TOOLS FOR ASSISTING IN PETROLEUM PRODUCT TRANSPORTATION LOGISTICS

• US 20100332273A1
• Filed: 06/24/2010
• Published: 12/30/2010
• Est. Priority Date: 06/24/2009
• Status: Abandoned Application

• Current Assignee: ExxonMobil Research & Engineering Company (Exxon Mobil Corporation)
• Original Assignee: ExxonMobil Research & Engineering Company (Exxon Mobil Corporation)
• Inventors: MARGOLIES, Michael F., SAWAYA, Nicolas, SONG, Jin-Hwa, MCDONALD, Michael K., MIRANDA, Merryl J., BALASUBRAMANIAN, Jayanth, KOCIS, Gary R., WARRICK, Philip H., FURMAN, Kevin C.
• Application Number:
  US12/822,695
• Publication Number:
  US 20100332273A1
• Time in Patent Office: Days
• US Class Current: 705/332
• CPC Class Codes: G06Q 10/08 Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
• CPC Class Codes: G06Q 10/0832 Special goods or special handling procedures, e.g. handling of hazardous or fragile goods
• CPC Class Codes: G06Q 50/28 Logistics, e.g. warehousing, loading, distribution or shipping
• CPC Class Codes: G06Q 99/00 Subject matter not provided for in other groups of this subclass

Abstract

A tool to assist decision-making in the logistics of bulk product transportation. For example, the tool may be used to solve a problem involving the transportation and the inventory management of crude oil, in which the transportation of crude oil between supply ports and discharge ports are performed by a fleet of ships. The tool is capable of handling a typical petroleum product transportation problem, which can be quite complex. The tool uses advanced modeling and optimization technology to find a solution (either optimal or near optimal) for the allocation of bulk products, vehicle routing, vehicle scheduling, and/or bulk product blending operations.

Claims

1. A method for transporting bulk products, comprising:

(I) receiving a data set comprising;

(a) an identification of a plurality of supply locations and a plurality of demand locations;

(b) for each supply location, identification of one or more supply streams of bulk products and the monetary values of the bulk products from the supply stream;

(c) for each demand location, identification of one or more demand streams of bulk products and the monetary values of the bulk products that meet the property specification requirements of the demand stream;

(d) identification of a fleet of vehicles for carrying bulk products between supply locations and demand locations;

(e) data relating to the capacity of each vehicle in the fleet;

(f) data relating to the costs for transporting bulk products from the supply locations to the demand locations; and

(g) identification of one or more blending tanks located at a demand location or a supply location for receiving bulk product from a vehicle and discharging bulk product to a demand stream;

(II) using the data set to populate a mathematical model that comprises an objective function for net profit margin and a plurality of constraints;

wherein the constraints include one or more non-linear terms relating to the quantity or property of blending tank content;

(III) obtaining a solution to the mathematical model for maximizing the objective function for net profit margin; and

(IV) physically transporting one or more bulk products based on the solution to the mathematical model.

2. The method of claim 1, wherein the net profit margin calculation comprises the sum of the monetary values of the bulk products discharged directly to the demand streams from the vehicles, the sum of the monetary values of the bulk products discharged from each blending tank to a demand stream, minus the sum of the monetary values of the bulk products loaded from the supply streams, minus costs related to the transportation of the bulk products between the supply locations and the demand locations, minus costs related to the use of each blending tank.

3. The method of claim 1, wherein the mathematical model is a mixed integer non-linear programming (MINLP) model.

4. The method of claim 3, wherein obtaining a solution to the MINLP model comprises transforming the MINLP model into a mixed integer linear programming (MILP) model subproblem and solving the MILP model subproblem.

5. The method of claim 4, wherein solving the MILP model subproblem comprises imposing as a constraint, a monotonic functionality to each blending tank.

6. The method of claim 5, wherein the monotonic functionality requires that once discharge begins, each blending tank must be fully emptied before accepting new bulk products.

7. The method of claim 4, wherein solving the MILP model subproblem further comprises reducing the model complexity and obtaining an initial feasible solution to the reduced MILP model subproblem.

8. The method of claim 7, wherein reducing the model complexity comprises restricting the accessible supply locations or demand locations for one or more vehicles.

9. The method of claim 7, further comprising obtaining an improved solution to the initial feasible solution by fixing the routes of one or more vehicles based on the initial feasible solution and applying a large neighborhood search to the MILP model subproblem.

10. The method of claim 4, further comprising using the solution of the MILP model subproblem to formulate a non-linear programming (NLP) model subproblem by fixing the integer components of the MINLP model based on the solution obtained for the MILP model subproblem

11. The method of claim 10, wherein the one or more non-linear terms are bilinear terms, and further comprising, if the NLP model subproblem solution uses a blending tank, obtaining an improved solution to the MINLP model by steps comprising:

(a) removing the constraint imposing monotonic functionality to each blending tank; and

(b) fixing one side of all bilinear terms in the MINLP model and solving the resulting MILP model.

