The car rental fleet management literature from its inception has made bounds in identifying key sub-problems which are faced by car rental companies in operating sustainably in the industry such as pool segmentation, fleet size and mix, fleet deployment, fleet assignment, capacity allocation, and price discovery. Previous research has primarily relied on isolating sub-problems to provide solutions, and thus have been unable to contextualize global interrelatedness, which is a necessary step in the ‘call for realism’ outlined in Oliveira et al. (2017). In my thesis, I thus model up to 7 key sub-problems simultaneously using a Monte Carlo simulation within which the physical and financial dimensions are implemented via Statistical Activity Cost Analysis (SACA). Financial statements are translated from simulated data to display financial outcomes from operational movements which is necessary for informed risk management. The most advanced simulation version considered 133 vehicles, 7 vehicle groups, 3 stations, 27 trip types, rebalancing capability, limited cascading upgrades, seasonal demand variability, and reservation arrivals and price setting based upon real data. These factors demonstrated that the inclusion of a maintenance regime in my simulation extension (V7.1) encompass more realistic assumptions from a fleet and revenue management perspective.