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

Optimisation of vendor-managed inventory systems

Imperial Users onl

An exact algorithm for the single-vehicle cyclic inventory routing problem

The single-vehicle cyclic inventory routing problem (SV CIRP) consists of a repetitive distribution of a product from a single depot to a selected subset of customers. For each customer that is selected for replenishments, the supplier collects a corresponding xed reward. The objective is to determine the subset of customers to replenish, the quantity of the product to be delivered to each, and to design the vehicle route so that the resulting pro t (di erence between the total reward and the total logistical cost) is maximized while preventing stockouts at each of the selected customers. In this paper, the SV CIRP is formulated as a mixed-integer program with a nonlinear objective function. After an e cient analysis of the problem, an exact algorithm for its solution is proposed. This exact algorithm requires only solutions of linear mixed-integer programs. Values of an insertion-based heuristic for this problem are compared to the optimal values obtained for a set of some test problems. In general the gap may get as large as 25%, which justi es the e ort to continue exploring and developing exact and approximation algorithms for the SV CIRP.Postprint (published version

The application of artificial intelligence techniques to large distributed networks

Data accessibility and transfer of information, including the land resources information system pilot, are structured as large computer information networks. These pilot efforts include the reduction of the difficulty to find and use data, reducing processing costs, and minimize incompatibility between data sources. Artificial Intelligence (AI) techniques were suggested to achieve these goals. The applicability of certain AI techniques are explored in the context of distributed problem solving systems and the pilot land data system (PLDS). The topics discussed include: PLDS and its data processing requirements, expert systems and PLDS, distributed problem solving systems, AI problem solving paradigms, query processing, and distributed data bases