A Decision-Support Model for Holistic Energy-Sustainable Fleet Transition

The transition toward sustainable transport systems requires decision-support tools that help organizations navigate strategic choices under environmental, economic, and operational constraints. This study introduces the Holistic Multi-Period Fleet Planning Problem (HMPFPP), a nonlinear optimization model designed to support long-term, sustainability-oriented fleet modernization. The model integrates investment costs, operational performance, emission limits, and dynamic demand into a unified analytical framework, enabling organizations to assess the long-term consequences of their decisions. A notable feature of the HMPFPP is the inclusion of outsourcing as a strategic option, which expands the decision space and helps maintain service performance when internal fleet capacity is constrained. An illustrative ten-year scenario demonstrates that the model generates non-uniform but cost-efficient transition pathways, in which legacy vehicles are gradually replaced by cleaner technologies, and temporary fleet downsizing can be optimal during low-demand periods. Outsourcing is activated only when joint emission and budget constraints make fully internal service provision infeasible. Across the tested instance, the HMPFPP is solved within seconds on standard hardware, confirming its computational tractability for exploratory planning. Taken together, these results indicate that data-driven optimization based on the HMPFPP can provide transparent and robust support for sustainable fleet management and transition planning.