Dynamic Dispatching for Large-Scale Heterogeneous Fleet via Multi-agent Deep Reinforcement Learning

Chi Zhang, Philip Odonkor, Shuai Zheng, Hamed Khorasgani, Susumu Serita, Chetan Gupta, Haiyan Wang

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    21 Scopus citations

    Abstract

    Dynamic dispatching is one of the core problems for operation optimization in traditional industries such as mining, as it is about how to smartly allocate the right resources to the right place at the right time. Conventionally, the industry relies on heuristics or even human intuitions which are often short-sighted and sub-optimal solutions. Leveraging the power of AI and Internet of Things (IoT), data-driven automation is reshaping this area. However, facing its own challenges such as large-scale and heterogenous trucks running in a highly dynamic environment, it can barely adopt methods developed in other domains (e.g., ride-sharing). In this paper, we propose a novel Deep Reinforcement Learning approach to solve the dynamic dispatching problem in mining. We first develop an event-based mining simulator with parameters calibrated in real mines. Then we propose an experience-sharing Deep Q Network with a novel abstract state/action representation to learn memories from heterogeneous agents altogether and realizes learning in a centralized way. We demonstrate that the proposed methods significantly outperform the most widely adopted approaches in the industry by 5.56% in terms of productivity. The proposed approach has great potential in a broader range of industries (e.g., manufacturing, logistics) which have a large-scale of heterogenous equipment working in a highly dynamic environment, as a general framework for dynamic resource allocation.

    Original languageEnglish
    Title of host publicationProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
    EditorsXintao Wu, Chris Jermaine, Li Xiong, Xiaohua Tony Hu, Olivera Kotevska, Siyuan Lu, Weijia Xu, Srinivas Aluru, Chengxiang Zhai, Eyhab Al-Masri, Zhiyuan Chen, Jeff Saltz
    Pages1436-1441
    Number of pages6
    ISBN (Electronic)9781728162515
    DOIs
    StatePublished - 10 Dec 2020
    Event8th IEEE International Conference on Big Data, Big Data 2020 - Virtual, Atlanta, United States
    Duration: 10 Dec 202013 Dec 2020

    Publication series

    NameProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020

    Conference

    Conference8th IEEE International Conference on Big Data, Big Data 2020
    Country/TerritoryUnited States
    CityVirtual, Atlanta
    Period10/12/2013/12/20

    Keywords

    • Dispatching
    • Mining
    • Reinforcement Learning

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