System for collaborative logistics using a collaborative logistics map and a knowledge graph

Patent metadata

Field	Value
Publication number	US-10909494-B2
Application number	US-201815937219-A
Country	US
Kind code	B2
Filing date	Mar 27, 2018
Priority date	Mar 27, 2018
Publication date	Feb 2, 2021
Grant date	Feb 2, 2021

Abstract

A device may generate a product delivery map that includes route information that is to be used by a fleet of delivery vehicles for performing a set of deliveries, and a set of location constraints identifying locations that are to be avoided by the fleet of delivery vehicles when performing the set of deliveries. The device may generate a collaborative interactions map that includes a set of collaborative constraints indicating particular supplier organizations that are candidates to engage in collaborative logistics. The device may determine, based on the set of location constraints and the set of collaborative constraints, a set of delivery schedules that are to be used to perform the set of deliveries. The device may provide the set of delivery schedules to one or more devices associated with the delivery organization to allow the fleet of delivery vehicles to perform the set of deliveries.

First claim

What is claimed is:

1. A device, comprising:
one or more memories; and
one or more processors, communicatively coupled to the one or more memories, to:
continuously obtain, via a network, real-time supplemental map information,
wherein the real-time supplemental map information includes at least one of:
real-time weather information, or
real-time traffic information;

generate, based on the real-time supplemental map information, a product delivery map,
the product delivery map including:
route information that is to be used by a fleet of delivery vehicles for performing a set of deliveries, and
a set of location constraints identifying locations that are to be avoided by the fleet of delivery vehicles when performing the set of deliveries,
 wherein the set of deliveries are for delivering a set of products that are associated with a plurality of supplier organizations, and
 wherein the fleet of delivery vehicles are associated with a delivery organization;

generate a collaborative interactions map that includes a set of collaborative constraints indicating particular supplier organizations of the plurality of supplier organizations that are candidates to engage in collaborative logistics,
wherein the one or more processors when, generating the collaborative interactions map, are to:
obtain historical product delivery information from a first data source,
obtain historical supplier organization information from a second data source,
identify collaborative interactions information between the particular supplier organizations based on the historical product delivery information and the historical supplier organization information using a natural language processing technique,
 the collaborative interactions information including information about whether the particular supplier organizations are candidates to engage in collaborative logistics with each other, and
generate the collaborative interactions map based on the collaborative interactions information;

generate a collaborative logistics map,
the collaborative logistics map including:
the set of location constraints,
the set of collaborative constraints, and
a set of product constraints associated with a knowledge graph,
 wherein the set of product constraints include one or more constraints that identify which products, of the set of products, are unable to be transported together in a particular delivery vehicle of the fleet of delivery vehicles;

determine, for the collaborative logistics map, a set of delivery schedules that are to be used to perform the set of deliveries,
wherein the set of delivery schedules are determined based on the set of location constraints, the set of collaborative constraints, and the set of product constraints;

provide the set of delivery schedules to one or more devices associated with the delivery organization to allow the fleet of delivery vehicles to perform the set of deliveries; and
provide, based on continuously obtaining the real-time supplemental map information, updated route information to one or more delivery vehicles of the fleet of delivery vehicles.

2. The device of claim 1 ,
wherein the real-time supplemental map information further includes:
construction information; and

wherein the set of location constraints includes at least one of:
one or more weather-related constraints,
one or more traffic-related constraints, or
one or more construction-related constraints.

3. The device of claim 1 , wherein the one or more processors, when generating the product delivery map, are to:
generate, using a neural network, a set of possible routes that the fleet of delivery vehicles are capable of using to perform the set of deliveries,
wherein the set of possible routes do not interfere with the set of location constraints.

4. The device of claim 1 , wherein the one or more processors are further to:
obtain, before generating the collaborative interactions map, the historical product delivery information associated with the plurality of supplier organizations,
wherein the historical product delivery information includes preferred route information identifying one or more routes that are most commonly taken by the fleet of delivery vehicles when performing the set of deliveries; and

wherein the one or more processors, when generating the collaborative interactions map, are to:
generate the collaborative interactions map that includes the set of collaborative constraints,
wherein the set of collaborative constraints are based on the preferred route information.

5. The device of claim 1 ,
wherein the collaborative interactions information is processed using the natural language processing technique and one or more of:
a machine learning technique, or
the knowledge graph.

6. The device of claim 1 , wherein the one or more processors, when determining the set of delivery schedules, are to:
determine final product placement for the set of products based on the set of product constraints,
wherein final product placement includes assigning each product, of the set of products, to particular delivery vehicles, of the fleet of delivery vehicles, and

determine a set of routes to be traversed by the fleet of delivery vehicles,
wherein the set of routes do not interfere with the set of location constraints and the set of collaborative constraints.

7. The device of claim 6 , wherein the one or more processors, when determining the set of routes, are to:
compare the set of routes to a product constraint of the set of product constraints included in the knowledge graph,
wherein the product constraint is a location constraint relating to particular products of the set of products,

determine that a route, of the set of routes, interferes with the product constraint of the set of product constraints, and
remove the route from the set of routes.

8. A method, comprising:
continuously obtaining, by a device and via a network, real-time supplemental map information,
wherein the real-time supplemental map information includes at least one of:
real-time weather information, or
real-time traffic information;

generating, by the device and based on the real-time supplemental map information, a product delivery map,
the product delivery map including:
route information that is to be used by a fleet of delivery vehicles for performing a set of deliveries, and
a set of location constraints identifying locations that are to be avoided by the fleet of delivery vehicles when performing the set of deliveries,
wherein the set of deliveries are for delivering a set of products that are associated with a plurality of supplier organizations, and
wherein the fleet of delivery vehicles are associated with a delivery organization;

generating, by the device, a collaborative interactions map that includes a set of collaborative constraints indicating particular supplier organizations of the plurality of supplier organizations that are candidates to engage in collaborative logistics,
where generating the collaborative interactions map comprises:
obtaining historical product delivery information from a first data source,
obtaining historical supplier organization information from a second data source,
identifying collaborative interactions information between the particular supplier organizations based on the historical product delivery information and the historical supplier organization information using a natural language processing technique,
 the collaborative interactions information including information about whether the particular supplier organizations are candidates to engage in collaborative logistics with each other, and
generating the collaborative interactions map based o

Assignees

• Accenture Global Solutions Ltd

Inventors

• Lecue Freddy
• Bouhini Chahrazed
• Limsopatham Nut

Classifications

• G06N3/042

  Knowledge-based neural networks; Logical representations of neural networks · CPC title

• G06Q10/08355

  Routing methods · CPC title

• G06F16/29

  Geographical information databases · CPC title

• G06Q10/0834 Primary

  Choice of carriers · CPC title

• G06Q10/1097

  using calendar-based scheduling for task assignment · CPC title