International Journal of Nonlinear Analysis and Applications

Developing a new fuzzy inference model for warehouse maintenance scheduling under an agile environment

Document Type : Research Paper

Authors

1 Department of Industrial Engineering, Imam Ali Military University, Tehran, Iran

2 bDepartment of Mechanical Engineering, Imam Ali Military University, Tehran, Iran

3 Department of Civil Engineering, Gazvin Branch, Islamic Azad University, Gazvin, Iran

Abstract

Today, the military bodies of different countries have identified the existence of a condition assessment system in order to optimize the process of maintenance and repair of military buildings and equipment as a need and are looking for a suitable answer. The issue of less and/or outright lack of knowledge and uncertainty in modeling and decision-making plays a crucial part in many engineering and especially military difficulties, resulting in designers and engineers being unable to obtain definitive solutions for the problems under discussion. This study develops a fuzzy logic application for representing the uncertainty inherent in the problem of warehouse maintenance scheduling. The relative risk score (RRS) approach, one of the most prevalent methodologies for maintenance assessment, is combined with fuzzy logic to achieve the goal. Based on expert knowledge, the suggested model is run on the MATLAB® fuzzy logic toolbox using the Mamdani algorithm. A representative case study is used, and a comparison is made between the traditional risk assessment technique and the suggested model. The findings show that the suggested model produces more accurate, exact, and certain results, allowing it to be used as an intelligent risk assessment tool in many engineering settings.

