:

receive a number and a type of workers available for deliveries, from user input via a graphical user interface (GUI) from at least one of a web browser or a mobile device.

6. The system of claim 5 , wherein the user input includes a package distribution and an attendance value.

7. The system of claim 1 , wherein the expected delivery efficiency generator is further configured to:

receive data comprising the groups of pre-assigned workers available for deliveries, the pre-assigned workers being classified into a plurality of categories.

8. A method for attendance assignment, the method comprising:

receiving geographical data from a plurality of the pre-defined regions and a plurality of the sub-regions, wherein the geographical data includes at least one of landscape data, business data, residential data, parking data, or building data;

determining, based on the geographical data, an expected delivery efficiency, the expected delivery efficiency being measured by percentiles of addresses visited by the workers per hour (APH);

calculating, based on historical delivery data, the APH for selected individual pre-defined regions and sub-regions;

calculating an expected time for the workers to travel between first and second regions, wherein the expected time includes a cross-region time and a sub-region time based on a median time gap or an average time;

determining, based on a linear regression and the cross-region time, a driving time between the first and the second regions;

determining candidate delivery regions and candidate delivery sub-regions;

combining the candidate delivery regions and the candidate delivery sub-regions into new regions for delivery;

determining a combination of the candidate delivery regions and the candidate delivery sub-regions based on the determined expected delivery efficiency and the calculated cross-region time;

redistributing at least one of candidate delivery regions and candidate delivery sub-regions based on the determined expected delivery efficiency and the calculated cross-region time;

receiving data comprising groups of workers available for deliveries, the workers being classified into a plurality of categories;

comparing, based on the received groups, the workers against delivery routes and delivery sub-routes, wherein the delivery sub-routes are part of the delivery routes;

assigning, based on the comparison, packages, the delivery routes, and the delivery sub-routes to the workers;

generating a plurality of candidate routes associated with the candidate delivery regions, the candidate delivery sub-regions, and the workers;

calibrating, based on the assignment and the generated candidate routes, the delivery sub-routes;

transmitting, at least one of the delivery sub-routes to a mobile device associated with a delivery worker, the mobile device capturing data associated with an identifier of the packages upon delivery;

receiving information from the mobile device relating to the delivery, wherein the information is configured to be stored as historical delivery data;

recalculating, based on the historical delivery data, the APH for the selected individual pre-defined regions and sub-regions; and

recalibrating, based on the recalculated APH, at least one of the calibrated delivery sub-routes.

9. The method of claim 8 , the method further comprising:

determining delivery routes and delivery sub-routes based on the stored historical delivery data and optimized map data.

10. The method of claim 8 , the method further comprising:

calculating an average deviation value of the received groups from an average value of packages per worker delivery.

11. The method of claim 8 , the method further comprising:

receiving a type of worker available for deliveries, wherein the type includes at least one of experience or efficiency.

12. The method of claim 8 , the method further comprising:

receiving a number and a type of workers available for deliveries, from user input via a graphical user interface (GUI) from at least one of a web browser or a mobile device.

13. The method claim 12 , wherein the user input includes a package distribution and an attendance value.

14. The method of claim 8 , the method further comprising:

receiving data comprising the groups of pre-assigned workers available for deliveries, the pre-assigned workers being classified into a plurality of categories.

15. A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform operations comprising: