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Determining the operator-machine assignment for machine interference problem and an empirical study in semiconductor test facility

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Abstract

Semiconductor is a capital-intensive industry in which equipment costs account for more than seventy percentage of the capital investment in semiconductor test facilities. In an industrial investigation, the machine interference may be 10% of machine time. Hence, there is a need to assign an appropriate number of machines to the operators to minimize machine interference time or labor cost. This paper aims to develop an effective methodology to determine the optimal assignment relationships between the test machines and the operators for different product mixes to enhance utilization for the optimal system performance. In particular, we employed response surface methodology and genetic algorithms to explore alternative assignment rations and thus identify well-performed assignment alternatives for the test machines and operators in various decision contexts with simulation. An empirical study with real data collected was conducted in a semiconductor test facility to validate this approach. The results have shown the validity of the proposed approach in real settings. Indeed, the developed approach has been implemented on line.

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References

  • Ahuja, R. K., Orlin, J. B., & Tiwari, A. (2000). A greedy genetic algorithm for the quadratic assignment problem. Computers and Operations Research, 27(10), 1–24.

    Article  Google Scholar 

  • Ashcroft, M. A. (1950). The productivity of several machines under the care of one operator. Journal of the Royal Statistical Society (B), 12(1), 145–151.

    Google Scholar 

  • Bahnasawi, A. A., Mahmoud, M. S., & Eid, S. Z. (1996). Sensitivity analysis of machine interference in manufacturing systems. Computers and Industrial Engineering, 30(4), 753–764.

    Article  Google Scholar 

  • Chan, F. T. S., Wong, T. C., & Chan, L. Y. (2005). A genetic algorithm-based approach to machine assignment problem. International Journal of Production Research, 43(12), 2451–2472.

    Article  Google Scholar 

  • Chen, W., & Chien, C.-F. (2011). Measuring relative performance of wafer fabrication operations: A case study. Journal of Intelligent Manufacturing, 22(3), 447–457.

    Article  Google Scholar 

  • Chien, C.-F., & Chen, C.-H. (2007a). Using GA and CTPN for modeling the optimization-based schedule generator of a generic production scheduling system. International Journal of Production Research, 45(8), 1763–1789.

    Article  Google Scholar 

  • Chien, C.-F., & Chen, L.-F. (2007b). Using rough set theory to recruit and retain high-potential talents for semiconductor manufacturing. IEEE Transactions on Semiconductor Manufacturing, 20(4), 528–541.

    Article  Google Scholar 

  • Chien, C.-F., & Chen, L.-F. (2008). Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry. Expert Systems with Applications, 34(1), 280–290.

    Article  Google Scholar 

  • Chien, C.-F., Chen, Y., & Peng, J. (2010). Manufacturing intelligence for semiconductor demand forecast based on technology diffusion and product life cycle. International Journal of Production Economics, 128(2), 496–509.

    Article  Google Scholar 

  • Chien, C.-F., & Hsu, C. (2006). A novel method for determining machine subgroups and backups with an empirical study for semiconductor manufacturing. Journal of Intelligent Manufacturing, 17(4), 429–439.

    Google Scholar 

  • Chien, C.-F., & Hsu, C. (2011). UNISON analysis to model and reduce step-and-scan overlay errors for semiconductor manufacturing. Journal of Intelligent Manufacturing, 22(3), 399–412.

    Article  Google Scholar 

  • Chien, C.-F., Hsu, C., & Hsiao, C. (2012). Manufacturing intelligence to forecast and reduce semiconductor cycle time. Journal of Intelligent Manufacturing, 23(6), 2281–2294.

    Article  Google Scholar 

  • Chien, C.-F., Wang, H.-J., & Wang, M. (2007). A UNISON framework for analyzing alternative strategies of IC final testing for enhancing overall operational effectiveness. International Journal of Production Economics, 107(1), 20–30.

    Article  Google Scholar 

  • Chien, C.-F., & Zheng, J.-N. (2012). Mini-max regret strategy for robust capacity expansion decisions in semiconductor manufacturing. Journal of Intelligent Manufacturing, 23(6), 2151–2159.

    Google Scholar 

  • Deng, Y., Bard, J. F., Chacon, G. R., & Stuber, J. (2010). Scheduling back-end operations in semiconductor manufacturing. IEEE Transactions on Semiconductor Manufacturing, 23(2), 210–220.

    Article  Google Scholar 

  • Gen, M., & Cheng, R. (1997). Genetic algorithms and engineering design. New York: Wiley.

    Google Scholar 

  • Gupta, S. M. (1996). Machine interference problem with warm spares, server vacations and exhaustive service. Performance Evaluation, 29(3), 195–211.

    Article  Google Scholar 

  • Gupta, U. C., & Srinivasa Rao, T. S. S. (1996). Computing steady state probabilities in \(\lambda \text{(n)/G/1/K }\) queue. Performance Evaluation, 24(4), 265–275.

    Article  Google Scholar 

  • Haque, L., & Armstrong, M. J. (2007). A survey of the machine interference problem. European Journal of Operational Research, 179(2), 469–482.

