Intra-City Call-Taxi Fleet Sizing using Petri Net Embedded Simulation Optimization

Abstract

Call-taxi has seen tremendous growth during the past two decades and the smartphone app based call-taxi hiring for intra-city commuting has not only provided greater visibility to customers but also increased their expectations. Fleet sizing is an important decision to a call-taxi aggregator that strikes a balance between customer service in terms of call-taxi availability and the utilization of the fleet for a day’s forecasted demand. An optimization via simulation approach is developed for optimizing call-taxi fleet size. The simulation model embeds a Petri Net model of a call-taxi system where the customer requests and travel times are stochastic. Three optimization models are used to determine the fleet sizes, which are compared and inferences drawn. The developed approach can serve as a template for decision support tool for call-taxi service providers.