Online Scheduling in Manufacturing: A Cumulative Delay Approach 2013th Edition

Online Scheduling in Manufacturing: A Cumulative Delay Approach 2013th Edition book cover

Online Scheduling in Manufacturing: A Cumulative Delay Approach 2013th Edition

Author(s): Haruhiko Suwa (Author), Hiroaki Sandoh (Author)

  • Publisher: Springer
  • Publication Date: 11 Oct. 2012
  • Edition: 2013th
  • Language: English
  • Print length: 168 pages
  • ISBN-10: 1447145607
  • ISBN-13: 9781447145608

Book Description

Online scheduling is recognized as the crucial decision-making process of production control at a phase of “being in production” according to the released shop floor schedule. Online scheduling can be also considered as one of key enablers to realize prompt capable-to-promise as well as available-to-promise to customers along with reducing production lead times under recent globalized competitive markets.

Online Scheduling in Manufacturing introduces new approaches to online scheduling based on a concept of cumulative delay. The cumulative delay is regarded as consolidated information of uncertainties under a dynamic environment in manufacturing and can be collected constantly without much effort at any points in time during a schedule execution. In this approach, the cumulative delay of the schedule has the important role of a criterion for making a decision whether or not a schedule revision is carried out. The cumulative delay approach to trigger schedule revisions has the following capabilities for the practical decision-making:

1. To reduce frequent schedule revisions which do not necessarily improve a current situation with much expense for its operation;

2. To avoid overreacting to disturbances dependent on strongly an individual shop floor circumstance; and

3. To simplify the monitoring process of a schedule status.

Online Scheduling in Manufacturing will be of interest to both practitioners and researchers who work in planning and scheduling in manufacturing. Readers will find the importance of when-to-revise policies during a schedule execution and their influences on scheduling results.

Editorial Reviews

From the Back Cover

Online scheduling is recognized as the crucial decision-making process of production control at a phase of “being in production” according to the released shop floor schedule. Online scheduling can be also considered as one of key enablers to realize prompt capable-to-promise as well as available-to-promise to customers along with reducing production lead times under recent globalized competitive markets.

Online Scheduling in Manufacturing introduces new approaches to online scheduling based on a concept of cumulative delay. The cumulative delay is regarded as consolidated information of uncertainties under a dynamic environment in manufacturing and can be collected constantly without much effort at any points in time during a schedule execution. In this approach, the cumulative delay of the schedule has the important role of a criterion for making a decision whether or not a schedule revision is carried out. The cumulative delay approach to trigger schedule revisions has the following capabilities for the practical decision-making:

1. To reduce frequent schedule revisions which do not necessarily improve a current situation with much expense for its operation;

2. To avoid overreacting to disturbances dependent on strongly an individual shop floor circumstance; and

3. To simplify the monitoring process of a schedule status.

Online Scheduling in Manufacturing will be of interest to both practitioners and researchers who work in planning and scheduling in manufacturing. Readers will find the importance of when-to-revise policies during a schedule execution and their influences on scheduling results.

About the Author

Haruhiko Suwa is a Professor of Department of Mechanical Engineering at Setsunan University. His research is in Manufacturing Engineering and Systems Engineering.

  

Hiroaki Sandoh is a Professor at Graduate School of Economics, Osaka University, working in the areas of Operations Research and Management Science.

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