Keywords

[1] M. Aalipour Erdi, B. Malekmohammadi, and H.R. Jafari, Risk zoning of land subsidence due to groundwater level declining using fuzzy analytical hierarchy process, Iran. J. Watershed Manag. Sci. Engin. 11 (2017), no. 38, 25–34.
[2] S. Abdollahi, H.R. Pourghasemi, G.A. Ghanbarian, and R. Safaeian, Prioritization of effective factors in the occurrence of land subsidence and its susceptibility mapping using an SVM model and their different kernel functions, Bull. Engin. Geol. Envir. 78 (2019), 4017–4034.
[3] K.M. Atashgah, R. Ghousi, A.M. Abbasi, and A.T. Nasrabadi, A development model for identifying the uncertainty sources and their impacts on bridge construction projects, Baltic J. Road Bridge Engin. 18 (2023), no. 1, 140–166.
[4] T. Breton, J.C. Sanchez-Gheno, J.L. Alamilla, and J. Alvarez-Ramirez, Identification of failure type in corroded pipelines: A Bayesian probabilistic approach, J. Hazardous Mater. 179 (2010), 628–634.
[5] A.J. Brito, A.T. Almeida and C.M.M. Mota, A multicriteria model for risk sorting of natural gas pipelines based on ELECTRE TRI integrating Utility Theory, Eur. J. Oper. Res. 200 (2010), 812–821.
[6] E. Cagno, F. Caron, M. Mancini, and F. Ruggeri, Using AHP in determining the prior distributions on gas pipeline failures in a robust Bayesian approach, Reliab. Engin. Syst. Safety 67 (2000), 275–284.
[7] T. Chanapathi, S. Thatikonda, V.P. Pandey, and S. Shrestha, Fuzzy-based approach for evaluating groundwater sustainability of Asian cities, Sustain. Cities Soc. 44 (2019), 321–331.
[8] A. Daftaribesheli, M. Ataei, and F. Sereshki, Assessment of rock slope stability using the Fuzzy Slope Mass Rating (FSMR) system, Appl. Soft Comput.11 (2011), 4465–4473.
[9] M. Dziubinski, M. Fratczaka, and A.S. Markowski, Aspects of risk analysis associated with major failures of fuel pipelines, J. Loss Prevent. Process Ind. 19 (2006), 399–408.
[10] M.M. Fouladgar, A. Yazdani-Chamzini, and E.K. Zavadskas, An integrated model for prioritizing strategies of the Iranian mining sector, Technol. Econ. Dev.f Econ. 17 (2011), no. 3, 459–483.
[11] M.J. Gharabagh, H. Asilian, S.B. Mortasavi, A.Z. Mogaddam, E. Hajizadeh, and A. Khavanin, Comprehensive risk assessment and management of petrochemical feed and product transportation pipelines, J. Loss Prevent. Process Ind. 22 (2009), 533-539.
[12] E. Ghasemi and M. Ataei, Application of fuzzy logic for predicting roof fall rate in coal mines, Neural Comput. Appl. 22 (2013), no. 1, 311–321.
[13] R. Ghousi, M. Khanzadi, and K. Mohammadi Atashgah, A flexible method of building construction safety risk assessment and investigating financial aspects of safety program, Int. Journal of Optim. Civil Engin. 8 (2018), no. 3, 433–452.
[14] M.A. Grima, P.A. Bruines and P.N.W. Verhoef, Modelling tunnel boring machine performance by neuro-fuzzy methods, Tunnell. Underground Space Technol. 15 (2000), 259–269.
[15] Z.Y. Han and W.G. Weng, An integrated quantitative risk analysis method for natural gas pipeline network, J. Loss Prevent. Process Ind. 23 (2010), 428–436.
[16] M. Iphar and R.M. Goktan, An application of fuzzy sets to the diggability index rating method for surface mine equipment selection, Int. J. Rock Mech. Min. Sci. 43 (2006), 253-266.
[17] J.S.R. Jang, C.T. Sun, and E. Mizutani, Neural-Fuzzy and Soft Computing, Prentice-Hall, Englewood Cliffs, NJ, 1997.
[18] Y.D. Jo and B.J. Ahn, A method of quantitative risk assessment for transmission pipeline carrying natural gas, J. Hazardous Mater. 123 (2005), 1–12.
[19] Z. Li, Fuzzy Chaotic Systems: Modeling, Control, and Applications, Springer-Verlag Berlin Heidelberg, 2006.
[20] W. Liang, J. Hu, L. Zhang, C. Guo, and W. Lin, Assessing and classifying risk of pipeline third-party interference based on fault tree and SOM, Engin. Appl. Artific. Intell. 25 (2012), 594–608.
[21] E.H. Mamdani and S. Assilian, An experiment in linguistic synthesis with a fuzzy logic controller, Int. J. Man-Mach. Stud. 7 (1975), no. 1, 1–13. [22] A.S. Markowski and M.S. Mannan, Fuzzy risk matrix, J. Hazardous Mater. 159 (2008), 152-157.
[23] S.G. Meshram, V.P. Singh, E. Kahya, M. Sepehri, C. Meshram, M.A. Hasan, S. Islam, and P.A. Duc, Assessing erosion prone areas in a watershed using interval rough-analytical hierarchy process (IR-AHP) and fuzzy logic (FL), Stoch. Environ. Res. Risk Assess 36 (2022), 297–312.
[24] O. Milton-Thompson, A.A. Javadi, Z. Kapelan, A.G. Cahill, and L. Welch, Developing a fuzzy logic-based risk assessment for groundwater contamination from well integrity failure during hydraulic fracturing, Sci. Total Envir. 769 (2021), 145051.
[25] K. Mohammadi Atashgah, R. Ghousi, A. Monir Abbasi, and A. Tayefi Nasrabadi, Developing a model for time-cost trade-off optimization problem considering overdraft issue in uncertain environments, J. Ind. Syst. Engin. 14 (2022), no. 3, 259–279.
[26] M. Monjezi and M., Rezae, Developing a new fuzzy model to predict burden from rock geomechanical properties, Expert Syst. Appl. 38(2012), 9266–9273.
[27] W.K. Muhlbauer, Pipeline Risk Management Manual; Ideas, Techniques, and Resources (Third Edition), Elsevier Inc., 2004.
[28] E. Plebankiewicz, K. Zima, and D. Wieczorek, Modelling of time, cost and risk of construction with using fuzzy logic, J. Civil Engin. Manag. 27 (2021), no. 6, 412–426.
[29] T.J. Ross, Fuzzy logic with engineering applications (Third Edition), John Wiley & Sons, Ltd, 2010.
[30] A. Shahriar, R. Sadiq, and S. Tesfamariam, Risk analysis for oil & gas pipelines: A sustainability assessment approach using fuzzy based bow-tie analysis, J. Loss Prevent. Process Ind. 25 (2012), 505-523.
[31] J. Soszynska, Reliability and risk evaluation of a port oil pipeline transportation system in variable operation conditions, Int. J. Pressure Vessels Pip. 87 (2010), 81–87.
[32] M. Xie, Fundamentals of Robotics: Linking Perception to Action, World Scientific Publishing Co Ltd, London, 2003.
[33] A. Yazdani-Chamzini and S.H. Yakhchali, Tunnel Boring Machine (TBM) selection using fuzzy multicriteria decision making methods, Tunnell. Underground Space Technol. 30 (2012), 194–204.
[34] D. Yuhua and Y. Datao, Estimation of failure probability of oil and gas transmission pipelines by fuzzy fault tree analysis, J. Loss Prevent. Process Ind. 18 (2005), 83-88.
[35] A.S. Yuksel and S. Atmaca, Driver’s black box: A system for driver risk assessment using machine learning and fuzzy logic, J. Intell. Transport. Syst. 25 (2021), no. 5, 482–500.
[36] L.A. Zadeh, Fuzzy set, Inf. Control 8 (1965), 338–353.
International Journal of Nonlinear Analysis and Applications
Volume 16, Issue 1
January 2025
Pages 297-306
  • Receive Date: 13 November 2023
  • Revise Date: 23 December 2023
  • Accept Date: 02 January 2024