A Hidden‎ Markov Model‎ ‎Based‎ ‎Extended Case-Based Reasoning Algorithm for Relief Materials Demand Forecasting

Document Type : Original Scientific Paper


Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran



‎In emergency situations‎, ‎accurate demand forecasting for relief materials such as food‎, ‎water‎, ‎and medicine is crucial for effective disaster response‎. ‎This research is presented a novel algorithm to demand forecasting for relief materials using extended Case-Based Reasoning (CBR) with the best-worst method (BWM) and Hidden Markov Models (HMMs)‎. ‎The proposed algorithm involves training an HMM on historical data to obtain a set of state sequences representing the temporal fluctuations in demand for different relief materials‎. ‎When a new disaster occurs‎, ‎the algorithm first determines the current state sequence using the available data and searches the case library for past disasters with similar state sequences‎. ‎The effectiveness of the proposed algorithm is demonstrated through experiments on real-world disaster data of Iran‎. ‎Based on the results‎, ‎the forecasting error index for four relief materials is less than 10\%; therefore‎, ‎the proposed CBR-BWM-HMM is a strong and robust algorithm‎.


Main Subjects

[1] I. G. Sahebi, B. Masoomi and S. Ghorbani, Expert oriented approach for analyzing the blockchain adoption barriers in humanitarian supply chain, Technol. Soc. 63 (2020) p. 101427, https://doi.org/10.1016/j.techsoc.2020.101427.
[2] J. H. Park, B. Kazaz and S.Webster, Surface vs. air shipment of humanitarian goods under demand uncertainty, Prod. Oper. Manag. 27 (2018) 928 - 948, https://doi.org/10.1111/poms.12849.
[3] M. R. Sadeghi Moghadam, I. Ghasemian Sahebi, B. Masoomi, M. Azzavi, A. Anjomshoae, R. Banomyong and P. Ractham, Modeling IoT enablers for humanitarian supply chains coordination, In Li, E.Y. et al. (Eds.) Proceedings
of The International Conference on Electronic Business, 22 (2022) 315-322. ICEB’22, Bangkok, Thailand, October 13-17.
[4] M. R. Sadeghi Moghadam and I. Ghasemian Sahebi, A mathematical model to improve the quality of demand responding in emergency medical centers in a humanitarian supply chain, Mod. Res. Decis. Mak. 3 (2018) 217-242.
[5] R. R. Mili, K. A. Hosseini and Y. O. Izadkhah, Developing a holistic model for earthquake risk assessment and disaster management interventions in urban fabrics, Int. J. Disaster Risk Reduct. 27 (2018) 355 - 365, https://doi.org/10.1016/j.ijdrr.2017.10.022.
[6] S. Basu, S. Roy and S. DasBit, A post-disaster demand forecasting system using principal component regression analysis and case-based reasoning over smartphone-based DTN, IEEE Trans. Eng. Manag. 66 (2019) 224 - 239,
[7] D. Fuqua and S. Hespeler, Commodity demand forecasting using modulated rank reduction for humanitarian logistics planning, Expert Syst. Appl. 206 (2022) p. 117753, https://doi.org/10.1016/j.eswa.2022.117753.
[8] Y. A. Nahleh, A. Kumar and F. Daver, Predicting relief materials’ demand for emergency logistics planning using ARENA input analyzer, Int. J. Eng. Sci. Innov. Technol. 2 (2013) 318 - 327.
[9] A. R. Akkihal, Inventory pre-positioning for humanitarian operations, Master Thesis, Massachusetts Institute of Technology (MIT) (2006).
[10] S. Taskin and E. J. Lodree, Inventory decisions for emergency supplies based on hurricane count predictions, Int. J. Prod. Econ. 126 (2010) 66 - 75, https://doi.org/10.1016/j.ijpe.2009.10.008.
[11] J. B. Sheu, An emergency logistics distribution approach for quick response to urgent relief demand in disasters, Transp. Res. E Logist. Transp. Rev. 43 (2007) 687 - 709, https://doi.org/10.1016/j.tre.2006.04.004.
[12] L. Fei and Y. Wang, Demand prediction of emergency materials using casebased reasoning extended by the Dempster-Shafer theory, Socio-Econ. Plan. Sci. 84 (2022) p. 101386, https://doi.org/10.1016/j.seps.2022.101386.
[13] J. Shao, C. Liang, Y. Liu, J. Xu and S. Zhao, Relief demand forecasting based on intuitionistic fuzzy case-based reasoning, Socio-Econ. Plan. Sci. 74 (2021) p. 100932, https://doi.org/10.1016/j.seps.2020.100932.
[14] A. Mohaghar, I. G. Sahebi and A. Arab, Appraisal of humanitarian supply chain risks using Best-Worst method, Int. J. Soc. Behav. Educ. Econ. Bus. Ind. Eng. 11 (2017) 349 - 354.
[15] J. E. Cox Jr and D. G. Loomis, Improving forecasting through textbooks—A 25 year review, Int. J. Forecast. 22 (2006) 617 - 624, https://doi.org/10.1016/j.ijforecast.2005.12.004.
[16] F. Deqiang, L. Yun and L. Changbing, Forecasting the demand of emergency supplies: based on the CBR theory and BP neural network, in Proc. Int. Conf. Innov. Manage. 45 (2011) 700 - 704.
