This document summarizes the implementation of a decision support system (DSS) for ship scheduling at PETROBRAS, a Brazilian petroleum company. The DSS uses an algorithm to generate feasible schedules and calculates costs to determine the minimum cost schedule. During a 3-month test, the DSS achieved savings of 7.5% of operational costs, equivalent to $10 million. Over the longer term, the DSS was estimated to save the company $140 million from 2013-2016 and could be expanded to support fleet size determination and inventory management.

1. Decision Support System for
PETROBRAS Ship Scheduling
Gustavo Diz
Logistics Department, PETROBRAS
Luiz Felipe Scavarda
Department of Industrial Engineering, Pontifical Catholic University
Roger Rocha
Production Engineering Department, PETROBRAS
Silvio Hamacher
Department of Industrial Engineering, Pontifical Catholic University
May 19, 2014

2. Content
• Introduction
• Research Method
• Before the DSS Implementation
• The Decision Support System
• Lessons Learned from the DSS Implementation
• Analysis of the Results
• Conclusion

3. Introduction
• Maritime transportation is responsible for transporting
approximately 65 to 80 percent of the total volume of internationally
commercialized goods.
• Crude oil and its derivatives represent approximately 32 percent of
total cargo volume.
• The goal is to provide results from a real-life case of a long-haul ship
scheduling problem for crude oil transportation at PETROBRAS.

4. Petróleo Brasileiro
S.A. — Petrobras
• Since 1953
• Petroleum industry
• Controling significant oil
and energy assets in 16
countries in Africa, North
America, South America,
Europe, and Asia.

5. Research Method
1. Obtain a map of the company’s current ship scheduling status
2. Conduct unstructured interviews with the same interviewees to
select the most relevant scheduling information.
→focus on interactive functionality between the schedulers and
the DSS
3. Implement and test for 3 months
4. Approve and refine for 9 months

6. Before the DSS Implementation
• The import and export cargoes have volumes varying from 950,000 to
1,000,000 barrels.
• When time-chartered vessels’ capacity is not enough, it charter
vessels on a per-voyage basis in the spot market.
• Need to respect the commercially agreed-upon time window,
consider the operational restrictions, and seek the lowest cost.

8. Vessel Scheduling Decision Indicators:
• Decrease the idle time of the time-chartered vessel (demurrage)
• Increase the vessel efficiency (ton-miles)
• Could not define the minimum cost schedule
• Schedulers evaluated the indicators based on experience
• Schedulers could only simultaneously compare two or three schedule
alternatives

9. The Decision Support System
• Each cargo has only one loading and unloading port.
• The vessel is fully loaded at the first port and fully unloaded at the
second port.
• The standard speed of the vessels is 13.5 knots, which is the average
economical speed of PETROBRAS’ time-chartered fleet.
• The DSS structure follows the four-phase approach proposed in
Brown et al. (1987).

10. • Use an algorithm to generate schedules and create a complete set of
feasible schedules from the input data.
• the initial or fixed data that aim to describe the complete maritime
transportation environment
• the data for the scenarios, which describe the instance to be tested
• the cargo data that provide details about the set of cargoes to be
shipped
1

11. Schedule-generation Algorithm:
1. Generate all possible routes, respecting the time window in the
loading and unloading ports.
2. Match the time-chartered vessels with each route generated in the
previous step, respecting the time-window restrictions and
considering the availability, locations, and dates of each vessel.

12. • Calculate the costs of each feasible schedule generated in the first
phase.
• Daily cost
• Demurrage
• Fuel cost
• Port taxes
• Freight rate in the spot market
2

13. • Apply an IP model, such as the set-partitioning problem type.
3

15. • Apply an efficient solution procedure (commercial solver) to solve the
IP problem and use the AIMMS optimization platform, which uses
CPLEX to optimize the problem, to implement the DSS.
4

16. Lessons Learned from the DSS
Implementation
• Schedules may require manual adjustments to ensure feasibility.
• Some conditions can only be known after a vessel has been assigned
to transport specific cargo.
• Interaction helps to prevent scheduler resistance to accepting the
new scheduling methods.
• The DSS also provides perspectives that the schedulers do not
typically consider.

17. Analysis of the Results

18. • Based on the cost savings obtained during 2012, PETROBRAS
calculated the potential economic benefits of the DSS to be
approximately $140 million from 2013 to 2016.

19. Conclusion
• During the three-month test period, it achieved savings of
approximately 7.5 percent of the operational costs in crude oil long-
haul transport, which is equivalent to approximately $10 million,
while transporting 70 crude oil cargoes.
• Adapting the proposed model to a one-year period or longer, thereby
allowing PETROBRAS to use the model as a support tool to determine
fleet size, and incorporating inventory management at the loading
and unloading points into the schedule, thereby transforming the
problem into an inventory routing problem.