Chapter Scheduling Optimization of Electric Ready Mixed Concrete Vehicles Using an Improved Model-Based Reinforcement Learning
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 groundb...
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| Materiálatiipa: | Online |
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Firenze University Press
2024
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| Liŋkkat: | ONIX_20240402_9791221502893_27 |
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| _version_ | 1869521617600643072 |
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| author | Chen, Zhengyi Zhang, Xiao Song, Changhao Cheng, Jack C. P. |
| author_browse | Chen, Zhengyi Cheng, Jack C. P. Song, Changhao Zhang, Xiao |
| author_facet | Chen, Zhengyi Zhang, Xiao Song, Changhao Cheng, Jack C. P. |
| author_sort | Chen, Zhengyi |
| collection | Directory of Open Access Books |
| description | 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 |
| format | Online |
| id | doab-20.500.12854ir-137233 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Firenze University Press |
| publisherStr | Firenze University Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1372332024-05-13T03:05:29Z Chapter Scheduling Optimization of Electric Ready Mixed Concrete Vehicles Using an Improved Model-Based Reinforcement Learning Chen, Zhengyi Zhang, Xiao Song, Changhao Cheng, Jack C. P. Electric vehicle Ready-mixed concrete delivery Scheduling optimization Model-based reinforcement learning Monte Carlo Tree Search thema EDItEUR::U Computing and Information Technology 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 2024-05-13T03:05:26Z 2024-05-13T03:05:26Z 2024-04-02T15:45:10Z 2023 chapter ONIX_20240402_9791221502893_27 2704-5846 https://library.oapen.org/handle/20.500.12657/89058 9791221502893 https://directory.doabooks.org/handle/20.500.12854/137233 eng Proceedings e report open access image/jpeg n/a https://library.oapen.org/bitstream/20.500.12657/89058/1/9791221502893_74.pdf Firenze University Press 10.36253/979-12-215-0289-3.74 10.36253/979-12-215-0289-3.74 2ec4474d-93b1-4cfa-b313-9c6019b51b1a 9791221502893 12 Florence open access |
| spellingShingle | Electric vehicle Ready-mixed concrete delivery Scheduling optimization Model-based reinforcement learning Monte Carlo Tree Search thema EDItEUR::U Computing and Information Technology Chen, Zhengyi Zhang, Xiao Song, Changhao Cheng, Jack C. P. Chapter Scheduling Optimization of Electric Ready Mixed Concrete Vehicles Using an Improved Model-Based Reinforcement Learning |
| title | Chapter Scheduling Optimization of Electric Ready Mixed Concrete Vehicles Using an Improved Model-Based Reinforcement Learning |
| title_full | Chapter Scheduling Optimization of Electric Ready Mixed Concrete Vehicles Using an Improved Model-Based Reinforcement Learning |
| title_fullStr | Chapter Scheduling Optimization of Electric Ready Mixed Concrete Vehicles Using an Improved Model-Based Reinforcement Learning |
| title_full_unstemmed | Chapter Scheduling Optimization of Electric Ready Mixed Concrete Vehicles Using an Improved Model-Based Reinforcement Learning |
| title_short | Chapter Scheduling Optimization of Electric Ready Mixed Concrete Vehicles Using an Improved Model-Based Reinforcement Learning |
| title_sort | chapter scheduling optimization of electric ready mixed concrete vehicles using an improved model based reinforcement learning |
| topic | Electric vehicle Ready-mixed concrete delivery Scheduling optimization Model-based reinforcement learning Monte Carlo Tree Search thema EDItEUR::U Computing and Information Technology |
| topic_facet | Electric vehicle Ready-mixed concrete delivery Scheduling optimization Model-based reinforcement learning Monte Carlo Tree Search thema EDItEUR::U Computing and Information Technology |
| url | ONIX_20240402_9791221502893_27 |
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