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

Le résumé fourni par la source

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

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