Deep Reinforcement Learning for Minimizing Tardiness in Parallel Machine Scheduling With Sequence Dependent Family Setups
Parallel machine scheduling with sequence-dependent family setups has attracted much attention from academia and industry due to its practical applications. In a real-world manufacturing system, however, solving the scheduling problem becomes challenging since it is required to address urgent and fr...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9486959/ |
| _version_ | 1818655629029408768 |
|---|---|
| author | Bohyung Paeng In-Beom Park Jonghun Park |
| author_facet | Bohyung Paeng In-Beom Park Jonghun Park |
| author_sort | Bohyung Paeng |
| collection | DOAJ |
| description | Parallel machine scheduling with sequence-dependent family setups has attracted much attention from academia and industry due to its practical applications. In a real-world manufacturing system, however, solving the scheduling problem becomes challenging since it is required to address urgent and frequent changes in demand and due-dates of products. To minimize the total tardiness of the scheduling problem, we propose a deep reinforcement learning (RL) based scheduling framework in which trained neural networks (NNs) are able to solve unseen scheduling problems without re-training even when such changes occur. Specifically, we propose state and action representations whose dimensions are independent of production requirements and due-dates of jobs while accommodating family setups. At the same time, an NN architecture with parameter sharing was utilized to improve the training efficiency. Extensive experiments demonstrate that the proposed method outperforms the recent metaheuristics, rule-based, and other RL-based methods in terms of total tardiness. Moreover, the computation time for obtaining a schedule by our framework is shorter than those of the metaheuristics and other RL-based methods. |
| first_indexed | 2024-12-17T03:12:43Z |
| format | Article |
| id | doaj.art-5af43b17cadd4729b3aa6b41d4132ab4 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T03:12:43Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-5af43b17cadd4729b3aa6b41d4132ab42022-12-21T22:05:47ZengIEEEIEEE Access2169-35362021-01-01910139010140110.1109/ACCESS.2021.30972549486959Deep Reinforcement Learning for Minimizing Tardiness in Parallel Machine Scheduling With Sequence Dependent Family SetupsBohyung Paeng0https://orcid.org/0000-0002-9414-4907In-Beom Park1https://orcid.org/0000-0002-8890-7381Jonghun Park2https://orcid.org/0000-0001-7505-110XDepartment of Industrial Engineering, Seoul National University, Seoul, South KoreaDepartment of Industrial Engineering, Sungkyunkwan University, Suwon, South KoreaDepartment of Industrial Engineering, Seoul National University, Seoul, South KoreaParallel machine scheduling with sequence-dependent family setups has attracted much attention from academia and industry due to its practical applications. In a real-world manufacturing system, however, solving the scheduling problem becomes challenging since it is required to address urgent and frequent changes in demand and due-dates of products. To minimize the total tardiness of the scheduling problem, we propose a deep reinforcement learning (RL) based scheduling framework in which trained neural networks (NNs) are able to solve unseen scheduling problems without re-training even when such changes occur. Specifically, we propose state and action representations whose dimensions are independent of production requirements and due-dates of jobs while accommodating family setups. At the same time, an NN architecture with parameter sharing was utilized to improve the training efficiency. Extensive experiments demonstrate that the proposed method outperforms the recent metaheuristics, rule-based, and other RL-based methods in terms of total tardiness. Moreover, the computation time for obtaining a schedule by our framework is shorter than those of the metaheuristics and other RL-based methods.https://ieeexplore.ieee.org/document/9486959/Deep reinforcement learningunrelated parallel machine schedulingsequence-dependent family setupstotal tardiness objectivedeep <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">Q</italic>-network |
| spellingShingle | Bohyung Paeng In-Beom Park Jonghun Park Deep Reinforcement Learning for Minimizing Tardiness in Parallel Machine Scheduling With Sequence Dependent Family Setups IEEE Access Deep reinforcement learning unrelated parallel machine scheduling sequence-dependent family setups total tardiness objective deep <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">Q</italic>-network |
| title | Deep Reinforcement Learning for Minimizing Tardiness in Parallel Machine Scheduling With Sequence Dependent Family Setups |
| title_full | Deep Reinforcement Learning for Minimizing Tardiness in Parallel Machine Scheduling With Sequence Dependent Family Setups |
| title_fullStr | Deep Reinforcement Learning for Minimizing Tardiness in Parallel Machine Scheduling With Sequence Dependent Family Setups |
| title_full_unstemmed | Deep Reinforcement Learning for Minimizing Tardiness in Parallel Machine Scheduling With Sequence Dependent Family Setups |
| title_short | Deep Reinforcement Learning for Minimizing Tardiness in Parallel Machine Scheduling With Sequence Dependent Family Setups |
| title_sort | deep reinforcement learning for minimizing tardiness in parallel machine scheduling with sequence dependent family setups |
| topic | Deep reinforcement learning unrelated parallel machine scheduling sequence-dependent family setups total tardiness objective deep <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">Q</italic>-network |
| url | https://ieeexplore.ieee.org/document/9486959/ |
| work_keys_str_mv | AT bohyungpaeng deepreinforcementlearningforminimizingtardinessinparallelmachineschedulingwithsequencedependentfamilysetups AT inbeompark deepreinforcementlearningforminimizingtardinessinparallelmachineschedulingwithsequencedependentfamilysetups AT jonghunpark deepreinforcementlearningforminimizingtardinessinparallelmachineschedulingwithsequencedependentfamilysetups |