Obnoxious facility location models and solution algorithms

In this thesis we focused on the strategic selection of locations for obnoxious facilities to minimize their negative impact on communities and the environment. We explored two algorithms, the genetic algorithm and the greedy algorithm, to address these challenging NP-hard problems. Additionally, we compared the solutions obtained from these algorithms with exact methods. The genetic algorithm systematically explores potential facility sites, incorporating diverse criteria and constraints for balanced solutions. Its iterative approach refines solutions to find optimal or near-optimal outcomes. The greedy algorithm, on the other hand, makes locally optimal choices efficiently, especially when immediate gains are crucial. Through comprehensive experimentation, we compared the performance of both algorithms with exact methods. While exact methods provide guarantees of optimality, they can be computationally intensive for large-scale problems. In contrast, the genetic algorithm efficiently explores a wide solution space, offering a promising balance between solution quality and computational effort. The greedy algorithm provides satisfactory results in computationally efficient scenarios. By combining the strengths of both algorithms and evaluating their performance against exact methods, decision-makers can identify optimal facility locations that minimize harmful effects on communities and the environment. This research contributes to an effective approach for mitigating harm caused by obnoxious facilities.