Using Geopandas for locating virtual stations in a free-floating bike sharing system.

Free-floating bike-sharing systems can have a positive influence on the mobility of urban centers and developing efficient localization strategies is crucial to avoid crowding at peak times and increase service availability. Our study aims to efficiently resolve the location of virtual bike stations in a Latin American city through a geospatial data wrangling methodology that allows us to respond opportunely to the potential demand forecasted for the city. This approach is implemented in Python, and it uses the Geopandas and LocalSolver libraries to determine locations for the virtual bike stations that maximize the system coverage. The decision-making process is supported by a binary integer mathematical programming model, and the instances are built from intercity travel surveys that provide realistic data based on travel demand. The developed decision support system prototype provides a recommendation about where virtual bike stations should be located during peak hours and improve general availability by more than 37%. Moreover, when the system's users participate in the relocation of bicycles, the model can eliminate up to 80% of the CO2 emissions and nearly 50% of the operational costs associated with the relocation process.

Best compromise nutritional menus for childhood obesity.

Childhood obesity is an undeniable reality that has rapidly increased in many countries. Obesity at an early age not only increases the risks of chronic diseases but also produces a problem for the whole healthcare system. One way to alleviate this problem is to provide each patient with an appropriate menu that is defined by a mathematical model. Existing mathematical models only partially address the objective and constraints of childhood obesity; therefore, the solutions provided are insufficient for health specialists to prepare nutritional menus for individual patients. This manuscript proposes a multiobjective mathematical programming model to aid in healthy nutritional menu planning that may prevent childhood obesity. This model provides a plan for combinations and amounts of food across different schedules and daily meals. This approach minimizes the major risk factors of childhood obesity (i.e., glycemic load and cholesterol intake). In addition, this approach considers the minimization of nutritional mismatch and total cost. The model is solved using a deterministic method and two metaheuristic methods. Test instances associated with children aged 4-18 years were created with the support of health professionals to complete this numerical study. The quality of the solutions generated using the three methods was similar, but the metaheuristic methods provided solutions in a shorter computational time. These results are submitted to statistical hypothesis tests to be validated. The numerical results indicate proper guidelines for personalized plans for individual children.