Reforming mixed operation schedule for electric buses and traditional fuel buses by an optimal framework

Abstract

Bus scheduling plays a significant role in public transportation and supports the sustainable development of transportation systems. Challenges are beginning to appear with the newly emerging electric buses (EBs), as scheduling changes due to fleet composition make traditional fixed timetables no longer able to satisfy operational needs. Moreover, the fixed-trip time hypothesis has been inappropriate for large cities due to the variety of urban traffic statuses. This paper proposes an optimal framework for reforming the mixed operation schedule for electric buses and traditional fuel buses under stochastic trip times. Based on the primary grouping genetic algorithm (GGA), a straightforward framework with a Monte Carlo simulation is presented to optimize the scheduling scheme. Case studies based on the operating environment and service trips of real bus lines in Beijing are conducted to verify the effectiveness of the proposed model by considering both the composition of fleet types and time stochasticity. Additionally, the impacts of stochasticity, fleet composition, government subsidies and cost factors on operational costs are investigated. Considering stochastic trip times, the achieved scheduling strategies can provide the optimal proportion of electric and traditional fuel buses and make a crucial impact on operational costs. INTRODUCTIONRecently, considering climate change and health impacts, air quality has attracted more attention worldwide [1]. Therefore, reducing the use of fossil fuel is a commonly consentaneous measure [2,3]. In public transportation, most conventional bus types (such as heavy fuel diesel buses) make significant contributions to air pollution and greenhouse gases due to high daily mileages [4,5]. The introduction of electrified transportation solutions is part of a wide range of policy options worldwide [6,7]. Compared with traditional fuel buses (CBs), electric buses (EBs) have considerable inherent advantages such as zero exhaust pipe emissions, lower energy costs, high comfortability, and low noise emissions [8,9]. Therefore, the transformation of the public transport fleet from conventional fuel buses to electric or alternative energy buses can significantly reduce exhaust pipe emissions and improve air quality. At present, EBs have been introduced into the market and operated in many countries (e.g. China, Norway, Sweden, and Germany). However, high ownership costs and limitations on driving range and charging speed make it hard for EBs to com-This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

A Simulated Annealing Approach to the Scheduling of Battery-Electric Bus Charging

“…Strategies vary in terms of their basic formulation, many using variations of the vehicle scheduling problem [8][9][10][11][12][13] while [14] utilizes a network flow approach and [15] utilizes a rectangle packing approach referred to as the Position Allocation Problem (PAP). Some works assume that BEBs always charge to full capacity each time they connect to a charger [8,9,16,17], while others allow for partial charging using a linear battery charge model [13,18,19], and others use a higher-fidelity, non-linear battery model [8,14,[20][21][22]. Some works consider only fast chargers during planing [12,18,[23][24][25], while others assume that different types of chargers can exist in different locations [11,13].…”

“…When considering the utility rate schedules, two key elements must be considered: the consumption cost and the demand cost. Most approaches consider a consumption cost [9,10,16,[19][20][21][22][23]. This is akin to the combustion engine fuel cost.…”

Collaborative Optimization of Vehicle and Crew Scheduling for a Mixed Fleet with Electric and Conventional Buses

“…Rinaldi et al (2020) developed a mixed-integer linear program model to address the problem of vehicle scheduling with a mixed fleet of electric and hybrid/nonelectric buses and used an ad hoc decomposition scheme to enhance the scalability of the proposed model [4]. Duan et al (2021) proposed an optimal framework for reforming the mixed operation schedule for electric buses and traditional fuel buses under stochastic trip times [19].…”