Bulk material ship routing and inventory management schedule optimization

• US 8,374,898 B2
• Filed: 09/01/2009
• Issued: 02/12/2013
• Est. Priority Date: 09/05/2008
• Status: Active Grant

• Current Assignee: ExxonMobil Research & Engineering Company (Exxon Mobil Corporation)
• Original Assignee: ExxonMobil Research & Engineering Company (Exxon Mobil Corporation)
• Inventors: El-Bakry, Amr S., Song, Jin-Hwa, Goel, Vikas, Furman, Kevin C., Carvallo, Federico D., Lee, Sangbum, Dreher, James, Miroyannis, Aristides, Connell, Richard Perry
• Primary Examiner(s): Jasmin, Lynda
• Assistant Examiner(s): FEENEY, BRETT A
• Application Number:
  US12/584,239
• Publication Number:
  US 20100088142A1
• Time in Patent Office: 1,260 Days
• Field of Search: None
• US Class Current: 705/7.11
• CPC Class Codes: G06Q 10/06312 Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
• CPC Class Codes: G06Q 10/08 Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders

Abstract

The present invention is a computational system to generate feasible marine transportation schedules given a proposed bulk material supply chain. The decision support tool will fulfill the objectives and scope defined above. The technology employed will require (a) the use of mathematical programming modeling techniques (mixed integer linear programming) to mathematically formulate the business problem, and (b) advanced proprietary solution methods to produce solutions satisfying all constraints of the mathematical model while optimizing an objective function. The decision support tool has the following capabilities: Read input data, Generate feasible bulk material ship Schedule, Evaluate bulk material ship Schedule with User-defined Objective Function, Allow Case comparison, Graphical user interface (GUI) for specifying input data and displaying results.

Claims

1. A computer-implemented method to plan and schedule bulk material distribution for the transportation of bulk materials from supply terminals to demand terminals, comprising:

providing a computational system;

executing, using the computational system, a plurality of computer-executable instructions, the executed instructions providing a method comprising;

a) obtaining input data specifying a plurality of requirements to be satisfied for a marine transportation schedule of a plurality of ships to meet predetermined objectives represented by an objective function,

b) determining a current feasible schedule from said input data, the current feasible schedule comprising a ship schedule for each of the plurality of ships,

c) evaluating said current feasible schedule to determine a current objective function value based on the predetermined objectives,

d) iteratively generating alternative feasible schedules, the iterative generation method comprising;

selecting a first ship and a second ship from the plurality of ships, the first ship having a first ship schedule in the current feasible schedule and the second ship having a second ship schedule in the current feasible schedule;

modifying the first ship schedule and the second ship schedule to generate an alternative feasible schedule;

evaluating the alternative feasible schedule to determine an alternative objective function value based on the predetermined objectives; and

selecting the current feasible schedule or the alternative feasible schedule to become the current feasible schedule based on the current objective function value and the alternative objective function value, and

e) generating, after said iterative generating of the alternative feasible schedules, an operational plan for ship scheduling based on the current feasible schedule,

wherein the objective function comprises at least a first constraint for generation of bulk material at a supply terminal, a second constraint for a maximum amount of bulk material at a supply terminal, a third constraint for reduction of bulk material at a demand terminal, and a fourth constraint for a minimum amount of bulk material at a demand terminal.

2. The method of claim 1 wherein said input data includes shipping input data, production terminal input data, demand terminal input data, constraints, rules, variables, and objectives.

3. The method of claim 1 wherein said input data includes one or more of time horizon, assumed travel times, throughputs and tolerance, inventory limits, vessel information, and initial system conditions.

4. The method of claim 3 wherein said vessel information includes capacity, speed, number, and route constraints.

5. The method of claim 3 wherein said initial system conditions include vessel locations and inventory levels.

6. The method of claim 3 wherein said input data further includes objectives.

7. The method of claim 6 wherein said objectives include one or more of deliver full annual volume, minimize time below inventory lower bound, and minimize time above inventory upper bound.

8. The method of claim 7 wherein said objectives further include minimize deviations from the current and the alternative feasible schedules.

9. The method according to claim 1 involving more than one ship, wherein:

i) the step of determining the current and the alternative feasible schedules comprises generating a minimum set of travel choices by eliminating infeasible choices from the time-space network, and

ii) the step of evaluating the current and the alternative feasible schedules includes assigning an initial voyage to each ship among terminals.

10. The computer-implemented method of claim 1, wherein selecting a pair of ships comprises:

selecting a first ship based on amount of slack in a schedule for the first ship using a sequence of terminals in the current feasible schedule; and

selecting a second ship based on an amount of demurrage time for the second ship at a terminal, wherein the first ship is present at the terminal during the demurrage time.

11. The computer-implemented method of claim 1, further comprising:

after iteratively generating the alternative feasible schedules and prior to generating the operational plan for ship scheduling, applying an unplanned event to the current feasible schedule to generate a modified feasible schedule;

optimizing the modified feasible schedule based on iteratively generating alternative modified feasible schedules by modifying schedules for pairs of ships;

determining an objective function value for the optimized modified feasible schedule, and

selecting the current feasible schedule or the optimized modified feasible schedule as the current feasible schedule based on the objective function values of the current feasible schedule and the optimized modified feasible schedule.