Effects of Personnel Availability and Competency on Fleet Readiness
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Abstract
The planning of future operations is a complex process that requires knowledge and understanding of many different factors and resources. Although there is much literature on maintenance planning, existing work lacks the integration of robust personnel work schedules into scheduling algorithms. Thus the objective of this research is to develop a procedure that aids in the short- term planning of operations by predicting the future readiness level of a fleet of vehicles that are subjected to various personnel factors. This research presents a procedure that combines two different models to appropriately predict readiness levels at the end of a seven-period horizon. This first model is a Monte Carlo simulation that determines different personnel availability scenarios based on three different factors that affect the net resource pool of workers of a maintenance unit. These scenarios are then entered into a binary integer linear program (BILP) which literatively optimizes fleet maintenance schedules on a daily basis. An overall fleet readiness level with a certain degree of probability is determined which serves as an extremely useful tool for operations planning. In addition, sensitivity analysis is presented on the different factors affecting personnel availability that can serve as useful aids in operational decision-making.
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Fleet Readiness Level, Resource Planning, Maintenance Personnel Availability and Competency
Bard, J., & Wan, L. (2005). Weekly scheduling in the service industry: an application to mail processing and distribution centers. IIE Transactions , 37 (5), 379-396.
Cassady, C., Murdock, W., Nachlas, J., & Pohl, E. (1998). Comprehensive Fleet Maintenance Management. SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (pp. 4665-4669 vol. 5). New York, NY: IEEE.
Chattopadhyay. (1998, November). A Practical Maintenance Scheduling Program: Mathematical Model and Case Study. IEEE Transactions on Power Systems , 13 (4), pp. 1475-1580.
Chiesa, S., Quer, S., Corpino, S., & Viola, N. (2009). Heuristic and exact techniques for aircraft maintenance scheduling. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering , 223 (7), 989- 999.
Drew, J.G., Lynch, K., Masters, J., Tripp, R., & Roll Jr, C. (2008). Options for Meeting the Maintenance Demands of Active Associate Flying Units. Santa Monica, CA: RAND Corporation.
Duffuaa, S., & Al-Sultan, K. (1997). Mathematical programming approaches for the management of maintenance planning and scheduling. Journal of Quality in Maintenance Engineering , 3 (3), 163- 176.
Haghani, A., & Y, S. (2002). Bus maintenance systems and maintenance scheduling: model formulations and solutions. Transportation Research, Part A (Policy and Practice) , 36A (5), 453-482.
Howe, J. A., Thoele, B. A., Pendley, S. A., Antoline, A. F., & Golden, R. D. (2009). Beyond Authorized versus Assigned: Aircraft Maintenance Personnel Capacity. In A. B. Badiru, & M. U. Thomas, Handbook of Military Industrial Engineering (pp. 15.1-15.13). Boca Raton: CRC Press.
Leou, R.-C. (2001, August). A Flexible Unit Maintenance Scheduling Considering Uncertainties. IEEE Transactions on Power Systems , 16 (3), pp. 552-559.
Loucks, J., & Jacobs, F. (1991). Tour Scheduling and Task Assignment og a Heterogeneous Work Force: A Heuristic Approach. Decision Sciences , 22 (4), 719-738.
Luczak, H., & Mjema, E. (1999). A quantitative analysis of the factors affecting personnel capacity requirement in maintenance department. International Journal of Production Research , 37 (17), 4021-4037.
Yan, S., Yang, T.-H., & Chen, H.-H. (2004). Airline short-term maintenance manpower supply planning. Transportation Research Part A: Policy and Practice , 38 (9-10), 625-642.
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