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Research Papers

Integrated Maintenance Decision-Making and Product Sequencing in Flexible Manufacturing Systems

[+] Author and Article Information
Merve Celen

Department of Mechanical Engineering,
The University of Texas at Austin,
Austin, TX 78712
e-mail: mervecelen@utexas.edu

Dragan Djurdjanovic

Department of Mechanical Engineering,
The University of Texas at Austin,
Austin, TX 78712
e-mail: dragand@me.utexas.edu

Matrices P(m) are assumed to be such that Pd,e(m)=0 for d > e in order to model the intuition that maintenance does not worsen the state of the machine.

Effectively, [d1,,dF] is the sequence in which the products are released into the system.

Products of acceptable quality.

Of course, if such periods of cheaper maintenance do not exist in the given FMS, then ccr=cr.

Once again, if such periods of time do not exist in a given FMS, then ccp=cp.

Multiple chambers as manufacturing stations, supported by a material handling system.

Regardless of the cheap/regular maintenance ratio, average maintenance costs over the length of the day were kept the same as they were for the baseline example.

I.e., as one increases the production goals Nwj for each product type in the cost functions (1)(3).

Situations when Markovian transition matrices corresponding to PMs have higher likelihood of returning the system closer to the as good as new state, i.e., scenarios obtained as we go from Markovian transition matrix MP1 to MP2, then on to MP3 and so on.

1Corresponding author.

Contributed by the Manufacturing Engineering Division of ASME for publication in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript received May 23, 2014; final manuscript received March 25, 2015; published online July 8, 2015. Assoc. Editor: Wayne Cai.

J. Manuf. Sci. Eng 137(4), 041006 (Aug 01, 2015) (15 pages) Paper No: MANU-14-1291; doi: 10.1115/1.4030301 History: Received May 23, 2014; Revised March 25, 2015; Online July 08, 2015

In highly flexible and integrated manufacturing systems, such as semiconductor manufacturing, the strong dynamic interactions between the equipment condition, operations executed on the equipment, and the resulting product quality necessitate a methodology that integrates the decision-making process across the domains of maintenance scheduling and production operations. Currently, maintenance and production operations decision-making are two decoupled processes. In this paper, we devise an integrated decision-making policy for maintenance scheduling and production sequencing, with the objective of optimizing a customizable objective function, while taking into account operation-dependent degradation models and a production target. Optimization was achieved using a metaheuristic method based on the results of discrete-event simulations of the target manufacturing system. The new approach is demonstrated in simulations of a generic cluster tool routinely used in semiconductor manufacturing. The results show that jointly making maintenance and production sequencing decisions consistently and often significantly outperforms the current practice of making these decisions separately.

Copyright © 2015 by ASME
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Figures

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Fig. 1

Representation of a candidate solution for the integrated decision-making policy

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Fig. 2

Neighborhood generation for maintenance triggering states

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Fig. 3

Neighborhood generation for product type sequence

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Fig. 4

Comparison of expected profits for the integrated decision-making policy, operation-dependent CBM policy and the traditional operation-independent CBM policy

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Fig. 5

Comparison between the expected numbers of maintenance events conducted during the expensive and cheap maintenance periods

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Fig. 6

Comparison of expected profits for different cheap/regular maintenance cost ratios (black lines denote the ±2σ limits of the simulation outcomes) for the integrated decision-making policy, operation-dependent CBM policy and the traditional operation-independent CBM policy

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Fig. 7

Comparison of number of regular and cheap PM events for various cost ratios

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Fig. 8

Percent improvement obtained by the integrated decision-making policy over the benchmark policies for different cheap/regular maintenance cost ratios

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Fig. 9

Comparison of expected profits for changing unmet production penalty costs (black lines denote the ±2σ limits of the simulation outcomes) for the integrated decision-making policy, operation-dependent CBM policy and the traditional operation-independent CBM policy

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Fig. 10

Percent improvement obtained by the integrated decision-making policy over the benchmark policies for different unmet production penalty costs

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Fig. 11

Expected total number of PM events for changing penalty costs (black lines denote ±2σ limits of the simulation outcomes) for the integrated decision-making policy, operation-dependent CBM policy and the traditional operation-independent CBM policy

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Fig. 12

Comparison of expected profits for different production goals (black lines denote the ±2σ limits of the simulation outcomes) for the integrated decision-making policy, operation-dependent CBM policy and the traditional operation-independent CBM policy

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Fig. 13

Total expected maintenance cost obtained by different maintenance policies for increasing production goals

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Fig. 14

Percent improvement over benchmark policies obtained by the integrated decision-making policy for different production goals

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Fig. 15

Probabilities of meeting the production goal for three decision-making policies over increasing production goals

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Fig. 16

Percent improvement over the benchmark policies obtained by the integrated decision-making policy for higher unmet production penalty costs

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Fig. 17

Comparison of expected profits for different probabilities of perfect maintenance (black lines denote the ±2σ limits of the simulation outcomes) for the integrated decision-making policy, operation-dependent CBM policy and the traditional operation-independent CBM policy

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Fig. 18

Percent improvement obtained by the integrated decision-making policy over the benchmark policies for different probabilities of perfect maintenance

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Fig. 19

Operation-specific transition probability matrices and maintenance transition probability matrix for integrated decision-making

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Fig. 20

Perfect maintenance probabilities used for sensitivity analysis

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