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Clustering and Genetic Algorithm Based Hybrid Flowshop Scheduling with Multiple Operations
KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems.
2014 (English)In: Mathematical problems in engineering (Print), ISSN 1024-123X, E-ISSN 1563-5147Article in journal (Refereed) Published
Abstract [en]

This research is motivated by a flowshop scheduling problem of our collaborative manufacturing company for aeronautic products. The heat-treatment stage (HTS) and precision forging stage (PFS) of the case are selected as a two-stage hybrid flowshop system. In HTS, there are four parallel machines and each machine can process a batch of jobs simultaneously. In PFS, there are two machines. Each machine can install any module of the four modules for processing the workpeices with different sizes. The problem is characterized by many constraints, such as batching operation, blocking environment, and setup time and working time limitations of modules, and so forth. In order to deal with the above special characteristics, the clustering and genetic algorithm is used to calculate the good solution for the two-stage hybrid flowshop problem. The clustering is used to group the jobs according to the processing ranges of the different modules of PFS. The genetic algorithm is used to schedule the optimal sequence of the grouped jobs for the HTS and PFS. Finally, a case study is used to demonstrate the efficiency and effectiveness of the designed genetic algorithm.

Place, publisher, year, edition, pages
Hindawi Publishing Corporation, 2014.
National Category
Mechanical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-253506DOI: 10.1155/2014/167073ISI: 000333819000001Scopus ID: 2-s2.0-84899422128OAI: oai:DiVA.org:kth-253506DiVA, id: diva2:1325407
Note

QC 20190625

Available from: 2019-06-15 Created: 2019-06-15 Last updated: 2019-06-25Bibliographically approved

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Liu, Sichao

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