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arXiv · 2604.10664

Preference-Agile Multi-Objective Optimization for Real-time Vehicle Dispatching

Jiahuan Jin·Wenhao Zhao·Rong Qu·Jianfeng Ren·Xinan Chen·Qingfu Zhang·Ruibin Bai
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Abstract

Multi-objective optimization (MOO) has been widely studied in literature because of its versatility in human-centered decision making in real-life applications. Recently, demand for dynamic MOO is fast-emerging due to tough market dynamics that require real-time re-adjustments of priorities for different objectives. However, most existing studies focus either on deterministic MOO problems which are not practical, or non-sequential dynamic MOO decision problems that cannot deal with some real-life complexities. To address these challenges, a preference-agile multi-objective optimization (PAMOO) is proposed in this paper to permit users to dynamically adjust and interactively assign the preferences on the fly. To achieve this, a novel uniform model within a deep reinforcement learning (DRL) framework is proposed that can take as inputs users' dynamic preference vectors explicitly. Additionally, a calibration function is fitted to ensure high quality alignment between the preference vector inputs and the output DRL decision policy. Extensive experiments on challenging real-life vehicle dispatching problems at a container terminal showed that PAMOO obtains superior performance and generalization ability when compared with two most popular MOO methods. Our method presents the first dynamic MOO method for challenging \rev{dynamic sequential MOO decision problems

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Jiahuan Jin, Wenhao Zhao, Rong Qu, Jianfeng Ren, Xinan Chen, Qingfu Zhang, Ruibin Bai. 2026-04-12. Preference-Agile Multi-Objective Optimization for Real-time Vehicle Dispatching. https://arxiv.org/abs/2604.10664

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