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Original Article
Xinyu LI
Huazhong University of Science and Technology
Corresponding Author
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The connection between production scheduling and transportation scheduling is getting closer in smart manufacturing system, both of which are summarized as NP-hard problems. However, only a few studies have considered them simultaneously. This paper studies the integrated production and transportation scheduling problem (IPTSP) in hybrid flow shop, which is an extension of the hybrid flow shop scheduling problem (HFSP). In this problem, the transfer tasks of jobs are performed by a certain number of automated guided vehicles (AGV). In addition to the production scheduling on machines, we consider the transportation scheduling on AGVs as the part of the optimization process. To solve it, we make some preparation (including the establishment of task pool, the new solution representation and the new solution evaluation), which can help algorithm efficiently find satisfactory solutions while appropriately limiting the search space. Then, an effective genetic tabu search algorithm is used to minimize the makespan. Finally, two groups of instances are designed and three types of experiments are conducted to evaluate the performance of proposed method. The results show that the proposed method can achieve good results, showing the effectiveness of the presented approach.
Keywords
Hybrid flow shop, Integrated scheduling, Task pool, Hybrid algorithm
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CURRENT STATUS: Under Review
Version 1
Posted 06 Mar, 2021
No community comments so far
Review #1 received
Received 07 Apr, 2021
Reviewer #2 agreed
On 14 Mar, 2021
Reviewers invited
Invitations sent on 13 Mar, 2021
Reviewer #1 agreed
On 13 Mar, 2021
Review #? received
Received 13 Mar, 2021
Editor assigned
On 21 Feb, 2021
Submission checks complete
On 21 Feb, 2021
Editor invited
On 21 Feb, 2021
First submitted
On 21 Feb, 2021
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Subject Areas
Mechanical Engineering
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This preprint is under consideration at Chinese Journal of Mechanical Engineering. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Original Article
Xinyu LI
Huazhong University of Science and Technology
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 06 Mar, 2021
No community comments so far
Review #1 received
Received 07 Apr, 2021
Reviewer #2 agreed
On 14 Mar, 2021
Reviewers invited
Invitations sent on 13 Mar, 2021
Reviewer #1 agreed
On 13 Mar, 2021
Review #? received
Received 13 Mar, 2021
Editor assigned
On 21 Feb, 2021
Submission checks complete
On 21 Feb, 2021
Editor invited
On 21 Feb, 2021
First submitted
On 21 Feb, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Mechanical Engineering
License:
This work is licensed under a CC BY 4.0 License. Read Full License
The connection between production scheduling and transportation scheduling is getting closer in smart manufacturing system, both of which are summarized as NP-hard problems. However, only a few studies have considered them simultaneously. This paper studies the integrated production and transportation scheduling problem (IPTSP) in hybrid flow shop, which is an extension of the hybrid flow shop scheduling problem (HFSP). In this problem, the transfer tasks of jobs are performed by a certain number of automated guided vehicles (AGV). In addition to the production scheduling on machines, we consider the transportation scheduling on AGVs as the part of the optimization process. To solve it, we make some preparation (including the establishment of task pool, the new solution representation and the new solution evaluation), which can help algorithm efficiently find satisfactory solutions while appropriately limiting the search space. Then, an effective genetic tabu search algorithm is used to minimize the makespan. Finally, two groups of instances are designed and three types of experiments are conducted to evaluate the performance of proposed method. The results show that the proposed method can achieve good results, showing the effectiveness of the presented approach.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
This preprint is available for download as a PDF.
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