Chapter Scheduling Optimization of Electric Ready Mixed Concrete Vehicles Using an Improved Model-Based Reinforcement Learning

Mitwirkende(r):

Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Reihen: Proceedings e reportVerlag: Florence : Firenze University Press, 2023Beschreibung: 1 Online-Ressource (1 electronic resource 12 p.)ISBN:

9791221502893

Schlagwörter:

Online-Ressourcen:

Volltext frei verfügbar

Zusammenfassung: 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-MCTSPPN: PPN: 1983954381Package identifier: Produktsigel: ZDB-94-OAB

Exemplare ( 0 )
Titelinformationen ( 5 )

Dieser Titel hat keine Exemplare

Open Access. Unrestricted online access star

Namensnennung - Nicht-kommerziell 4.0 International CC BY-NC 4.0 cc:

https://creativecommons.org/licenses/by-nc/4.0/

Anmerkungen zur Barrierefreiheit: Accessibility options of PDF file not available.

eng