LNG Allocation Strategy Using Optimization Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for managing liquefied natural gas (LNG) projects lack robust planning and optimization tools, leading to sub-optimal ship scheduling, inadequate consideration of contractual flexibility, and high computational complexity in supply chain design, which complicates the evolution of the LNG market from long-term to short-term contracts.
Innovation Solution
A method and system that utilize optimization models and algorithms to develop a long-term strategy for LNG allocation, incorporating uncertainty and accounting for shipping capacity limitations, contractual obligations, and market opportunities, including ship scheduling, optionality planning, and supply chain design optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If simple spreadsheets are used for ship scheduling, then ease of operation is improved, but productivity and optimization capability deteriorate
Solution Approach 1:
The patent replaces manual spreadsheet-based scheduling with an automated optimization system that uses mathematical models and algorithms to generate ship schedules. This substitution transforms the mechanical process of manual planning into an automated computational system that can handle complex constraints and objectives, thereby improving productivity while maintaining ease of operation through automated generation of optimized schedules.
2Productivity
If detailed optimization models are used for ship scheduling, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex optimization problem into distinct modules: supply chain design model, ship scheduling model, and simulation model. Each module handles specific aspects of the scheduling problem independently, allowing the system to manage complexity through modular architecture while maintaining high productivity through integrated optimization across all segments.
Solution Approach 2:
The patent introduces a simulation model as an intermediary between the optimization models and real-world operations. This simulation component validates the feasibility of optimized schedules and provides feedback to refine the optimization models, thereby managing system complexity while enhancing productivity through iterative improvement.
3Productivity
If integrated models for supply chain design and ship scheduling are used, then productivity is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The patent implements feedback mechanisms where simulation results feed back into the optimization models to refine scheduling decisions. This closed-loop system continuously measures performance metrics from simulations and uses this information to adjust optimization parameters, thereby managing the complexity of integrated models while enhancing productivity through data-driven iterative improvement.
4Productivity
If robust planning tools are used for LNG allocation, then profitability is improved, but device complexity increases
Solution Approach 1:
The patent employs dynamic optimization models that can adapt to changing market conditions, contractual obligations, and operational constraints. The system dynamically adjusts ship schedules and LNG allocation strategies based on real-time inputs, thereby improving profitability through flexible decision-making while managing complexity through adaptive algorithms that respond to changing conditions rather than requiring static complex structures.
Data Source
AI summary
A method is disclosed for developing a long-term strategy for allocating a supply of liquefied natural gas (LNG) while adhering to limitations of available shipping capacity An LNG market is modeled using one or more optimization models. The LNG market includes at least one buyer of LNG, at least one seller of LNG, and at least one means of transporting LNG. A plurality of inputs relevant to the LNG market are accepted. The inputs are configured to be input into the optimization models. One or more solution algorithms are interfaced with the optimization models. The optimization models are run using the interfaced solution algorithms to identify potential options in the LNG market. Uncertainty is accounted for in the identified potential options. The identified potential options are outputted.