ExxonMobil, a petroleum and natural gas company, operates in several countries worldwide. It provides several ranges of petroleum products, including clean fuels, lubricants, and high-value products and feedstock to several customers. This is completed through a complex supply chain between its refineries and customers. One of the main products ExxonMobil transports is vacuum gas oil (VGO). ExxonMobil transports several shiploads of VGO from Europe to the United States. In a year, it is estimated that ExxonMobil transports about 60–70 ships of VGO across the Atlantic Ocean. Hitherto, both ExxonMobil-managed vessels and third-party vessels were scheduled to transport VGO across the Atlantic through a cumbersome manual process. The whole process required the collaboration of several individuals across the supply chain organization. Several customized spreadsheets with special constraints, requirements, and economic trade-offs were used to determine the transportation schedule of the vessels. Some of the constraints included the following: 1. Constantly varying production and demand projections 2. Maximum and minimum inventory constraints 3. A pool of heterogeneous vessels (e.g., ships with varying speed, cargo size) 4. Vessels that load and discharge at multiple ports 5. Both ExxonMobil-managed and third-party supplies and ports 6. Complex transportation cost that includes variable overage and demurrage costs 7. Vessel size and draft limits for different ports The manual process could not determine the actual routes of vessels, the timing of each vessel, and the quantity of VGO loaded and discharged. In addition, consideration of the production and consumption data at several locations rendered the manual process burdensome and inefficient. Methodology/Solution A decision support tool that supported schedulers in planning an optimal schedule for ships to load, transport, and discharge VGO to and from multiple locations was developed. The problem was formulated as a mixed-integer linear programming problem. The solution had to satisfy requirements for routing, transportation, scheduling, and inventory management vis-à-vis varying production and demand profiles. A mathematical programming language, GAMS, was used for the problem formulation, and Microsoft Excel was used as the user interface. When the solver (ILOG CPLEX) is run, an optimal solution is reached at a point when the objective value of the incumbent solution stops improving. This stopping criterion is determined by the user during each program run. Results/Benefits It is expected that using the optimization model will lead to reduced shipping costs and less demurrage expenses. These would be achieved because the tool would be able to support higher utilization of ships and help make ship selection (e.g., Panamax versus Aframax) and design more optimal routing schedules. The researchers intend to further the research by exploring other alternate mathematical methods to solve the scheduling problem. They also intend to give the DSS tool the capability to consider multiple products for a pool of vessels.
Questions for Discussion
1. List three ways in which manual scheduling of ships could result in more operational costs as compared to the tool developed.
2. In what other ways can ExxonMobil leverage the decision support tool developed to expand and optimize their other business operations?
3. What are some strategic decisions that could be made by decision makers using the tool developed?
