Maximizing billboard coverage with limited resources and different objective goals plays a vital role in social activities. The maximalcoverage Billboard locationproblem (MCBLP) is complex, especially for multi -obj...
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Maximizing billboard coverage with limited resources and different objective goals plays a vital role in social activities. The maximalcoverage Billboard locationproblem (MCBLP) is complex, especially for multi -objective functions. A multi -objective spatial optimization model was developed using mixed -integer linear programming based on MCBLP to formulate the spatial optimization problem of determining billboard locations. Combining the distinctive features of locationproblems, we have developed a new approach called ReCovNet that utilizes Deep Reinforcement Learning (DRL) to solve the MCBLP. We applied the ReCovNet to address a real -world billboard locationproblem in New York City. To assess its performance, we implemented various algorithms such as Gurobi solver, Genetic Algorithm (GA) and a deep learning baseline called Attention Model (AM). The Gurobi reports the optimal solutions, while GA and AM serve as benchmark algorithms. Our proposed approach achieves a good balance between efficiency and accuracy and effectively solves MCBLP. The ReCovNet introduced in our study has potential to improve advertising effectiveness, and our proposed approach offers novel insights for addressing the MCBLP.
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