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A two-phase genetic algorithm to solve variants of the batch sequencing problem.

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Τίτλος: A two-phase genetic algorithm to solve variants of the batch sequencing problem.
Συγγραφείς: Jordan, C.
Πηγή: International Journal of Production Research. Mar1998, Vol. 36 Issue 3, p745-760. 16p.
Θεματικοί όροι: *Algorithms, Batch processing in electronic data processing, Programmable sequence controllers
Περίληψη: We introduce the batch sequencing problem (BSP) with item and batch availability for the single-machine and two-machine flow-shop case. We propose a genetic algorithm which solves the BSP through a decomposition into a phase I-batching and a phase II-scheduling decision. The batch sequencing problem is closely related to the discrete lotsizing and scheduling problem (DLSP). Computational experience shows that the genetic algorithm for solving the BSP favourably compares with procedures for solving the DLSP. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A two-phase genetic algorithm to solve variants of the batch sequencing problem.
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  Data: We introduce the batch sequencing problem (BSP) with item and batch availability for the single-machine and two-machine flow-shop case. We propose a genetic algorithm which solves the BSP through a decomposition into a phase I-batching and a phase II-scheduling decision. The batch sequencing problem is closely related to the discrete lotsizing and scheduling problem (DLSP). Computational experience shows that the genetic algorithm for solving the BSP favourably compares with procedures for solving the DLSP. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Mar1998
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