12. The method of claim 2, wherein the objective function for net profit margin further comprises the sum of the costs for leasing a blending tank as a subtraction.

13. The method of claim 2, wherein the objective function for net profit margin further comprises the sum of the costs for discharging bulk products from a blending tank to a demand stream as a subtraction.

14. The method of claim 2, wherein the objective function for net profit margin further comprises a value adjustment based on the specifications required by the demand stream.

15. The method of claim 1, wherein the model further comprises decision variables for one or more of:

vehicle choice, routing, load amounts, discharge amounts, timing, blending tank leasing, and bulk product blending.

16. The method of claim 1, wherein at least one demand location requires a bulk product having a different property specification than the bulk products available from one or more of the supply streams.

17. The method of claim 16, further comprising using the solution to determine a blending plan for blending one or more bulk products to form a blended bulk product that meets the different property specification requirement for a demand stream.

18. The method of claim 17, further comprising, according to the blending plan, physically transferring one or more bulk products into a blending tank containing another bulk product, and blending the bulk products to form the blended bulk product.

19. The method of claim 2, wherein the objective function for net profit margin further comprises the sum of the specification adjusted valuations of the discharged bulk products.

20. The method of claim 1, wherein each vehicle is a vessel, each supply location is a supply port, and each demand location is a demand port.

21. The method of claim 1, wherein the fleet of vehicles is heterogeneous.

22. The method of claim 1, wherein the constraints include one or more of the following:

terms relating to load amounts, terms relating to discharge amounts, terms relating to bulk product availability, terms relating to bulk product specifications, terms relating to vehicle capacity, and terms relating to property specification.

23. The method of claim 1, wherein the data set further comprises data relating to one or more of the following for one or more blending tanks:

tank capacity, bulk product property specification, loading restrictions, discharge restrictions, tank lease duration, and tank lease costs.

24. The method of claim 2, wherein the objective function further comprises the sum of the inventory holding costs.

25. The method of claim 3, wherein the MINLP model is solved by decomposing into a mixed integer linear programming (MILP) model subproblem and a non-linear programming (NLP) model subproblem;

wherein the MILP model is solved first and the solution to the MILP model is used to define the NLP model.

26. The method of claim 10, wherein the one or more non-linear terms are bilinear terms, and further comprising, if the NLP model subproblem solution uses a blending tank, obtaining an improved solution to the MINLP model by steps comprising:

(a) removing the constraint imposing monotonic functionality to each blending tank; and

(b) fixing one variable in each of the bilinear terms in the MINLP model and solving the resulting MILP model.

27. An computer apparatus for determining the transportation of bulk products, comprising:

(I) a memory device storing a data file containing;

(a) an identification of a plurality of supply locations and a plurality of demand locations;

(b) for each supply location, identification of one or more supply streams of bulk products and the monetary values of the bulk products from the supply stream;

(c) for each demand location, identification of one or more demand streams of bulk products and the monetary values of the bulk products that meet the property specification requirements of the demand stream;

(d) identification of a fleet of vehicles for carrying bulk products between supply locations and demand locations;

(e) data relating to the capacity of each vehicle in the fleet;

(f) data relating to the costs for transporting bulk products from the supply locations to the demand locations; and

(g) identification of one or more blending tanks located at a demand location or a supply location for receiving bulk product from a vehicle and discharging bulk product to a demand stream;

(II) a modeling application executable by the optimization apparatus to populate a mathematical model using the data file, the mathematical model comprising an objective function for net profit margin and a plurality of constraints;

wherein the constraints include one or more non-linear terms relating to the quantity or property of blending tank content;

(III) a solver engine operable by the optimization apparatus to obtain a solution to the mathematical model for maximizing the objective function for net profit margin.

28. A program storage device readable by a machine, tangibly embodying a program of instructions executable by the machine to perform method steps for determining the transportation of bulk products, said method steps comprising:

(I) reading a data file comprising;

(a) an identification of a plurality of supply locations and a plurality of demand locations;

(b) for each supply location, identification of one or more supply streams of bulk products and the monetary values of the bulk products from the supply stream;

(c) for each demand location, identification of one or more demand streams of bulk products and the monetary values of the bulk products that meet the property specification requirements of the demand stream;

(d) identification of a fleet of vehicles for carrying bulk products between supply locations and demand locations;

(e) data relating to the capacity of each vehicle in the fleet;

(f) data relating to the costs for transporting bulk products from the supply locations to the demand locations; and

(g) identification of one or more blending tanks located at a demand location or a supply location for receiving bulk product from a vehicle and discharging bulk product to a demand stream;

(II) using the data file to populate a mathematical model that comprises an objective function for net profit margin and a plurality of constraints;

wherein the constraints include one or more non-linear terms relating to the quantity or property of blending tank content;

(III) obtaining a solution to the mathematical model for maximizing the objective function for net profit margin.