Abstract
The bus transit system is promising to enable electric and autonomous vehicles for massive urban mobility, which relies on high-level automation and efficient resource management. Besides the on-road automation, the in-depot automated scheduling for battery recharging has not been adequately studied yet. This paper presents an integrated in-depot routing and recharging scheduling (IDRRS) problem, which is modeled as a constraint programming (CP) problem with Boolean satisfiability conditions (SAT). The model is converted to a flexible job-shop problem (FJSP) and is feasible to be solved by a CP-SAT solver for the optimal solution or feasible solutions with acceptable performance. This paper also presents a case study in Shanghai and compares the results from the FJSP model and the first-come first-serve (FCFS) method. The result demonstrates the allocation of routes and chargers under multiple scenarios with different numbers of chargers. The results show that the FJSP model shortens the delay and increases the time conservation for future rounds of operation than FCFS, while FCFS presents the simplicity of programming and better computational efficiency. The multiple random input test suggests that the proposed approach can decide the minimum number of chargers for stochastic charging requests. The proposed method can conserve the investment by increasing the utilization of automated recharging devices, improving vehicles’ in-depot efficiency.
Highlights
Developing high-efficient, safe, clean, and sustainable public transportation is always one of the essential issues for improving urban mobility in the era of severe urbanization across different continents [1,2,3]. e application of new technology brings public transit systems onto a new stage of electrification, automation, and rapidness. e U.S Department of Transportation has painted a bright roadmap to enable automation for future transportation [4], where the automation of transit bus systems plays an important role
The set of vehicles can be regarded as the set of jobs in flexible job-shop problem (FJSP), the set of paths and charging spaces can be regarded as the set of machines, and the steps of moving-recharging-moving can be regarded as the processing sequence, where moving can only occupy the paths and the recharging can only happen at charging spaces
We introduced the Google’s OR-Tools and applied their state-of-the-art constraint programming (CP)-satisfiability conditions (SAT) solver [39] to this model. e solution approach is based on a CP solver, and the CP solver is on top of a SAT solver
Summary
Teng et al modeled a multiobjective optimization problem of integrated timetabling and vehicle scheduling considering vehicle recharging [13]. One of their results is, which exports the service trips and charging requests. A subsequent problem is how to allocate chargers within the depot according to the required recharging plans. E article [14] proposed a recharging devices planning method from the energy consumption perspective, which could allocate and schedule charging devices utilizing a logic-based assignment approach. The in-depot routing and recharging scheduling (IDRRS) contributes to the complete automation for automated bus operation. E problem addressed in this paper is to decide the routing and recharging schedule for in-depot automation. (1) Assignment problem: to decide the route of each vehicle (for instance, whether the vehicle will go through path 1 or path 2 when moving from the parking area to the charging area and vice versa); and to decide which charging space to recharge for each vehicle (2) Scheduling problem: to schedule the occupancy for charging spaces and paths, which determines the time that each vehicle leaves the parking space, the time that each vehicle starts recharging, the time that each vehicle finishes recharging, and the time that each vehicle returns to the parking space
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