The rapid growth of urban populations and the increasing need for sustainable transportation solutions have prompted a shift towards electric buses in public transit systems. However, the effective management of mixed fleets consisting of both electric and diesel buses poses significant operational challenges. One major challenge is coping with dynamic electricity pricing, where charging costs vary throughout the day. Transit agencies must optimize charging assignments in response to such dynamism while accounting for secondary considerations such as seating constraints. This paper presents a comprehensive mixed-integer linear programming (MILP) model to address these challenges by jointly optimizing charging schedules and trip assignments for mixed (electric and diesel bus) fleets while considering factors such as dynamic electricity pricing, vehicle capacity, and route constraints. We address the potential computational intractability of the MILP formulation, which can arise even with relatively small fleets, by employing a hierarchical approach tailored to the fleet composition. By using real-world data from the city of Chattanooga, Tennessee, USA, we show that our approach can result in significant savings in the operating costs of the mixed transit fleets.

翻译：城市人口的快速增长和对可持续交通解决方案日益增长的需求，促使公共交通系统向电动公交车转型。然而，由电动和柴油公交车组成的混合车队的有效管理带来了重大的运营挑战。其中一个主要挑战是应对动态电价，即充电成本在一天内不断变化。公交机构必须针对这种动态性优化充电安排，同时兼顾座位限制等次要因素。本文提出了一个全面的混合整数线性规划模型，通过联合优化混合（电动和柴油公交车）车队的充电计划与班次分配，同时考虑动态电价、车辆容量和线路约束等因素，以应对这些挑战。我们针对该MILP模型在车队规模相对较小时也可能出现的潜在计算难题，采用了一种根据车队构成定制的分层方法。通过使用来自美国田纳西州查塔努加市的真实数据，我们表明，我们的方法可以为混合公交车队节省大量运营成本。