Implementation of Fuzzy Model for Maintenance Scheduling of Vehicles Based on Monte Carlo Simulation and Geographical Information System

REFERENCES

• Haghani Ali, and Yousef Shafahi, “Bus maintenance system and maintenance scheduling: model formulation and solution,” Trans. Res. Part A: Policy Pract. Vol. 36, no. 5, pp. 383–482, Jun 2002.
   Web of Science ®Google Scholar
• R. Zhou, B. Fox, H.P. Lee, and A.Y.C. Nee, “Bus maintenance Scheduling using multi-agent systems,” Eng. Appl. Artificial Intelligence, Vol. 17, no. 6, pp. 623–30, Sep. 2004.
   Web of Science ®Google Scholar
• Adil Baykasoğlu, and Vahit Kaplanoğlu, “An application oriented multi-agent based approach to dynamic load/truck planning,” Expert Syst. Appl., Vol. 42, no. 15, pp. 6008–25, Sep. 2015.
   Web of Science ®Google Scholar
• Amir Khanlari, Kaveh Mohammadi, and Babak Sohrabi, “Prioritizing equipments for preventive maintenance (PM) activities using fuzzy rules,” Comput. Indus. Eng., Vol. 54, no. 2, pp. 169–84, Mar. 2008.
   Web of Science ®Google Scholar
• Davor Vujanovic, Vladimir Momcilovic, Nebojša Bojovic, and Vladimir Papic, “Evaluation of vehicle fleet maintenance management indicators by application of DEMATEL and ANP,” Expert Syst Appl. Vol. 39, no. 12, pp.10552–63, Sep. 2012.
   Web of Science ®Google Scholar
• Ming-Jong Yao, Jia-yen Huang, “A new optimal search algorithm for the transportation fleet maintenance scheduling problem,” J. Oper. Res. Soc. Japan, Vol. 49, no. 1, pp. 33–48, 2006.
   Google Scholar
• Mohammad M. Aldurgam, and Moustafa Elshafei, “Optimal Maintenance Scheduling of N-Vehicles with Time-varying reward functions and constrained maintenance decisions,” in Operation Research Proceeding, Berlin Heidelberg: Springer, 2011, pp. 379–84.
   Google Scholar
• James A. Rodger, “Toward reducing failure risk in an integrated vehicle health maintenance system A fuzzy multi-sensor data fusion Kalman filter approach for IVHMS,” Expert Syst. Appl., Vol. 39, no. 10, pp. 9821–36, Aug. 2012.
   Web of Science ®Google Scholar
• James A. Rodger, “A fuzzy nearest neighbor neural network statistical model for predicting demand for natural gas and energy cost savings in public buildings,” Expert Syst. Appl., Vol. 41, no. 4, pp. 1813–29, Mar. 2014.
   Web of Science ®Google Scholar
• Bozorgi A., M.M. Pedram, and G.R. Yousefi, “Unit Maintenance Scheduling: A robust model, based on fuzzy cost factor and peak loads,” Int. J. Electr. Power Energy Syst., Vol. 79, pp. 142–9, Jul. 2016.
   Web of Science ®Google Scholar
• Bozorgi A., M.M. Pedram, and G.R. Yousefi, “Unit Maintenance Scheduling by means of fuzzy game –theory, considering uncertainty in rival –Genco's data,” in 8th Iranian Conference on Electrical Engg. (ICEE), Jul. 8, 2010, pp. 889–94.
   Google Scholar
• Bozorgi A., M.M. Pedram, and G.R. Yousefi, “A fuzzy game scheme for Unit Maintenance Scheduling with uncertain cost factor,” in Asia Pacific Power and Energy Engg. Conference (APPEEC), Apr. 15, 2010, pp. 1–6.
   Google Scholar
• D. K. Mohanta, Pradip Kumar Sadhu, and R. Chakrabarti, “Deterministic and stochastic approach for safety and reliability optimization of captive power plant maintenance scheduling using GA/SA-based hybrid techniques: A comparison of results,” Reliab. Engg. Syst. Safety, Vol. 92, no. 2, pp. 187–99, Feb. 2007.
   Web of Science ®Google Scholar
• D.K. Mohanta, Pradip Kumar Sadhu, and R. Chakrabarti, “Optimization of safety and reliability of Captive power plant maintenance scheduling using Genetic Algorithm and Simulated Annealing,” Int. J. Emerging Electric Power Syst., Vol. 3, no. 1, Aug. 2005, Article 1037.
   Google Scholar
• C.J. Davis and T.W. Reisinger, “Evaluating terrain for harvesting equipment selection,” J. Forest Eng., Vol. 2, no. 1, pp. 9–16, 1990.
   Google Scholar
• Chris C. Ward and Karl Iagnemma, “Speed-independent vibration-based terrain classification for passenger vehicles,” Vehicle Syst. Dyn., Vol. 47, no. 9, pp. 1095–113, Sep. 2009.
   Web of Science ®Google Scholar
• Forest engineering, chapter7, “terrain classification” with link pp. 42–3. “http://www.sappi.com/regions/sa/SappiSouthernAfrica/Sappi%20Forests/Tree%20Farming%20Guidelines/Part%203_Forest%20Engineering_Chapter%207_Terrain%20Classification.pdf”.
   Google Scholar
• O. Aloquili, A. Elbanna, and A. Al-Azizi, “Automatic vehicle location tracking system based on GIS environment,” IET Softw., Vol. 3, no. 4, pp. 255–63, Jul. 2009.
   Web of Science ®Google Scholar
• Vehicle technologies, office, workshop report on, “Trucks and heavy-duty vehicle technical requirements and gaps for light weight and propulsion materials” energy efficiency and renewable energy, February 2013.
   Google Scholar
• M. Jaya Bharata Reddy and Dusmanta Kumar Mohanta, “Performance evaluation of an adaptive-network based fuzzy inference system approach for location of faults on transmission line using Monte Carlo simulation,” IEEE Trans. Fuzzy Syst., Vol. 16, no. 4, pp. 909–19, Aug. 2008.
   Web of Science ®Google Scholar
• J.L. Torres, A. Garcia, M. De Blas, and A. De Francisco, “Forecast of hourly average wind speed with ARMA models in Navarre (Spain),” Solar Energy, Vol. 79, no. 1, pp. 65–77, Jul. 2005.
   Web of Science ®Google Scholar
• L.A. Zadeh, “Fuzzy sets,” Inform. Control, Vol. 8, no. 3, pp. 338–53, Jun. 1965.
   Web of Science ®Google Scholar