<?xml version="1.0"?>
<dblpperson name="YuZhu Ji" pid="419/3356" n="2">
<person key="homepages/419/3356" mdate="2025-11-07">
<author pid="419/3356">YuZhu Ji</author>
</person>
<r><inproceedings key="conf/icmcs/ChenZZJYP25" mdate="2025-11-07">
<author pid="17/8611">Zhengrong Chen</author>
<author pid="08/7078">Qinghua Zhu</author>
<author pid="14/1126">An Zeng</author>
<author pid="419/3356">YuZhu Ji</author>
<author pid="191/1061">Baoyao Yang</author>
<author pid="27/3045-1">Dan Pan 0001</author>
<title>Action Decomposition-based Actor-Critic for Supply Chain Optimization.</title>
<pages>1-6</pages>
<year>2025</year>
<booktitle>ICME</booktitle>
<ee>https://doi.org/10.1109/ICME59968.2025.11210182</ee>
<crossref>conf/icmcs/2025</crossref>
<url>db/conf/icmcs/icme2025.html#ChenZZJYP25</url>
<stream>streams/conf/icmcs</stream>
</inproceedings>
</r>
<r><inproceedings key="conf/smc/LiuZZJY25" mdate="2026-02-11">
<author pid="65/1163">Zhiqi Liu</author>
<author pid="08/7078">Qinghua Zhu</author>
<author pid="14/1126">An Zeng</author>
<author pid="419/3356">YuZhu Ji</author>
<author pid="191/1061">Baoyao Yang</author>
<title>Multi-Agent Reinforcement Learning Algorithm Using Dynamic OW-QMIX in Complex Supply Chain Scenarios.</title>
<booktitle>SMC</booktitle>
<year>2025</year>
<pages>1211-1218</pages>
<crossref>conf/smc/2025</crossref>
<ee>https://doi.org/10.1109/SMC58881.2025.11342630</ee>
<url>db/conf/smc/smc2025.html#LiuZZJY25</url>
<stream>streams/conf/smc</stream>
</inproceedings>
</r>
<coauthors n="6" nc="1">
<co c="0"><na f="c/Chen:Zhengrong" pid="17/8611">Zhengrong Chen</na></co>
<co c="0"><na f="l/Liu:Zhiqi" pid="65/1163">Zhiqi Liu</na></co>
<co c="0"><na f="p/Pan_0001:Dan" pid="27/3045-1">Dan Pan 0001</na></co>
<co c="0"><na f="y/Yang:Baoyao" pid="191/1061">Baoyao Yang</na></co>
<co c="0"><na f="z/Zeng:An" pid="14/1126">An Zeng</na></co>
<co c="0"><na f="z/Zhu:Qinghua" pid="08/7078">Qinghua Zhu</na></co>
</coauthors>
</dblpperson>

