A Dynamic Strategic Plan For The Transition To AClean Bus Fleet Using Multi-Stage Stochastic Programming

In recent years, the transition to clean bus fleets has accelerated. Although thistransition might bring environmental and economic benefits, it requires a long-termstrategic plan due to the large investment costs involved. This thesis proposesa multi-stage stochastic program to optimize strategic plans for the clean bus fleettransition that explicitly considers the uncertainty scenarios in the cost and efficiencyimprovements of clean buses.Our optimization model minimizes the total expected cost subject to emission targets,budget restrictions, and several other operational considerations. We proposea new forecasting approach that captures the correlation between technological improvementsto obtain realistic future pathways for Battery Electric Buses (BEBs)and Hydrogen Fuel Cell Buses (HFCBs), which are then given to the multi-stagestochastic program as scenarios. We also utilize a physics-based model for BEBs toaccurately capture their energy consumption and recharging needs.As a case study, we focus on the complex public bus network of Istanbul, whichaims to transition to a clean bus fleet by 2050. Utilizing real datasets, we solve afive-stage stochastic program spanning a 25-year planning horizon that involves 256scenarios to obtain dynamic strategic plans that can be used by policymakers.Our results suggest that BEBs are more advantageous than HFCBs, even in slow BEB but fast HFCB development scenarios. We also conduct several sensitivityanalyses to understand the effects of intermediate emission targets, budget limitations,and energy prices.