Supply chain design and planning for LNG as a transportation fuel

Increasing environmental and social concerns regarding the use of heavily polluting transportation fuels have led to an increasing interest in alternative fuels, such as liquefied natural gas (LNG). Supply chain management decisions play a critical role in realizing the potential environmental and economic advantages of LNG as a fuel. Yet, these decisions are complicated by two important challenges. First, both the distribution network and demand for LNG as a fuel are noticeably underdeveloped. Demand is expected to grow if the distribution network matures, yet such network investments require increased demand. Second, LNG is a cryogenic liquid that is constantly boiling-off. The boil-off not only reduces the quantity of the fuel, but also affects its quality. The consideration of these challenges requires developing new decision models that differ from those existing in the literature.This PhD thesis presents four projects that study and develop quantitative models in the domain of location-routing, inventory control and lot-sizing problems. The first project addresses the challenge of the currently underdeveloped distribution network for LNG as a fuel. Specifically, it introduces a novel location-routing problem related to finding the locations for establishing small-scale LNG storage facilities while considering the vehicle routes that fulfil the growing demand for LNG as a fuel from end-users. In the following three projects, we study new inventory management problems arising in the LNG distribution network, addressing the challenges related to the cryogenic nature of LNG. Specifically, these problems take into consideration—and propose solutions that mitigate the negative effects of—the quality deterioration of LNG. Together, these projects provide methodological contributions by developing new models and solution approaches that can find high quality solutions. Additionally, they provide insights into the design and planning of LNG distribution networks in practice