启发式
拖延
可扩展性
计算机科学
人工神经网络
启发式
机器学习
进化算法
人工智能
数学优化
集合(抽象数据类型)
作业车间调度
数学
地铁列车时刻表
数据库
程序设计语言
操作系统
作者
Sungbum Jun,Seokcheon Lee
标识
DOI:10.1016/j.cie.2022.108282
摘要
• Pickup and delivery problem with time windows and heterogenous vehicles is addressed. • Proposed approach learns efficient heuristics based on evolutionary neural network. • Optimal or best feasible solutions were used as the training data for learning. • Results showed the learned heuristic outperformed other heuristics and ML models. • Proposed approach was demonstrated to be effective in learning scalable heuristics. In this paper, we address the pickup and delivery problem with time windows (PDP-TW) and heterogenous vehicles for minimisation of total tardiness by learning heuristics from a given set of solutions. In order to extract scalable heuristics from optimal or best feasible solutions, we propose a machine-learning (ML)-based approach called ENSIGHT (Evolutionary Neural network with Scalable Information for Generation of Heuristics for Transportation). ENSIGHT consists of three phases: solution generation, interpretation of solutions, and improvement of heuristics by an evolutionary neural network (ENN). First, a set of optimal or best feasible solutions for the training set of problem instances is acquired by using the proposed mathematical model. Second, as for the process interpreting those solutions, an approach for transforming them into training data by way of scalable input attributes as well as output discretisation is followed. Third, the ENN improves the learned heuristics by an evolutionary parameter optimisation process for minimization of total tardiness. To verify the performance of the proposed ENSIGHT, we conducted experiments and the results of which showed that it outperforms other ML techniques and the current dispatching rules (DRs). Moreover, the approach was demonstrated to be effective in learning scalable heuristics based on combined scalable inputs and discretisation as well as an evolutionary improvement process.
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