Chapter Scheduling Optimization of Electric Ready Mixed Concrete Vehicles Using an Improved Model-Based Reinforcement Learning
dc.contributor.author | Chen, Zhengyi | |
dc.contributor.author | Zhang, Xiao | |
dc.contributor.author | Song, Changhao | |
dc.contributor.author | Cheng, Jack C. P. | |
dc.date.accessioned | 2024-04-02T15:45:10Z | |
dc.date.available | 2024-04-02T15:45:10Z | |
dc.date.issued | 2023 | |
dc.identifier | ONIX_20240402_9791221502893_27 | |
dc.identifier.issn | 2704-5846 | |
dc.identifier.uri | https://library.oapen.org/handle/20.500.12657/89058 | |
dc.description.abstract | Decarbonizing 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.language | English | |
dc.relation.ispartofseries | Proceedings e report | |
dc.subject.classification | thema EDItEUR::U Computing and Information Technology | |
dc.subject.other | Electric vehicle | |
dc.subject.other | Ready-mixed concrete delivery | |
dc.subject.other | Scheduling optimization | |
dc.subject.other | Model-based reinforcement learning | |
dc.subject.other | Monte Carlo Tree Search | |
dc.title | Chapter Scheduling Optimization of Electric Ready Mixed Concrete Vehicles Using an Improved Model-Based Reinforcement Learning | |
dc.type | chapter | |
oapen.identifier.doi | 10.36253/979-12-215-0289-3.74 | |
oapen.relation.isPublishedBy | bf65d21a-78e5-4ba2-983a-dbfa90962870 | |
oapen.relation.isbn | 9791221502893 | |
oapen.series.number | 137 | |
oapen.pages | 12 | |
oapen.place.publication | Florence |