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Scheduling job families about an unrestricted common due date on a single machine.

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Title: Scheduling job families about an unrestricted common due date on a single machine.
Authors: Azizoglu, M.1, Webster, S.2
Source: International Journal of Production Research. May97, Vol. 35 Issue 5, p1321-1330. 10p.
Subjects: Production scheduling, Manufacturing processes, Production methods, Algorithms, Operations research
Abstract: We consider the NP-hard problem of scheduling jobs on a single machine about an unrestricted due date to minimize total weighted earliness and tardiness cost. Jobs are grouped into families where jobs in the same family share a setup; a setup time is required between the processing of two jobs from different families. Each job has an earliness penalty rate and a tardiness penalty rate that are allowed to be arbitrary. These rates are assessed on a per-period basis when the completion time deviates from its due date. In this paper, we generalize properties from the literature that characterize the structure of an optimal schedule, present a lower bound, propose a branch and bound algorithm and a beam search procedure, and report results from a computational experiment. We find that optimal solutions can be quickly obtained for smaller problem instances. For large problems, we find high quality solutions within a few minutes of CPU time. [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: Scheduling job families about an unrestricted common due date on a single machine.
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  Data: We consider the NP-hard problem of scheduling jobs on a single machine about an unrestricted due date to minimize total weighted earliness and tardiness cost. Jobs are grouped into families where jobs in the same family share a setup; a setup time is required between the processing of two jobs from different families. Each job has an earliness penalty rate and a tardiness penalty rate that are allowed to be arbitrary. These rates are assessed on a per-period basis when the completion time deviates from its due date. In this paper, we generalize properties from the literature that characterize the structure of an optimal schedule, present a lower bound, propose a branch and bound algorithm and a beam search procedure, and report results from a computational experiment. We find that optimal solutions can be quickly obtained for smaller problem instances. For large problems, we find high quality solutions within a few minutes of CPU time. [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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        Value: 10.1080/002075497195344
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        Text: English
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      – SubjectFull: Production scheduling
        Type: general
      – SubjectFull: Manufacturing processes
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      – SubjectFull: Production methods
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      – SubjectFull: Algorithms
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      – SubjectFull: Operations research
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      – TitleFull: Scheduling job families about an unrestricted common due date on a single machine.
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              Text: May97
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              Y: 1997
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