METHOD FOR OPTIMIZING A TRANSPORTATION SCHEME

• US 20100287073A1
• Filed: 04/29/2010
• Published: 11/11/2010
• Est. Priority Date: 05/05/2009
• Status: Abandoned Application

• Current Assignee: ExxonMobil Research & Engineering Company (Exxon Mobil Corporation)
• Original Assignee: ExxonMobil Research & Engineering Company (Exxon Mobil Corporation)
• Inventors: Liok, Felix, Osmer, Mark, Chua, LeonaAnn, Furman, Kevin C., KOCIS, Gary R., Wheaton, Thomas A., Warrick, Philip H., Song, Jin-Hwa
• Application Number:
  US12/770,393
• Publication Number:
  US 20100287073A1
• Time in Patent Office: Days
• US Class Current: 705/28
• CPC Class Codes: G06Q 10/047 Optimisation of routes or paths, e.g. travelling salesman problem
• CPC Class Codes: G06Q 10/08355 Routing methods
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders

Abstract

A method and apparatus for optimal transporting of cargo is provided. The method includes optimizing a plurality of transportation decisions and mechanically transporting cargo through movement of a plurality of vehicles in accordance with a set of optimized transportation decisions. The decisions include transportation routes and schedules for the transportation vehicles, allocation of cargo to be transported to one or more demand locations by the transportation vehicles, nomination of cargo pickup by the transportation vehicles from the one or more supply locations, the use of specialized transportation locations, and vehicle assignments for each of the transportation vehicles. The set of decisions is optimized by collecting data relating to the various transportation decisions, using the data collected as part of a mixed integer linear programming model, and obtaining a solution to the model to arrive at a set of optimized transportation decisions.

Claims

1. A method for transporting bulk materials, 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, information relating to its physical location and bulk material availability;

(c) for each demand location, information relating to its physical location and bulk material requirements;

(d) an identification of a fleet of vehicles for transporting bulk materials from the supply locations to the demand locations, and information relating to the availability, cost, and capacity of each vehicle;

(e) for each type of bulk material, information relating to the economic value of the type of bulk material according to its potential dispositions;

(f) information relating to the cost of transporting the bulk materials;

(II) formulating a mathematical model comprising an objective function for net profit margin and a plurality of constraints;

wherein the objective function includes decision variables relating to supply nomination parameters, allocation of the supply of bulk materials to the requirements at the demand locations, and voyages by the vehicles between the supply locations and the demand locations;

(III) populating the mathematical model with data from the data set or parameters calculated using data from the data set;

(IV) obtaining one or more solutions to the mathematical model; and

(V) physically transporting the bulk materials based upon a solution to the mathematical model.

2. The method of claim 1, wherein the decision variables relating to supply nomination parameters include decision variables for the types of bulk material to be nominated, the volume of bulk materials to be nominated, and the schedule for loading the bulk materials at the supply locations.

3. The method of claim 1, wherein the decision variables relating to the allocation of the supply of bulk materials include decision variables for the discharge volumes of the bulk materials at each of the demand locations.

4. The method of claim 1, wherein the decision variables relating to the voyages by the vehicles include decision variables for the assignment of vehicles to the voyages and the sequence of supply and demand location visits, loadings, and discharges for each of the vehicles.

5. The method of claim 1, wherein the information relating to the bulk material availability at the supply locations includes information relating to types of bulk material available, volumes of bulk materials available, and temporal availability of bulk materials.

6. The method of claim 1, wherein the information relating to the bulk material requirements at the demand locations includes information relating to demand location segregations, types of bulk materials that are acceptable for each segregation, inventory capacity of each segregation, processing rates of each segregation, types of bulk material required, volumes of bulk material required including tolerances, and temporal requirements for bulk material delivery.

7. The method of claim 1, wherein the information relating to the availability of the vehicles include the temporal availability and physical location of the vehicles.

8. The method of claim 1, wherein the data set further comprises one or more of the following information relating to:

vehicle restrictions at the supply locations, demand locations, or voyages;

speed and fuel consumption of the vehicles;

vehicle costs;

the categorization of supply locations and demand locations into geographic regions;

limits for each supply location and demand location;

fees or tariffs for each supply location and demand location;

voyage fees, tariffs, or tolls;

freight costs; and

leg distances.