[17] I. G. Sahebi and A. Jafarnejad, Demand forecasting of emergency resource in humanitarian supply chain, Proceedings of the 103rd IRES International Conference, Zurich, Switzerland (2018) 129 - 136.
[18] J. Zhao and C. Cao, Review of relief demand forecasting problem in emergency logistic System, 8 (2015) 92 - 98, https://doi.org/10.4236/jssm.2015.81011.
[19] F. Zhiyan and C. Jian, Research on emergency material demand forecast model in disaster, Logistics Sci-Tech 10 (2009) 11 - 13.
[20] C. Deb, F. Zhang, J. Yang, S. E. Lee and K. W. Shah, A review on time series forecasting techniques for building energy consumption, Renew. Sustain. Energy Rev. 74 (2017) 902-924, https://doi.org/10.1016/j.rser.2017.02.085.
[21] M. Ozen and A. Krishnamurthy, Evaluating relief center designs for disaster relief distribution, J. Humanit. Logist. Supply Chain Manag. 8 (2018) 22-48, https://doi.org/10.1108/JHLSCM-03-2017-0012.
[22] S. S. Jones, R. S. Evans, T. L. Allen, A. Thomas, P. J. Haug, S. J. Welch and G. L. Snow, A multivariate time series approach to modeling and forecasting demand in the emergency department, J. Biomed. Inform. 42 (2009) 123 -139, https://doi.org/10.1016/j.jbi.2008.05.003.
[23] X. Xu, Y. Qi and Z. Hua, Forecasting demand of commodities after natural disasters, Expert Syst. Appl. 37 (2010) 4313 - 4317, https://doi.org/10.1016/j.eswa.2009.11.069.
[24] J.-B. Sheu, An emergency logistics distribution approach for quick response to urgent relief demand in disasters, Transp. Res. E Logist. Transp. 43 (2007) 687 - 709, https://doi.org/10.1016/j.tre.2006.04.004.
[25] B. Sun, W. Ma and H. Zhao, A fuzzy rough set approach to emergency material demand prediction over two universes, Appl. Math. Model. 37 (2013) 7062 - 7070, https://doi.org/10.1016/j.apm.2013.02.008.
[26] S. Wu, Y. Ru and H. Li, A study on inventory management method in emergency logistics based on natural disasters, 2010 Int. Conf. E-Product E-Service E-Entertainment, ICEEE2010 (2010) 1 - 4, https://doi.org/10.1109/ICEEE.2010.5661049.
[27] M. Lou Maher, CBR as a framework for design: augmenting CBR with other AI techniques, Case-Based Reason. Integr. (1998) 96 - 101.
[28] M. Relich and P. Pawlewski, A case-based reasoning approach to cost estimation of new product development, Neurocomputing 272 (2018) 40 - 45, https://doi.org/10.1016/j.neucom.2017.05.092.
[29] J. Rezaei, Best-worst multi-criteria decision-making method, Omega 53 (2015) 49 - 57, https://doi.org/10.1016/j.omega.2014.11.009.
[30] F. Yu, X.-Y. Li and X.-S. Han, Risk response for urban water supply network using case-based reasoning during a natural disaster, Saf. Sci. 106 (2018) 121 - 139, https://doi.org/10.1016/j.ssci.2018.03.003.
[31] L. K. de Godoy Tominaga, V. W. B. Martins, I. S. Rampasso, R. Anholon, D. Silva, J. S. Pinto, W. Leal Filho and F. R. Lima Junior, Critical analysis of engineering education focused on sustainability in supply chain management:
an overview of Brazilian higher education institutions, Int. J. Sustain. High. Educ. 22 (2021) 380 - 403, https://doi.org/10.1108/ijshe-01-2020-0002.
[32] A. Aamodt and E. Plaza, Case-based reasoning: foundational issues, methodological variations, and system approaches, AI Commun. 7 (1994) 39 - 59, https://doi.org/10.3233/AIC-1994-7104.
[33] W. Liu, G. Hu and J. Li, Emergency resources demand prediction using case-based reasoning, Saf. Sci. 50 (2012) 530 - 534, https://doi.org/10.1016/j.ssci.2011.11.007.
[34] J. Rezaei, Best-Worst multi-criteria decision-making method: some properties and a linear model, Omega 64 (2016) 126 - 130, https://doi.org/10.1016/j.omega.2015.12.001.
[35] S. R. Eddy, Profile hidden Markov models., Bioinformatics 14 (1998) 755 -763, https://doi.org/10.1093/bioinformatics/14.9.755.
[36] L. R. Rabiner, A tutorial on hidden Markov models and selected applications in speech recognition, Proc. IEEE 77 (1989) 257 - 286, https://doi.org/10.1109/5.18626.
[37] I. Ghasemian Sahebi, Needs Assessment and Demand Forecasting in the Lifecycle of Disaster in the Humanitarian Supply Chain, Master Thesis, University of Tehran, Iran, 2015.
[38] A. A. Zahari and J. Jaafar, Hybridization of hidden Markov model and case based reasoning for time series forecasting, Front. Artif. Intell. Appl. 265 (2014) 63 - 74, https://doi.org/10.3233/978-1-61499-434-3-63.
[39] S. P. Toufighi, M. R. Mehregan and A. Jafarnejad, Modeling of production strategies from common offshore gas field with game theory approach, Math. Interdisc. Res. 7 (2022) 21 - 44, https://doi.org/10.22052/MIR.2022.243449.1329.
[40] S. P. Toufighi, M. R. Mehregan and A. Jafarnejad, Optimization of Iran’s production in Forouzan common oil filed based on game theory, Math. Interdisc. Res. 5 (2020) 173 - 192, https://doi.org/10.22052/MIR.2020.238991.1222.