The problem we are addressing is the critical challenge of resource allocation for sustainable solutions in cities. Our focus is on how to meet environmental standards effectively by choosing efficient solutions while staying within budget.The challenge cities face is resource allocation. Urban sustainability demands meeting environmental standards within tight and specific budgets. The task is made more complex by the variety of solutions available, each with different costs, efficiencies, and impacts. Quantifying and comparing these options for optimal allocation is both crucial and difficult.
The suggested solutions are focused on rooftops and facades, which are often underutilized. These spaces do not require land acquisition, making them cost-efficient to develop. By repurposing these areas, cities can implement sustainable solutions without expanding urban footprints.
To have quantifiable data we need a location identification & compatibility check process. In the first step, from the 3D model of Barcelona, assuming blank walls are aligned orthogonally to the primary road axis, we identified unused walls. Using a sun exposure study with LadyBug, we calculated the sun exposure duration for each wall. Finally, we compiled this data into a CSV file containing the latitude and longitude of each wall, its sun exposure data, and its area.
In the second step, locations are being analyzed based on key factors like the type of surface (wall or rooftop), air quality, sun exposure. These inputs are processed to generate compatibility scores for three possible solutions: farming, air-purifying plants, and solar energy capturing.
Based on the score, the cases are being further analyzed the system by processing additional factors.
Precise, quantifiable outputs can then be generated, such as CO₂ absorbed, pollutant reduction capacity, or energy generation potential, providing actionable results for each solution.
To address the main challenge, we can consider four key inputs: the cost and effectiveness of each solution, budget constraints, and environmental impact. This allows us to evaluate the trade-offs between different options and prioritize actions based on both their feasibility and contribution to sustainability goals.
The problem itself is complex. We are working with large, multidimensional datasets that contain numerous variables and constraints. The interplay of these factors creates countless potential combinations. This makes traditional resource allocation methods inefficient and ineffective.
Our proposal is to use evolutionary algorithm to optimize resource allocation.
This approach is ideal for complex, constraint-heavy problems like ours.
