LLM-Assisted Automatic Memetic Algorithm for Lot-Streaming Hybrid Job Shop Scheduling With Variable Sublots
This study addresses the lot-streaming hybrid job shop scheduling problem with variable sublots (LHJSV), inspired by a real-world aircraft tooling shop. A computational model is developed to represent the complex scheduling processes of the tooling shop. To solve this problem, we propose an automati...
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| Vydané v: | IEEE transactions on evolutionary computation s. 1 |
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| Abstract | This study addresses the lot-streaming hybrid job shop scheduling problem with variable sublots (LHJSV), inspired by a real-world aircraft tooling shop. A computational model is developed to represent the complex scheduling processes of the tooling shop. To solve this problem, we propose an automatic memetic algorithm enhanced by a heuristic designed with the assistance of a large language model (LLM). The approach is designed as follows: first, a memetic computing framework with automated algorithmic design is proposed for LHJSV. Second, a cooperative evolutionary heuristic framework based on problem decomposition is introduced, enabling the LLM to comprehend the LHJSV characteristics and generate feasible algorithms. Third, problem-specific prompts for LHJSV are carefully designed to guide the LLM. To evaluate the effectiveness of the proposed method, 20 benchmark instances derived from the Taillard dataset and a real-world case involving 575 operations are utilized. The proposed algorithm is compared against three swarm-based algorithms, an end-to-end method, and an LLM-based algorithm. Experimental results demonstrate that our method outperforms the compared algorithms on 85% of benchmark instances and exhibits significant superiority in real-world scenarios. |
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| AbstractList | This study addresses the lot-streaming hybrid job shop scheduling problem with variable sublots (LHJSV), inspired by a real-world aircraft tooling shop. A computational model is developed to represent the complex scheduling processes of the tooling shop. To solve this problem, we propose an automatic memetic algorithm enhanced by a heuristic designed with the assistance of a large language model (LLM). The approach is designed as follows: first, a memetic computing framework with automated algorithmic design is proposed for LHJSV. Second, a cooperative evolutionary heuristic framework based on problem decomposition is introduced, enabling the LLM to comprehend the LHJSV characteristics and generate feasible algorithms. Third, problem-specific prompts for LHJSV are carefully designed to guide the LLM. To evaluate the effectiveness of the proposed method, 20 benchmark instances derived from the Taillard dataset and a real-world case involving 575 operations are utilized. The proposed algorithm is compared against three swarm-based algorithms, an end-to-end method, and an LLM-based algorithm. Experimental results demonstrate that our method outperforms the compared algorithms on 85% of benchmark instances and exhibits significant superiority in real-world scenarios. |
| Author | Yao, Lizhong Sang, Hongyan Pan, Lijun Li, Rui Wang, Ling |
| Author_xml | – sequence: 1 givenname: Rui orcidid: 0000-0001-5335-9453 surname: Li fullname: Li, Rui email: li-r23@mails.tsinghua.edu.cn organization: Department of Automation, Tsinghua University, Beijing, China – sequence: 2 givenname: Ling orcidid: 0000-0003-1226-2801 surname: Wang fullname: Wang, Ling email: wangling@mail.tsinghua.edu.cn organization: Department of Automation, Tsinghua University, Beijing, China – sequence: 3 givenname: Hongyan orcidid: 0000-0001-7476-5039 surname: Sang fullname: Sang, Hongyan – sequence: 4 givenname: Lizhong orcidid: 0000-0001-5765-9349 surname: Yao fullname: Yao, Lizhong email: lizhong yao@cqnu.edu.cn organization: College of Physics and Electronic Engineering, Chongqing Normal University, Chongqing, China – sequence: 5 givenname: Lijun surname: Pan fullname: Pan, Lijun email: pansoftware@126.com organization: School of Management, HUNAN Institute of Engineering, XiangTan, China |
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| Snippet | This study addresses the lot-streaming hybrid job shop scheduling problem with variable sublots (LHJSV), inspired by a real-world aircraft tooling shop. A... |
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| SubjectTerms | Aircraft Aircraft manufacture Automatic algorithm design Complexity theory Computational modeling Evolutionary Computation Heuristic algorithms Job shop scheduling Large language model Lot-streaming scheduling Memetic Computing Memetics Parallel machines Processor scheduling Scheduling |
| Title | LLM-Assisted Automatic Memetic Algorithm for Lot-Streaming Hybrid Job Shop Scheduling With Variable Sublots |
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