Construction & Improvement Heuristic for LNG Inventory Routing Problem

1. A construction and improvement heuristic for a large scale liquefied natural gas inventory routing problem Magnus Stålhane, Jørgen Glomvik Rakke, Christian Rørholt Moe, Marielle Christiansen, Kjetil Fagerholt and Henrik Andersson Department of Industrial Economics and Technology Management, NTNU 22.09.2009

2. Outline • Problem Description • Construction and Improvement Heuristic (CIH) • Computational Results • Future research

3. Gas Utilities Industries Exploitation & Production Liquefaction & Storage Shipping Regasification & Storage Electric Utilities Residential Problem Description • A combined large-scale route scheduling and inventory management problem for a producer and distributor of LNG • The goal is to create an annual delivery program (ADP) that: • Minimize cost of fulfilling the producers long-term contracts • Maximize profit from spot-contracts

6. A Large Problem • 30-50 LNG tankers • 8-20 long-term contracts • 1 year planning horizon • 300-600 deliveries • Two gas types: RLNG and LLNG • Heterogeneous fleet • Some contract specific ships

7. Assumptions • Unlimited number of spot ships available for chartering • Inventory management only on supply side • Discrete time (days) • Always spot-demand for LNG • Maintenance can be performed ”en-route” • A ship will only visit one regasification terminal on each voyage, and all loads have to be full ship loads

8. Maximize revenue from selling LNG in the spot market Add value of LNG in tank at end of year LNG Minimize transportation costs Penalize under-delivery Objective Function

9. Mathematical Model Berth constraints Inventory constraints Soft Demand constraints Routing constraints Maintenance constraints

10. Construction & Improvement Heuristic (CIH)

11. Construction & Improvement Heuristic (CIH)

12. Definition of a Scheduled Route • A feasible solution to the ADP planning problem consists of a set S of Scheduled Routes (SR), with SR = (v,c,t) • v is the ship sailing • c is the contract (destination) • t is the day loading starts at the loading port • The three parameters above implicitly give the day of delivery and the return day to the loading port

13. Construction Heuristic

14. Contract rankings • Two principal ideas: • Rank by volume left to be delivered • Rank by percentage of demand left to be delivered • Solution: • A combination of the two above. • If the difference in percentage is greater than some value α, rank by percentage • Otherwise, rank by volume • Spot contracts are given artificial demand equal to β times the excess production in a month • At the end of each month, deviations from contractual demands for long-term contracts are transferred to the next month

15. Ship rankings • Ships are prioritized in the following way • By how many contracts it may serve (few contracts prioritized) • By capacity to cost ratio (high ratio prioritized)

16. Lookahead parameter • Best lookahead parameter seems to be linked to the inventory to production ratio of each gas type. • Kg = floor( Inventory * days/total production) + σ • Where σ is an integer

17. Construction & Improvement Heuristic (CIH)

18. Local Search • Improves the ADP created by the construction heuristic • Neighborhood search by replacing/swapping ships v and contracts c in the Scheduled Routes (v,c,t)

19. Changing contract (destination) of a SR • Re-routing the destination of a Scheduled route from one contract to another • Replace (v,c,t) with (v,c*,t) where c≠ c* • Limited by the restrictions on which contracts the ship may serve • Limited by the routing constraints • c and c* must have demand for same type of LNG

20. Changing ship used on a SR • Replacing the ship used on a scheduled route • Replace (v,c,t) with (v*,c,t) where v ≠ v* • Limited by the restrictions on which contracts the ship may serve • Limited by the Inventory contraints • Limited by the routing contraints

21. Swapping ships between two SR • Remove a pair (v1,c1,t1) and (v2,c2,t2) from S, add pair (v2,c1,t1) and (v1,c2,t2) to S • Limited by inventory constraints • Limited by routing constraints • Both ships must be allowed to serve both contracts

22. Swapping contracts between two SR • Remove a pair (v1,c1,t1) and (v2, c2, t2) from S, add a pair (v1,c2,t1) and (v2,c1,t2) to S • Limited by routing constraints • Both ships must be allowed to serve both contracts • Both contracts must have demand for same type of LNG

23. Additional search moves • Adding a SR to the ADP, S = S U (v,c,t) • Deleting a SR from the ADP, S = S\ (v,c,t)

24. Construction & Improvement Heuristic (CIH)

25. Mathematical Programming Heuristic • Uses mathematical model with parts of solution fixed • Uses one feasible ADP as starting point • For each SR = (v,c,t) • If it is going to a long-term contract, we fix c and t • If it is going to a spot-contract, we fix t • If it is going to maintenance, we do nothing

26. Mathematical Programming Heuristic Variable generation: New constraints:

27. Computational Results (1:4) CIH-LS

28. Computational Results (2:4)

29. Computational Results (3:4)

30. Computational Results (4:4) • Provides very good solutions in a short period of time • Creates a feasible, low-cost ADP in less than a second. • Algorithm creates an ADP for ”all” combinations of parameters (α, β, σ) and selects the best • Total running time less than 30 minutes • Local search does improve the constructed ADP significantly • Mathematical programming may be used to improve ADP further

31. Concluding remarks and Future Research • Presented a heuristic solution approach to a large scale inventory routing problem. • CIH provides good solutions to the problem in short time • CIH is well suited for a Decision support system: • is flexible in time used • Deterministic • Look at Robustness and disruption management • Exact and other heuristic solution approaches • Improve lower bound

32. A construction and improvement heuristic for a large scale liquefied natural gas inventory routing problem Magnus Stålhane, Jørgen Glomvik Rakke, Christian Rørholt Moe, Marielle Christiansen, Kjetil Fagerholt and Henrik Andersson Department of Industrial Economics and Technology Management, NTNU 22.09.2009

33. A construction and improvement heuristic for a large scale liquefied natural gas inventory routing problem Magnus Stålhane, Jørgen Glomvik Rakke, Christian Rørholt Moe, Marielle Christiansen, Kjetil Fagerholt and Henrik Andersson Department of Industrial Economics and Technology Management, NTNU 22.09.2009