This research focuses on the complexities of integrating routing decisions, inventory management and pressure control in the context of the cryogenic logistics of Liquefied Natural Gas (LNG). This research aims to address these complexities by developing a planning method that considers the transportation and nitrogen cooling costs. The study evaluates the developed planning method through a case study at Rolande, which is a company that operates LNG and Bio-LNG refuelling stations in the Netherlands, Belgium and Germany.

This study considers three refrigeration methods for controlling the pressure level at refuelling stations. These are nitrogen cooling, offload cooling and logistic trailer cooling. The identified key performance indicators that are used to evaluate the develop planning method are: Total Costs, Total Transportation Costs, Total Nitrogen Cooling Costs and Cost per Kilogram. Furthermore, the key constraints and variables for the integrated control of the LNG logistics are identified.

The planning methodology was developed through three steps. First a Full Mixed-Integer Linear Programming (MILP) model was developed. Second, the Full-MILP model was refined to a Simplified MILP model. This Simplified MILP model was improved with the rolling horizon approach and a pre-solve process. The rolling horizon approach divided the planning horizon into smaller manageable time blocks. The pre-solve process identified the critical stations for each day to reduce the number of nodes in the network.

The proposed planning methodology was experimented in the case study that involved a network of 19 refuelling stations in the Netherlands and Belgium. The sensitivity analysis indicated that the model was sensitive to the vehicle capacity. Therefore, the pre-solve process was extended with determining the supply of LNG. The case study results revealed that the model is able to solve a seven-day planning horizon, while maintain the inventory and pressure levels within the specified bounds. However, the model can become computationally complex for days with high number of critical stations and cool vehicles.

This research contributes to inland LNG logistics by addressing the integration of routing, inventory and pressure management. Future studies should focus on a comparative analysis with heuristics, experimenting the feasibility and computational time with soft inventory and pressure bounds, and model a non-linear offload cooling effect to improve the realism of the model.