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A field service support system using a queueing network model and the priority MVA algorithm

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  • Papadopoulos, H. T.

Abstract

In many companies, the field service (FS) department plays an important role, contributing up to 40% to the company's total revenue. FS managers have to cope with conflicting objectives: (a) to maintain a high level of customer service and (b) to keep the spares inventory level as low as possible. Therefore, they need tools to analyse the impact of their decisions on customer service and inventory cost. Such a tool is presented in this paper. We have developed a closed queueing network model, similar to one due to Waller, incorporating priority classes of customers via the application of the priority mean value analysis (PMVA) algorithm, developed by Shalev-Oren et al. This model has been applied to the FS organization of the Greek subsidiary of a multinational computer company, and it has proved to be very efficient from the computational point of view, thus constituting a powerful tool for the FS managers.

Suggested Citation

  • Papadopoulos, H. T., 1996. "A field service support system using a queueing network model and the priority MVA algorithm," Omega, Elsevier, vol. 24(2), pages 195-203, April.
    • Handle: RePEc:eee:jomega:v:24:y:1996:i:2:p:195-203
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    References listed on IDEAS

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    1. Waller, AAW, 1994. "A queueing network model for field service support systems," Omega, Elsevier, vol. 22(1), pages 35-40, January.
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    Cited by:

    1. R. Sundarraj, 2006. "A model for standardizing human decisions concerning service-contracts management," Annals of Operations Research, Springer, vol. 143(1), pages 171-189, March.
    2. Ku, Cheng-Yuan & Yen, David C. & Chang, I-Chiu & Huang, Shi-Ming & Jordan, Scott, 2006. "Near-optimal control policy for loss networks," Omega, Elsevier, vol. 34(4), pages 406-416, August.
    3. Rahimi-Ghahroodi, S. & Al Hanbali, A. & Vliegen, I.M.H. & Cohen, M.A., 2019. "Joint optimization of spare parts inventory and service engineers staffing with full backlogging," International Journal of Production Economics, Elsevier, vol. 212(C), pages 39-50.
    4. Chen, Shih-Pin, 2007. "Solving fuzzy queueing decision problems via a parametric mixed integer nonlinear programming method," European Journal of Operational Research, Elsevier, vol. 177(1), pages 445-457, February.
    5. Colen, P.J. & Lambrecht, M.R., 2012. "Cross-training policies in field services," International Journal of Production Economics, Elsevier, vol. 138(1), pages 76-88.
    6. Douniel Lamghari-Idrissi & Roy van Hugten & Geert-Jan van Houtum & Rob Basten, 2022. "Increasing Chip Availability Through a New After-Sales Service Supply Concept at ASML," Interfaces, INFORMS, vol. 52(5), pages 460-470, September.
    7. Vardar, Cem & Gel, Esma S. & Fowler, John W., 2007. "A framework for evaluating remote diagnostics investment decisions for semiconductor equipment suppliers," European Journal of Operational Research, Elsevier, vol. 180(3), pages 1411-1426, August.
    8. Kennedy, W. J. & Wayne Patterson, J. & Fredendall, Lawrence D., 2002. "An overview of recent literature on spare parts inventories," International Journal of Production Economics, Elsevier, vol. 76(2), pages 201-215, March.
    9. S. Rahimi-Ghahroodi & A. Al Hanbali & W. H. M. Zijm & J. K. W. Ommeren & A. Sleptchenko, 2017. "Integrated planning of spare parts and service engineers with partial backlogging," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(3), pages 711-748, July.
    10. Lee, Loo Hay & Chew, Ek Peng & Teng, Suyan & Chen, Yankai, 2008. "Multi-objective simulation-based evolutionary algorithm for an aircraft spare parts allocation problem," European Journal of Operational Research, Elsevier, vol. 189(2), pages 476-491, September.

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