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Jiepin Ding - IEEE Xplore Author Profile
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Jiepin Ding

Publications
4
Citations
14
Publications by Year
20212025
Co-Authors:
Show All Co-Authors (12)
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Jiepin Ding

Affiliation

MoE Engineering Research Center of Hardware/Software Co-design Technology and Application
East China Normal University
Shanghai, China

Publication Topics

Biography

Jiepin Ding received the MS degrees from the Department of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, China, in 2016 and 2020, respectively. She is currently working toward the PhD degree with the Software Engineering Institute, East China Normal University, Shanghai, China. Her research interests include production scheduling, heuristic algorithm, and reinforcement learning.(Based on document published on 31 October 2024).
Publications
4
Citations
14
Publications by Year
20212025
Co-Authors:
Show All Co-Authors (12)
Author's Published Works

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Multi-Objective Deep Reinforcement Learning for Function Offloading in Serverless Edge Computing

Yaning Yang;Xiao Du;Yutong Ye;Jiepin Ding;Ting Wang;Mingsong Chen;Keqin Li

IEEE Transactions on Services Computing
Year: 2025 | Volume: 18, Issue: 1 | Journal Article |
Function offloading problems play a crucial role in optimizing the performance of applications in serverless edge computing (SEC). Existing research has extensively explored function offloading strategies based on optimizing a single objective. However, a significant challenge arises when users expect to optimize multiple objectives according to the relative importance of these objectives. This ch...Show More

Multi-Objective Deep Reinforcement Learning for Function Offloading in Serverless Edge Computing

Yaning Yang;Xiao Du;Yutong Ye;Jiepin Ding;Ting Wang;Mingsong Chen;Keqin Li

Year: 2025 | Volume: 18, Issue: 1 | Journal Article |

Energy-Efficient Shop Scheduling Using Space-Cooperation Multi-Objective Optimization

Jiepin Ding;Jun Xia;Yaning Yang;Junlong Zhou;Mingsong Chen;Keqin Li

IEEE Transactions on Sustainable Computing
Year: 2024 | Early Access Article |
Since Industry 5.0 emphasizes that manufacturing enterprises should raise awareness of social contribution to achieve sustainable development, more and more meta-heuristic algorithms are investigated to save energy in manufacturing systems. Although non-dominated sorting-based meta-heuristics have been recognized as promising multi-objective optimization methods for solving the energy-efficient fl...Show More

Energy-Efficient Shop Scheduling Using Space-Cooperation Multi-Objective Optimization

Jiepin Ding;Jun Xia;Yaning Yang;Junlong Zhou;Mingsong Chen;Keqin Li

Year: 2024 | Early Access Article |

FairLight: Fairness-Aware Autonomous Traffic Signal Control With Hierarchical Action Space

Yutong Ye;Jiepin Ding;Ting Wang;Junlong Zhou;Xian Wei;Mingsong Chen

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Year: 2023 | Volume: 42, Issue: 8 | Journal Article |
Cited by: Papers (9)
Although reinforcement learning (RL) approaches are promising in autonomous traffic signal control (TSC), they often suffer from the unfairness problem that causes extremely long waiting time at intersections for partial vehicles. This is mainly because the traditional RL methods focus on optimizing the overall traffic performance, while the fairness of individual vehicles is neglected. To address...Show More

FairLight: Fairness-Aware Autonomous Traffic Signal Control With Hierarchical Action Space

Yutong Ye;Jiepin Ding;Ting Wang;Junlong Zhou;Xian Wei;Mingsong Chen

Year: 2023 | Volume: 42, Issue: 8 | Journal Article |

Enumeration and Deduction Driven Co-Synthesis of CCSL Specifications using Reinforcement Learning

Ming Hu;Jiepin Ding;Min Zhang;Frédéric Mallet;Mingsong Chen

2021 IEEE Real-Time Systems Symposium (RTSS)
Year: 2021 | Conference Paper |
Cited by: Papers (5)
The Clock Constraint Specification Language (CCSL) has become popular for modeling and analyzing timing behaviors of real-time embedded systems. However, it is difficult for requirement engineers to accurately figure out CCSL specifications from natural language-based requirement descriptions. This is mainly because: i) most requirement engineers lack expertise in formal modeling; and ii) few exis...Show More

Enumeration and Deduction Driven Co-Synthesis of CCSL Specifications using Reinforcement Learning

Ming Hu;Jiepin Ding;Min Zhang;Frédéric Mallet;Mingsong Chen

Year: 2021 | Conference Paper |

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