Publication:
A rebalancing methodology for a dockless scooter rental service

dc.contributor.advisor Artiles-León, Noel
dc.contributor.author Villa Zapata, Lina Marcela
dc.contributor.college College of Engineering en_US
dc.contributor.committee Rodríguez-Román, Daniel
dc.contributor.committee Ferrer-Alameda, Mercedes
dc.contributor.committee Vásquez-Urbano, Pedro
dc.contributor.department Department of Industrial Engineering en_US
dc.contributor.representative Rodríguez-Martínez, Manuel
dc.date.accessioned 2021-11-30T14:18:37Z
dc.date.available 2021-11-30T14:18:37Z
dc.date.issued 2021-11-29
dc.description.abstract A comprehensive methodology is proposed to rebalance a scooter rental service to avoid shortages. This mode of transportation has become an environmentally friendly alternative for big and small cities around the world. However, because there are no docks that limit the number of scooters in each zone, large accumulations may occur at certain times of the day. Therefore, the goal is to use data analysis, statistics, and optimization tools to decide how many scooters to move from which zone to which zone before a shortage is presented. A scooter rental service in Mayagüez, Puerto Rico was used as a case study to develop the proposed methodology. First, the initial GPS locations were grouped to create virtual stations that would serve to define the scooters' drop-off locations in rebalancing operation. Second, a prediction model was formulated based on factors that affect the demand. Finally, a multi-objective optimization problem was developed to minimize the travel distance in the rebalance operation and the deficit related to the system capacity. The evaluation of the model was made based on historical data and the deliverable is the number of scooters that must be in each zone considering the season, day of the week, and time of day. en_US
dc.description.abstract Se propone una metodología integrada para un servicio de alquiler de patinetas, que no tiene estaciones definidas. Este modo de transporte se ha convertido en una alternativa amigable para el medio ambiente en ciudades grandes y pequeñas de todo el mundo. Sin embargo, debido a que no hay muelles que limiten el número de patinetas en cada zona, pueden ocurrir grandes acumulaciones en ciertos momentos del día. Por lo tanto, el objetivo es utilizar análisis de datos, herramientas de estadística y de optimización para decidir cuántas patinetas mover de qué zona a qué zona. Se utilizó un servicio de alquiler de patinetas en Mayagüez, Puerto Rico como caso de estudio para desarrollar la metodología propuesta. Primero, las ubicaciones iniciales del GPS se agruparon para crear estaciones virtuales que servirían para definir las ubicaciones de entrega de las patinetas en la operación de rebalanceo. Segundo, se formuló un modelo de predicción basado en factores que afectan la demanda. Finalmente, se desarrolló un problema de optimización multiobjetivo para minimizar la distancia de recorrido en la operación de rebalanceo y el déficit relacionado con la capacidad del sistema. La evaluación del modelo se realizó con base a datos históricos y el entregable es el número de patinetas que deben estar en cada zona considerando la temporada, día de la semana y hora del día. en_US
dc.description.graduationSemester Fall en_US
dc.description.graduationYear 2021 en_US
dc.description.sponsorship Investigation subsidized with funds from the National Institute for Congestion Reduction (NICR) en_US
dc.identifier.uri https://hdl.handle.net/20.500.11801/2829
dc.language.iso en en_US
dc.rights CC0 1.0 Universal *
dc.rights.holder (c) 2021 Lina Marcela Villa Zapata en_US
dc.rights.uri http://creativecommons.org/publicdomain/zero/1.0/ *
dc.subject Rebalancing en_US
dc.subject Multi-objective optimization problem en_US
dc.subject Dockless en_US
dc.subject Clustering en_US
dc.title A rebalancing methodology for a dockless scooter rental service en_US
dc.type Thesis en_US
dspace.entity.type Publication
thesis.degree.discipline Industrial Engineering en_US
thesis.degree.level M.S. en_US
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