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ASP: An Adaptive Setup Planning Approach for Dynamic Machine Assignments
KTH, School of Industrial Engineering and Management (ITM), Production Engineering. University of Skövde.
2010 (English)In: IEEE Transactions on Automation Science and Engineering, ISSN 1545-5955, E-ISSN 1558-3783, Vol. 7, no 1, p. 2-14Article in journal (Refereed) Published
Abstract [en]

This paper presents a decision-making approach towards adaptive setup planning that considers both the availability and capability of machines on a shop floor. It loosely integrates scheduling functions at the setup planning stage, and utilizes a two-step decision-making strategy for generating machine-neutral and machine-specific setup plans at each stage. The objective of the research is to enable adaptive setup planning for dynamic job shop machining operations. Particularly, this paper covers basic concepts and algorithms for one-time generic setup planning, and run-time final setup merging for dynamic machine assignments. The decision-making algorithms validation is further demonstrated through a case study. Note to Practitioners-With increased product diversification, companies must be able to profitably produce in small quantities and make frequent product changeovers. This leads to dynamic job shop operations that require a growing number of setups in a machine shop. Moreover, today's customer-driven market and just-in-time production demand for rapid and adaptive decision making capability to deal with dynamic changes in the job shop environment. Within the context, how to come up with effective and efficient setup plans where machine availability and capability change over time is crucial for engineers. The adaptive setup planning approach presented in this paper is expected to largely enhance the dynamism of fluctuating job shop operations through adaptive yet rapid decision makings.

Place, publisher, year, edition, pages
IEEE Press, 2010. Vol. 7, no 1, p. 2-14
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-205739DOI: 10.1109/TASE.2008.2011919OAI: oai:DiVA.org:kth-205739DiVA, id: diva2:1090277
Note

QC 20170519

Available from: 2017-04-24 Created: 2017-04-24 Last updated: 2017-05-19Bibliographically approved

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  • apa
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