    Article  Google Scholar 

  • Hsieh, Y.-C. (1997). Optimal assignment of priorities for the machine interference problems. Microelectronics and Reliability, 37(4), 635–640.

    Article  Google Scholar 

  • Hung, Y.-F., & Chen, I.-H. (2000). Dynamic operator assignment based on shifting machine loading. International Journal of Production Research, 38(14), 3403–3420.

    Article  Google Scholar 

  • Ignizio, J. P. (2004). Optimal maintenance headcount allocation: An application of Chebyshev goal programming. International Journal of Production Research, 42(1), 201–210.

    Article  Google Scholar 

  • Jackman, J., & Johnson, E. (1993). The role of queueing network models in performance evaluation of manufacturing system. Journal of the Operation Research Society, 44(8), 797–807.

    Article  Google Scholar 

  • Lachenmaier, L. S., Cochran, J. K., (1996). Use of resource blocks in SLAM II for the evaluation of operator-machine assignments. In Proceedings of the 1996 winter simulation conference, pp. 1135–1140.

  • Law, A. M., & Kelton, W. D. (1991). Simulation modeling and analysis (2nd ed.). New York: McGraw-Hill.

    Google Scholar 

  • Little, J. D. C. (1961). A proof for the queueing formula \(\text{ L }=\Lambda \text{ w }\). Operation Research, 9, 383–387.

    Article  Google Scholar 

  • Luo, X., Li, W., Tu, Y., Xue, D., & Tang, J. (2011). Operator allocation planning for reconfigurable production line in one-of-a-kind production. International Journal of Production Research, 49(3), 689–705.

    Article  Google Scholar 

  • Nazzal, D., Mollaghasemi, M., & Anderson, D. (2006). A simulation-based evaluation of the cost of cycle time reduction in Agere Systems wafer fabrication facility: A case study. International Journal of Production Economics, 100(2), 300–313.

    Article  Google Scholar 

  • Oh, K. H. (1996). The computerized operator-machine system (OMS) for the least cost combination of operators and machines. Computers and Industrial Engineering, 31(1), 159–162.

    Article  Google Scholar 

  • Özcan, U., Kellegöz, T., & Toklu, B. (2011). A genetic algorithm for the stochastic mixed-model U-line balancing and sequencing problem. International Journal of Production Research, 49(6), 1605–1626.

    Article  Google Scholar 

  • Raviv, A. (1998). Optimal staffing in semiconductor manufacturing: A queueing theory approach. Solid State Technology, 41(4), 77–82.

    Google Scholar 

  • Reeves, C. (1995). A genetic algorithm for flowshop sequencing. Computers & Operations Research, 22(1), 5–13.

    Google Scholar 

  • Rossetti, M. D., & Clark, G. (1998). Evaluating a queueing approximation for the machine interference problem with two types of stoppages via simulation optimization. Computers Industrial Engineering, 34(3), 655–668.

    Article  Google Scholar 

  • Stafford, E. F. (1988). An optimal solution technique for the operator-machine assignment problem. Production and Inventory Management Journal, 29(9), 1354–1359.

    Google Scholar 

  • Wang, K.-H., Chen, W.-L., & Yang, D.-Y. (2009). Optimal management of the machine repair problem with working vacation: Newton’s method. Journal of Computational and Applied Mathematics, 233, 449–458.

    Article  Google Scholar 

  • Wu, J.-Z. (2013). Inventory write-down prediction for semiconductor manufacturing considering inventory age, accounting principle, and product structure with real settings. Computers & Industrial Engineering, 65(1), 128–136.

    Google Scholar 

  • Wu, J.-Z., & Chien, C.-F. (2008). Modeling strategic semiconductor assembly outsourcing decisions based on empirical settings. OR Spectrum, 30(3), 401–430.

    Google Scholar 

  • Yang, T., Chen, M.-C., & Hung, C.-C. (2007). Multiple attribute decision-making methods for the dynamic operator allocation problem. Mathematics and Computers in Simulation, 73, 285–299.

    Article  Google Scholar 

  • Yang, T., Lee, R.-S., Chen, M.-C., & Chen, P. (2005). Queueing network model for a single-operator machine interference problem with external operations. European Journal of Operational Research, 167(1), 163–178.

    Article  Google Scholar 

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Acknowledgments

This research is supported by National Science Council, Taiwan (NSC 99-2221-E-007 -047 -MY3), National Tsing Hua University under the Toward World-Class University Project (101N2074E1), and Macronix International Ltd.

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Authors and Affiliations

  1. Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, 30013, Taiwan, ROC

    Chen-Fu Chien, Jia-Nian Zheng & Yi-Jay Lin

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  1. Chen-Fu Chien
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  2. Jia-Nian Zheng
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  3. Yi-Jay Lin
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Correspondence to Chen-Fu Chien.

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Chien, CF., Zheng, JN. & Lin, YJ. Determining the operator-machine assignment for machine interference problem and an empirical study in semiconductor test facility. J Intell Manuf 25, 899–911 (2014). https://doi.org/10.1007/s10845-013-0777-3

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  • DOI: https://doi.org/10.1007/s10845-013-0777-3

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