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Degradation-Based Swapping Policy With Application to System-Level Manufacturing Utilization

[+] Author Affiliations
Ahmad Almuhtady, Seungchul Lee, Jun Ni

University of Michigan, Ann Arbor, MI

Paper No. MSEC2012-7280, pp. 673-682; 10 pages
  • ASME 2012 International Manufacturing Science and Engineering Conference collocated with the 40th North American Manufacturing Research Conference and in participation with the International Conference on Tribology Materials and Processing
  • ASME 2012 International Manufacturing Science and Engineering Conference
  • Notre Dame, Indiana, USA, June 4–8, 2012
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 978-0-7918-5499-0
  • Copyright © 2012 by ASME


Degradation is an inevitable course of any manufacturing tool, machine or system. The degradation of the health state of manufacturing tools results in some sort of an ineludible maintenance action which could be both costly and happening in a critical production time. In most manufacturing systems, a fleet of identical machines are assigned different tasks towards satisfying requirements within the production process. We introduce a degradation-based resource allocation policy to optimally utilize a fleet of identical machines. The policy, denoted as Degradation Based Swapping Optimization (DBSO), incorporates the optimal implementation of swapping scheduled tasks and scheduling maintenance actions throughout a finite time horizon to minimize projected maintenance costs and/or utilize the manufacturing productivity towards pre-specified logistics objectives. A mathematical model for the policy is provided and this model is optimized using elitist genetic algorithm and simulated annealing algorithm where numerical results have been introduced. The proposed policy succeeds in establishing substantial savings in the simulated example which amount to 43% of the estimated maintenance costs in comparison to the scenario where fixed scheduling is applied.

Copyright © 2012 by ASME
Topics: Manufacturing



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