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2021
DOI: 10.1155/2021/5531063
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Enabling In-Depot Automated Routing and Recharging Scheduling for Automated Electric Bus Transit Systems

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 i… Show more

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Cited by 5 publications
(3 citation statements)
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References 35 publications
(47 reference statements)
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“…As the AFC system finds widespread application in passenger transport management of urban rail transit stations, it has become an important system in the rail transit system. It directly impacts public experience and interaction (Wang et al, 2021). Not only is it a trend in the development of the subway and transportation system, but it is also an important symbol of urban information construction (Wu et al, 2021).…”
Section: Afc System Overviewmentioning
confidence: 99%
“…As the AFC system finds widespread application in passenger transport management of urban rail transit stations, it has become an important system in the rail transit system. It directly impacts public experience and interaction (Wang et al, 2021). Not only is it a trend in the development of the subway and transportation system, but it is also an important symbol of urban information construction (Wu et al, 2021).…”
Section: Afc System Overviewmentioning
confidence: 99%
“…CP-SAT solver can be applied in many industries and for solving different scheduling problems. For instance, it was used for satellite scheduling problems [21], vehicle routing problems [22][23][24], shift scheduling in healthcare [25], hoist scheduling [26], recharging scheduling of electric buses [27] and many more.…”
Section: Constraint Satisfaction Problem Optimisation Using Or-toolsmentioning
confidence: 99%
“…Te agent can learn to do this over time through trial-and-error interactions. In recent years, reinforcement learning has been applied in the game of Go [11], highly automated driving [12][13][14], trafc signal control [15][16][17], and has proven its efectiveness. Meanwhile, multi-agent reinforcement learning is becoming a hot research area [18][19][20].…”
Section: Introductionmentioning
confidence: 99%