System To Organize Commodity-Product Distribution

• US 20120084223A1
• Filed: 10/28/2010
• Published: 04/05/2012
• Est. Priority Date: 09/30/2010
• Status: Abandoned Application

• Current Assignee: L'Air Liquide Societe Anonyme Pour L'Etude et L'Exploitation Des Procedes Georges Claude (L'Air Liquide SA)
• Original Assignee: L'Air Liquide Societe Anonyme Pour L'Etude et L'Exploitation Des Procedes Georges Claude (L'Air Liquide SA)
• Inventors: Briet, Philippe, Mornet, Franck, Omer, Jeremy
• Application Number:
  US12/914,430
• Publication Number:
  US 20120084223A1
• Time in Patent Office: Days
• US Class Current: 705/338
• CPC Class Codes: G06Q 10/08355 Routing methods

Abstract

Techniques are disclosed for generating a delivery schedule for distributing commodity products to a group of customers grouped by clusters. For example, a distributor/producer of refined gasses distributed via cylinders may create a proposed delivery schedule limiting the days at which product is delivered to different customers. A delivery planning application may include a clustering module a set of input data to generate a set of clusters representing groups of customers, e.g., using a Greedy Algorithm optimized using a Tabu search. Once the clusters are generated, a planning module may be used to affect trucks (or other delivery vehicles) to clusters. A truck affected to a given cluster by the delivery planning application is then scheduled to deliver cylinders to that cluster. The delivery schedule may provide a reusable two-week plan for servicing a group of customers in a supply chain network.

Claims

1. A computer-implemented method for generating a delivery schedule for a group of customers grouped by clusters, the method comprising:

receiving a set of input data specifying a set of customers, delivery vehicles, and delivery parameters for distributing a commodity product to the set of customers from one of a filling plant or a hub using direct transport to customers;

generating, from the input data, a set of one or more clusters, wherein each cluster specifies a disjoint group of one or more of the customers, wherein the disjoint group of one or more of the customers in a cluster represents a group of customers allowed to request deliveries on a common set of days of the week, and which are delivered by the same delivery vehicle on the common set of days of the week;

determining a delivery schedule for distributing the commodity product to the set of customers, wherein the delivery schedule allocates one or more days of a week for a delivery to each cluster using one of the delivery vehicles.

2. The method of claim 1, wherein the set of one or more clusters are generated according to a Greedy Algorithm.

3. The method of claim 2, wherein the set of one or more clusters are generated according to the Greedy Algorithm are further generated according to a Tabu Search.

4. The method of claim 3, further comprising:

generating, from the input data, at least one meta-customer aggregating two or more customers specified by the input data, and wherein the at least one meta customer is assigned to one of the clusters; and

prior to determining the delivery schedule, disaggregating the meta-customer.

5. The method of claim 3, further comprising:

generating, from the input data, at least one meta-delivery vehicle aggregating two or more vehicles specified by the input data, wherein at least one of the days for which a delivery is allocated to a first cluster is allocated using the meta-delivery vehicle; and

prior to determining the delivery schedule, disaggregating the meta-delivery vehicle by allocating one of the two or more aggregated delivery vehicle to the first cluster.

6. The method of claim 1, wherein determining a delivery schedule for distributing the commodity product to the set of customers comprises solving a Constraint Programming model.

7. The method of claim 1, wherein the commodity product comprises refined gases stored in cylinders.

8. A computer-readable storage medium containing a delivery scheduling application, which when executed on a processor performs an operation for generating a delivery schedule for a group of customers grouped by clusters, the operation comprising:

receiving a set of input data specifying a set of customers, delivery vehicles, and delivery parameters for distributing a commodity product to the set of customers from one of a filling plant or a hub using direct transport to customers;

generating, from the input data, a set of one or more clusters, wherein each cluster specifies a disjoint group of one or more of the customers, wherein the disjoint group of one or more of the customers in a cluster represents a group of customers allowed to request deliveries on a common set of days of the week and which are delivered by the same delivery vehicle on the common set of days of the week;

determining a delivery schedule for distributing the commodity product to the set of customers, wherein the delivery schedule allocates one or more days of a week for a delivery to each cluster using one of the delivery vehicles.

9. The computer-readable storage medium of claim 8, wherein the set of one or more clusters are generated according to a Greedy Algorithm.

10. The computer-readable storage medium of claim 9, wherein the set of one or more clusters are generated according to the Greedy Algorithm are further generated according to a Tabu Search.

11. The computer-readable storage medium of claim 10, wherein the operation further comprises:

generating, from the input data, at least one meta-customer aggregating two or more customers specified by the input data, and wherein the at least one meta customer is assigned to one of the clusters; and

prior to determining the delivery schedule, disaggregating the meta-customer.

12. The computer-readable storage medium of claim 10, wherein the operation further comprises:

generating, from the input data, at least one meta-delivery vehicle aggregating two or more vehicles specified by the input data, wherein at least one of the days for which a delivery is allocated to a first cluster is allocated using the meta-delivery vehicle; and

prior to determining the delivery schedule, disaggregating the meta-delivery vehicle by allocating one of the two or more aggregated delivery vehicle to the first cluster.

13. The computer-readable storage medium of claim 8, wherein determining a delivery schedule for distributing the commodity product to the set of customers comprises solving a Constraint Programming model.

14. The computer-readable storage medium of claim 8, wherein the commodity product comprises refined gases stored in cylinders.

15. A system, comprising:

a processor; and

a memory storing a delivery scheduling application, which when executed on the processor performs an operation for generating a delivery schedule for a group of customers grouped by clusters, the operation comprising;

receiving a set of input data specifying a set of customers, delivery vehicles, and delivery parameters for distributing a commodity product to the set of customers from one of a filling plant or a hub using direct transport to customers,

generating, from the input data, a set of one or more clusters, wherein each cluster specifies a disjoint group of one or more of the customers, wherein the disjoint group of one or more of the customers in a cluster represents a group of customers allowed to request deliveries on a common set of days of the week and which are delivered by the same delivery vehicle on the common set of days of the week, and

determining a delivery schedule for distributing the commodity product to the set of customers, wherein the delivery schedule allocates one or more days of a week for a delivery to each cluster using one of the delivery vehicles.

16. The system of claim 15, wherein the set of one or more clusters are generated according to a Greedy Algorithm.

17. The system of claim 16, wherein the set of one or more clusters are generated according to the Greedy Algorithm are further generated according to a Tabu Search.

18. The system of claim 17, wherein the operation further comprises:

generating, from the input data, at least one meta-customer aggregating two or more customers specified by the input data, and wherein the at least one meta customer is assigned to one of the clusters; and

prior to determining the delivery schedule, disaggregating the meta-customer

19. The system of claim 17, wherein the operation further comprises:

generating, from the input data, at least one meta-delivery vehicle aggregating two or more vehicles specified by the input data, wherein at least one of the days for which a delivery is allocated to a first cluster is allocated using the meta-delivery vehicle; and

prior to determining the delivery schedule, disaggregating the meta-delivery vehicle by allocating one of the two or more aggregated delivery vehicle to the first cluster.

20. The system of claim 15, wherein determining a delivery schedule for distributing the commodity product to the set of customers comprises solving a Constraint Programming model.

21. The system of claim 15, wherein the commodity product comprises refined gases stored in cylinders.