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dc.contributor.authorChen, Zhengyi
dc.contributor.authorZhang, Xiao
dc.contributor.authorSong, Changhao
dc.contributor.authorCheng, Jack C. P.
dc.date.accessioned2024-04-02T15:45:10Z
dc.date.available2024-04-02T15:45:10Z
dc.date.issued2023
dc.identifierONIX_20240402_9791221502893_27
dc.identifier.issn2704-5846
dc.identifier.urihttps://library.oapen.org/handle/20.500.12657/89058
dc.description.abstractDecarbonizing the construction sector has become an imperative global agenda, with electric machinery playing a pivotal role in realizing this objective. This research concentrates on devising an operational scheduling optimization method for electric ready-mixed concrete vehicles (ERVs) – a groundbreaking, eco-friendly intervention for the construction sector. We commence by outlining a systematic problem definition for the ERV operational process, considering the distinctive characteristics of electric vehicles and ready-mixed concrete (RMC) delivery tasks. The entire process is then conceptualized as a Markov decision problem (MDP), which enables sequential decision-making. We subsequently develop an enhanced model-based reinforcement learning technique, named parallel-masked-decaying Monte Carlo Tree Search (PMD-MCTS), for efficient resolution of the MDP. The entire system is authenticated via a real-world case study, and the PMD-MCTS's performance is juxtaposed against existing benchmarks. The results demonstrate the appropriateness of the proposed MDP formulation for tackling RMC delivery tasks. The PMD-MCTS algorithm and one of its ablation algorithms (PM-MCTS) have demonstrated superior performance compared to other benchmarks in either cost reduction or delay minimization, with PMD-MCTS requiring 30% less computation time than PM-MCTS
dc.languageEnglish
dc.relation.ispartofseriesProceedings e report
dc.subject.classificationthema EDItEUR::U Computing and Information Technology
dc.subject.otherElectric vehicle
dc.subject.otherReady-mixed concrete delivery
dc.subject.otherScheduling optimization
dc.subject.otherModel-based reinforcement learning
dc.subject.otherMonte Carlo Tree Search
dc.titleChapter Scheduling Optimization of Electric Ready Mixed Concrete Vehicles Using an Improved Model-Based Reinforcement Learning
dc.typechapter
oapen.identifier.doi10.36253/979-12-215-0289-3.74
oapen.relation.isPublishedBybf65d21a-78e5-4ba2-983a-dbfa90962870
oapen.relation.isbn9791221502893
oapen.series.number137
oapen.pages12
oapen.place.publicationFlorence


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