In the realm of methane emission monitoring at oil and gas sites, Klise et al. developed a sensor placement optimization framework designed to enhance the detection capabilities of sensor networks. This framework begins by employing site-specific wind data and emission characteristics to create simulated emission scenarios, thereby closely approximating the actual conditions present at the site. Following this, the framework applies mixed-integer linear programming (MILP) to identify optimal sensor placements. Klise et al. showcased their methodology through a case study within a confined space of 100 x 100 x 10 meters, arranging potential sensor locations at intervals of 10 meters horizontally and 1 meter vertically, culminating in a search space comprising 100,000 potential sensor locations. This complexity is further amplified by introducing three sensor categories with different detection threshold and cost, expanding the search space to 300,000 distinct configurations. Moreover, to refine the simulation of real-world conditions, 1,200 emission scenarios were generated, capturing a range of meteorological conditions and emission patterns. Building upon Klise et al. 's foundational work, Zi et al. introduced a novel approach by incorporating wind condition uncertainties and employing a distributionally robust optimization (DRO) strategy to bolster the sensor network's detection robustness, particularly in worst-case scenarios. This methodology was evaluated using the same spatial domain as in Klise et al. 's study, demonstrating significant improvements in detection capabilities. Both studies leveraged MILP for optimization, showcasing its efficacy in smaller search spaces. However practical applications such as reducing sensor spatial resolution to 1 meter on oil gas sites or extending the analysis to larger regions reveal the limitations of MILP due to its computational demands, which escalate exponentially with the increase in decision variables -in this context, the potential sensor locations. Although advanced computational strategies like branch-and-bound or branch-and-cut can alleviate some computational burdens, the nature of MILP algorithm means that solving them remains NP-hard in the most challenging scenarios.