Grid-Based Supply Facility Location Planning
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Solution Overview
Problem
Current methods for planning and installing new fueling station infrastructure for alternative energy sources, such as hydrogen and diesel exhaust fluid, are complex and hinder quick execution due to the need for detailed journey data analysis, making it difficult to determine optimal locations and expansion stages for supply facilities.
Innovation Solution
A method using a processor circuit to divide a geographical region into grid cells, assess initial requirement values based on existing reference facilities, adjust demand values, and simulate supply potential to identify the most efficient locations for new supply facilities, ensuring a suitable level of expansion and coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex models based on detailed journey data analysis are used to determine optimal refueling station locations, then the precision of location selection is improved, but the computational complexity and time required for execution increase significantly
Solution Approach 1:
The geographical region is divided into a grid of discrete cells, transforming the continuous location selection problem into a discrete optimization problem. Each grid cell can be independently evaluated based on its characteristics and demand potential, simplifying the overall computational task while maintaining location selection precision.
Solution Approach 2:
The patent transforms the complex journey data analysis into simplified demand value calculations based on grid cell characteristics. By changing the parameters from detailed trajectory data to aggregated demand metrics per grid cell, the computational complexity is reduced while still identifying optimal locations.
2Measurement precision
If complex models based on detailed journey data analysis are used to determine optimal refueling station locations, then the precision of location selection is improved, but the time required for calculation and execution increases
Solution Approach 1:
Dividing the region into grid cells enables parallel processing of location evaluations. Each cell's demand value can be calculated independently and simultaneously, dramatically reducing total calculation time compared to sequential analysis of continuous spatial data.
Solution Approach 2:
The patent uses virtual placement of supply facilities in grid cells to simulate and evaluate their impact on demand coverage. This virtual modeling approach allows rapid iteration and evaluation of multiple location scenarios without requiring complex real-world simulations.
3Adaptability or versatility
If the supply network is expanded to achieve a predetermined level of development, then the coverage and accessibility of alternative fuels is improved, but the cost and complexity of infrastructure construction increases
Solution Approach 1:
The patent uses demand coverage values as a quantitative parameter to guide expansion decisions. By calculating and comparing demand coverage values for different grid cells, the system objectively determines which locations will provide the greatest benefit to supply network coverage, optimizing the expansion strategy.
Solution Approach 2:
The system calculates demand coverage values that reflect the impact of each potential supply facility on overall network coverage. This feedback mechanism guides sequential expansion decisions, ensuring that each new facility is placed to maximize its contribution to achieving the predetermined development level.
Data Source
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AI summary
The invention relates to a method for controlling a construction process of supply facilities (11) in a predetermined geographical region (10) in which reference facilities (12) are already located, wherein a processor circuit (16) divides the region (10) into grid cells (22') in a digital map (18) of the region (10) by means of a grid (22), and an initial demand value (T) is determined for each grid cell (22'), and its demand value (B) is reduced depending on a distance to a central grid cell (32) which already has a supply facility (11) in order to simulate a supply potential of the central grid cell (32) for the grid cell (22'), and in a virtual expansion phase (24) a demand coverage value (D) is determined for each grid cell (22) when a new supply facility (11) is at least virtually present.and in a selection phase (28) the grid cell (22') with the highest demand coverage value (D) is selected.