Liquefied natural gas portfolio optimisation under IMO short term measures for annual delivery planning

Mostafazadeh, P (2026) Liquefied natural gas portfolio optimisation under IMO short term measures for annual delivery planning. Doctoral thesis, Liverpool John Moores University.

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Abstract

The global effort to mitigate climate change and transition towards net-zero emissions has intensified the need for cleaner energy carriers. Low-carbon fuels such as liquefied natural gas (LNG), hydrogen, ammonia, and methanol have emerged as viable alternatives to conventional fossil fuels due to their lower greenhouse gas emissions. They are crucial for decarbonising shipping and transport. However, the production of these fuels is often geographically concentrated, necessitating long-distance transportation, primarily via maritime shipping, to meet international demand. The shipping of cryogenic fuels presents considerable logistical and environmental challenges, particularly in the context of tightening international regulations and the growing emphasis on decarbonisation across the maritime sector.
This thesis investigates the optimisation of maritime logistics for low-carbon cryogenic fuels, focusing on both operational efficiency and environmental compliance. Special attention is given to LNG, which is currently the most commercially mature and widely transported low-carbon fuel. The work is presented through the development and application of advanced optimisation models that consider a wide range of technical and economic constraints. These include routing, vessel scheduling, speed optimisation, cargo pairing between suppliers and customers, time-varying fuel prices, and operational costs. Moreover, cryogenic shipping constraints such as fuel boil-off rates, the management of heel (minimum retained fuel for tank cooling), fuel consumption rate, and compatibility of the vessel and port are explicitly modelled to reflect real-world operational conditions.
To address these multifaceted challenges, the research introduces a novel two-pronged optimisation framework. A Mixed-Integer Linear Programming (MILP) model is developed for high-fidelity, small- to medium-scale scenarios, enabling precise scheduling and resource allocation. For larger, industrial-scale applications involving a heterogeneous fleet and hundreds of cargo movements, a genetic-based metaheuristic algorithm (GBMHA) is proposed. This hybrid framework offers both exact and heuristic solutions, balancing computational feasibility with solution quality.
The applicability of this framework is demonstrated using real operational data provided by a global LNG supplier. The findings indicate that while the MILP model efficiently handles small- to medium-scale deliveries, the metaheuristic approach successfully scales to manage over 130 transactions and 60 vessels, encompassing a delivery planning horizon of more than 18 months. These outcomes validate the robustness and scalability of the proposed approach in supporting strategic decision-making for Annual Delivery Planning (ADP).
In parallel, the thesis examines the impact of emerging environmental regulations introduced by the International Maritime Organization (IMO), particularly the Carbon Intensity Indicator (CII). Using the same modelling framework, scenarios are tested to understand how CII compliance affects LNG portfolio optimisation. The analysis indicates that ensuring compliance generally results in a reduction in overall profitability of between 0.3% and 10%, depending on the extent to which fleets transition towards greener configurations. However, strategic adjustments in vessel selection, such as favouring smaller ships with better emissions profiles and route optimisation can help offset these losses while improving environmental performance.
The analysis conducted in this study further reveals that greener fleet strategies do not automatically result in reduced CO₂ emissions, underscoring the complex interplay between regulatory compliance, environmental performance, and economic viability.
This research highlights the importance of integrated, data-driven planning in balancing operational, economic, and regulatory pressures. It underscores that achieving environmental targets in the maritime sector is not solely a matter of compliance, but one that requires strategic innovation and optimisation. By contributing novel modelling tools and empirical insights, this thesis advances the field of sustainable maritime logistics and provides actionable guidance for industry stakeholders navigating the transition to low-carbon fuel transport.

Item Type:	Thesis (Doctoral)
Uncontrolled Keywords:	Industrial Decision Support; Liquefied Natural Gas (LNG); Carbon Intensity Indicator (CII); Mixed-Integer Linear Programming (MILP); Hybrid Genetic Algorithm; Large-Scale Optimisation
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences Q Science > QA Mathematics > QA76 Computer software T Technology > TD Environmental technology. Sanitary engineering V Naval Science > VM Naval architecture. Shipbuilding. Marine engineering
Divisions:	Engineering
Date of acceptance:	12 June 2026
Date of first compliant Open Access:	2 July 2026
Date Deposited:	02 Jul 2026 09:56
Last Modified:	02 Jul 2026 09:56
DOI or ID number:	10.24377/LJMU.t.00028908
Supervisors:	Matellini, B, Nguyen, TT and Kavakeb, S
URI:	https://researchonline.ljmu.ac.uk/id/eprint/28908

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