Optimization of Portable Electric Vehicle Charging Station Deployment Using a Mixed Integer Programming Approach

Abstract

. This study addresses the supply and demand imbalance in electric vehicle (EV) infrastructure by proposing a strategic optimization model for the rollout of portable charging stations. Going beyond the problems of fixed facility locations, we develop a multi-period Mixed-Integer Programming (MIP) model that treats portability as a dynamic buffer to address demand variability over time. The objective is to maximize total net profit, which is a combination of net operational revenues and vital cost dimensions: fixed deployment costs, marginal operational costs, penalties on unserved demand, and logistical relocation costs incurred in moving units across planning periods. The mathematical program is modeled in GAMS and optimized to the global optimum using the CPLEX solver, with a full-factorial Design of Experiments (DOE) across different operational conditions. Such situations are used to systematically analyze the interactions among demand intensity, relocation costs, and penalty multipliers. Qualitative analysis shows the penalty cost of unserved demand has a greater influence in profitability compared to the cost of installing and operating. The analysis of the study finds an optimal operating point at which the system is in perfect balance between financial gains and service reliability. Moreover, we propose a definition of an Operational Lockdown Zone in which system stagnation occurs when the cost of over-relocation exceeds the marginal gains from service. The findings indicate that demand-responsive planning and optimized portability can provide a better buffer to uncertainty than the conventional budget expansion. The suggested model can therefore be a powerful decision-support system for developing sustainable, adaptive, and resource-efficient EV charging networks.