Maritime inventory routing with transshipment: the case of Yamal LNG

The LNG-ADP problem is a tactical planning problem for creating an annual delivery program (ADP) for a liquefied natural gas (LNG) producer. An ADP specifies the departure dates of the LNG carriers as well as the delivery dates at the different customers for a period of 12-18 months. The problem can be formulated as maritime inventory routing problem, as it is an important requirement to plan the deliveries such that inventory levels are kept within minimum and maximum limits at the customers as well as the LNG production facility. Inspired by the case of Yamal LNG, we propose a novel discrete-time formulation for the LNG-ADP problem with transshipment and intermediate storage. Our formulation also allows for waiting at the unloading ports. The problem is solved using a rolling horizon heuristic (RHH) for a case based on the Yamal LNG project. We study the impact of different RHH configurations on run time and solution quality. The results show that using a central period that is shorter than the forecast period provides the best objective function, whereas a central period that is longer than the forecast period improves run time. We also explore the effect of allowing waiting at the unloading ports. Waiting does not necessarily improve the objective function value, despite increasing the solution space. However, we observe a reduction in run time for instances where waiting is allowed.

Automated summary

The LNG-ADP problem is a tactical planning problem for creating an annual delivery program for a LNG producer. This study proposes a discrete-time formulation for the problem, which allows for transshipment, intermediate storage, and waiting at unloading ports. The impact of different heuristic configurations on run time and solution quality is investigated, and it is found that a shorter central period provides the best objective function while a longer central period improves run time. Allowing waiting can reduce run time, but may not necessarily improve the objective function value.