基于强化学习的多目标订单调度

IF 1.8 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Algorithms Pub Date : 2023-10-24 DOI:10.3390/a16110495
Sirui Chen, Yuming Tian, Lingling An
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引用次数: 0

摘要

订单调度在互联网和通信行业中具有重要意义。随着通信行业的快速发展和用户需求的日益多样化,通信运营商的工单数量呈指数级增长。大多数试图解决订单调度问题的研究都集中在改进基于实时性的分配规则上。然而,这些传统方法面临实时性差、人力资源消耗大、效率低等挑战。因此,为了获得一个鲁棒的订单调度策略以满足实际问题中订单调度的多重要求,解决多目标问题是至关重要的。优先级调度规则(PDR)是一种广泛应用于现实调度系统的启发式方法,本文提出了一种利用深度多目标强化学习智能体自动优化优先级调度规则(PDR)的方法,并利用凸包对加权向量进行优化,以获得最客观、最有效的权重。采用凸包法计算最大线性标化值,使我们能够客观地确定最优权向量,实现各目标的均衡优化,而不是依赖于基于个人经验的主观权重设置。在多个数据集上的实验结果表明,与现有的最先进的订单调度算法相比,我们提出的算法具有竞争力的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-Objective Order Scheduling via Reinforcement Learning
Order scheduling is of a great significance in the internet and communication industries. With the rapid development of the communication industry and the increasing variety of user demands, the number of work orders for communication operators has grown exponentially. Most of the research that tries to solve the order scheduling problem has focused on improving assignment rules based on real-time performance. However, these traditional methods face challenges such as poor real-time performance, high human resource consumption, and low efficiency. Therefore, it is crucial to solve multi-objective problems in order to obtain a robust order scheduling policy to meet the multiple requirements of order scheduling in real problems. The priority dispatching rule (PDR) is a heuristic method that is widely used in real-world scheduling systems In this paper, we propose an approach to automatically optimize the Priority Dispatching Rule (PDR) using a deep multiple-objective reinforcement learning agent and to optimize the weighted vector with a convex hull to obtain the most objective and efficient weights. The convex hull method is employed to calculate the maximal linearly scalarized value, enabling us to determine the optimal weight vector objectively and achieve a balanced optimization of each objective rather than relying on subjective weight settings based on personal experience. Experimental results on multiple datasets demonstrate that our proposed algorithm achieves competitive performance compared to existing state-of-the-art order scheduling algorithms.
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来源期刊
Algorithms
Algorithms Mathematics-Numerical Analysis
CiteScore
4.10
自引率
4.30%
发文量
394
审稿时长
11 weeks
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