Integrated optimization of electric bus scheduling and charging planning incorporating flexible charging and timetable shifting strategies

Duan, Mengyuan; Liao, Feixiong; Qi, Geqi; Guan, Wei

doi:10.1016/j.trc.2023.104175

Cited by 18 publications

(2 citation statements)

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“…To achieve the objective, a Lagrangian relaxation method was proposed to find the constrained shortest path [13], which is a weighted edge problem in order to ascertain the minimum cost-feasible path for a vehicle under operation. A planning methodology to incorporate charging, battery degradation, and a possible shift in the timetable of the buses was discussed in [14]. The paper used a column generation method to minimize the fleet cost.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The same is satisfied in (13) when a bus is taking a T i trip after R n . Similar to time, a variable for SOC (s lj ) is defined, which represents the remaining distance in kilometres of a bus at the end of connection l. In (14), if a bus is taking a trip from D n or R n , then s lj will be the maximum SOC (s) minus the kilometres travelled to complete a trip (s tr l ). For the rest of the trips, the remaining SOC will be equal to the SOC at the last completed trip minus the kilometres travelled to complete a trip (s tr l ), shown in (15).…”

Section: Public Transport Optimization Frameworkmentioning

confidence: 99%

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Optimizing the Scheduling of Electrified Public Transport System in Malta

et al. 2023

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In this paper, we describe a comparative analysis of a bus route scheduling problem as part of timetable trips. We consider the current uptake of electric buses as a viable public transportation option that will eventually phase out the diesel-engine-based buses. We note that, with the increasing number of electric buses, the complexity related to the scheduling also increases, especially stemming from the charging requirement and the dedicated infrastructure behind it. The aim of our comparative study is to highlight the brevity with which a multi-agent-system-based scheduling method can be helpful as compared to the classical mixed-integer linear-programming-based approach. The multi-agent approach we design is centralized with asymmetric communication between the master agent, the bus agent, and the depot agent, which makes it possible to solve the multi-depot scheduling problem in almost real time as opposed to the classical optimizer, which sees a multi-depot problem as a combinatorial heuristic NP-hard problem, which, for large system cases, can be computationally inefficient to solve. We test the efficacy of the multi-agent algorithm and also compare the same with the MILP objective designed in harmony with the multi-agent system. We test the comparisons first on a small network and then extend the scheduling application to real data extracted from the public transport of the Maltese Islands.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Public Transport Optimization Frameworkmentioning

confidence: 99%