9. The method of claim 8, wherein the data set further comprises one or more of the following information relating to:

spot costs and term costs;

draft limits, cargo limits, and berth limits for each supply location and demand location;

canal tolls;

drop and pick or repackaging fees; and

pipeline costs.

10. The method of claim 1, wherein the constraints for the mathematical model include constraints relating to processing limitations at the demand locations.

11. The method of claim 10, wherein the constraints relating to processing limitations include constraints for blending specifications or blend-down ratios.

12. The method of claim 1, wherein the constraints for the mathematical model include the ratability of the supply nominations.

13. The method of claim 1, wherein the data set includes information relating to the categorization of demand locations into geographic regions, and wherein the constraints for the MIP model include requirements relating to minimum or maximum amounts of cargo, with respect to each geographic region, to be delivered by the vehicles or retained at the region.

14. The method of claim 1, wherein the mathematical model is a mixed-integer programming model.

15. The method of claim 1, wherein the mixed-integer programming model is a mixed-integer linear programming model.

16. The method of claim 1, wherein obtaining the one or more solutions to the mathematical model comprises the steps of:

obtaining a first solution by removing one or more groups of constraints of the mathematical model; and

obtaining a second solution by solving a subproblem of the mathematical model in which, based on the first solution, one or more decision variables of the mathematical model are held constant or are disregarded.

17. The method of claim 16, wherein the constraints for the mathematical model include inventory constraints and blending constraints, and wherein the first solution is obtained by ignoring the inventory constraints, the blending constraints, or both.

18. The method of claim 16, wherein obtaining the one or more solutions to the mathematical model further comprises the steps of:

obtaining a third solution by a local search heuristic using the second solution as an initial feasible solution; and

obtaining a fourth solution by reducing the solution space for the mathematical model.

19. The method of claim 18, wherein the second solution is obtained by a construction heuristic, the third solution is obtained by an improvement heuristic, and the fourth solution is obtained by a solution space reduction.

20. The method of claim 16, wherein the first solution is an upper bound for the objective function value and the second solution is a lower bound for the objective function value.

21. The method of claim 16, wherein the first solution includes a determination of a selected set of voyages, and wherein the second solution is obtained by solving a subproblem of the mathematical model in which decision variables relating to the non-selected voyages are disregarded.

22. A computer apparatus 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, information relating to its physical location and bulk material availability;

(c) for each demand location, information relating to its physical location and bulk material requirements;

(d) an identification of a fleet of vehicles for transporting bulk materials from the supply locations to the demand locations, and information relating to the availability, cost, and capacity of each vehicle;

(e) for each type of bulk material, information relating to the economic value of the type of bulk material;

(f) information relating to the cost of transporting the bulk materials;

(II) a modeling application executable by the computer apparatus to populate a mathematical model that comprises an objective function for net profit margin and a plurality of constraints;

wherein the objective function includes decision variables relating to supply nomination parameters, allocation of the supply of bulk materials to the requirements at the demand locations, and voyages by the vehicles between the supply locations and the demand locations;

(III) a solver operable by the computer apparatus to obtain one or more solutions to the mathematical model.

23. The computer apparatus of claim 22, further comprising a display for displaying an output based on a solution to the mathematical model.

24. A non-transitory computer-readable storage medium containing a program of instructions executable by a computer to perform method steps for determining the transportation of bulk materials, the 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, information relating to its physical location and bulk material availability;

(c) for each demand location, information relating to its physical location and bulk material requirements;

(d) an identification of a fleet of vehicles for transporting bulk materials from the supply locations to the demand locations, and information relating to the availability, cost, and capacity of each vehicle;

(e) for each type of bulk material, information relating to the economic value of the type of bulk material;

(f) information relating to the cost of transporting the bulk materials;

(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 objective function includes decision variables relating to supply nomination parameters, allocation of the supply of bulk materials to the requirements at the demand locations, and voyages by the vehicles between the supply locations and the demand locations;

(III) obtaining one or more solutions to the mathematical model.

25. The computer-readable storage medium of claim 24, wherein the method steps further comprise creating an output based on a solution to the mathematical model.