@inproceedings{2577a9b1d46247f38426db11b2d37992,
title = "Reinforcement Learning Based Iterated Greedy for Parallel Machine Scheduling with Weighted Earliness Tardiness",
abstract = "On-time delivery is becoming more and more important for industrial companies, as their customers often require orders to meet specific deadlines. In this paper, we investigate the parallel machine scheduling problem with sequence-dependent setup times and due date windows. This problem has attracted attention due to its significance and relevance in real-world applications. To minimize total weighted earliness tardiness, we propose an efficient adaptive iterated greedy method enhanced with Q-learning (QIG). The performance of our approach is compared against six well selected metaheuristics from the literature. Computational experiments on a benchmark set of 600 instances demonstrate the efficiency of the proposed iterated greedy method.",
keywords = "Iterated Greedy, Parallel Machine, Q-Learning, Setup times, Total Earliness Tardiness",
author = "Ahmed Missaoui and Barry O{\textquoteright}Sullivan",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.; 38th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2025 ; Conference date: 01-07-2025 Through 04-07-2025",
year = "2026",
doi = "10.1007/978-981-96-8889-0\_2",
language = "English",
isbn = "9789819688883",
series = "Lecture Notes in Computer Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "15--26",
editor = "Hamido Fujita and Yutaka Watanobe and Moonis Ali and Yinglin Wang",
booktitle = "Advances and Trends in Artificial Intelligence. Theory and Applications - 38th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2025, Proceedings",
address = "Germany",
}