Intelligent job shop scheduling via deep reinforcement learning over graphs
Job Shop Scheduling Problem (JSSP) is a well-known NP-hard combinatorial optimization problem (COP) with extensive applications in today’s manufacturing system. Due to its NP-hardness, approximation, heuristic, and meta-heuristic algorithms have been proposed in the past. These methods have some li...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis-Doctor of Philosophy |
| Language: | English |
| Published: |
Nanyang Technological University
2023
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/164926 |
| _version_ | 1826120674537635840 |
|---|---|
| author | Zhang, Cong |
| author2 | - |
| author_facet | - Zhang, Cong |
| author_sort | Zhang, Cong |
| collection | NTU |
| description | Job Shop Scheduling Problem (JSSP) is a well-known NP-hard combinatorial optimization problem (COP) with extensive applications in today’s manufacturing
system. Due to its NP-hardness, approximation, heuristic, and meta-heuristic algorithms have been proposed in the past. These methods have some limitations,
among which two are well recognized. One is high computation overhead due to
the nature of computational inefficiency of the methods and the curse of dimensionality (the problem sizes). The other is that existing methods strongly depend
on human expert experience for algorithm design, which is less automatic. In addition, the manually designed components are also highly dependent on human
expertise, lacking a substantial level of exploration. |
| first_indexed | 2024-10-01T05:20:19Z |
| format | Thesis-Doctor of Philosophy |
| id | ntu-10356/164926 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:20:19Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1649262023-04-04T02:58:00Z Intelligent job shop scheduling via deep reinforcement learning over graphs Zhang, Cong - School of Computer Science and Engineering Zhang Jie ZhangJ@ntu.edu.sg Engineering::Computer science and engineering Job Shop Scheduling Problem (JSSP) is a well-known NP-hard combinatorial optimization problem (COP) with extensive applications in today’s manufacturing system. Due to its NP-hardness, approximation, heuristic, and meta-heuristic algorithms have been proposed in the past. These methods have some limitations, among which two are well recognized. One is high computation overhead due to the nature of computational inefficiency of the methods and the curse of dimensionality (the problem sizes). The other is that existing methods strongly depend on human expert experience for algorithm design, which is less automatic. In addition, the manually designed components are also highly dependent on human expertise, lacking a substantial level of exploration. Doctor of Philosophy 2023-03-02T01:21:41Z 2023-03-02T01:21:41Z 2023 Thesis-Doctor of Philosophy Zhang, C. (2023). Intelligent job shop scheduling via deep reinforcement learning over graphs. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/164926 https://hdl.handle.net/10356/164926 10.32657/10356/164926 en This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering Zhang, Cong Intelligent job shop scheduling via deep reinforcement learning over graphs |
| title | Intelligent job shop scheduling via deep reinforcement learning over graphs |
| title_full | Intelligent job shop scheduling via deep reinforcement learning over graphs |
| title_fullStr | Intelligent job shop scheduling via deep reinforcement learning over graphs |
| title_full_unstemmed | Intelligent job shop scheduling via deep reinforcement learning over graphs |
| title_short | Intelligent job shop scheduling via deep reinforcement learning over graphs |
| title_sort | intelligent job shop scheduling via deep reinforcement learning over graphs |
| topic | Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/164926 |
| work_keys_str_mv | AT zhangcong intelligentjobshopschedulingviadeepreinforcementlearningovergraphs |