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

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Sadeghi Moghadam Mohammad Reza; Jafarnejad Ahmad; Heidary Dahooie Jalil; Ghasemian Sahebi Iman

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Journal Paper

Paper Information

Journal: Mathematics Interdisciplinary Research Year:2024 | Volume:9 | Issue:1 Page(s): 89-109

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Title

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

Author(s)

Sadeghi Moghadam Mohammad Reza | Jafarnejad Ahmad | Heidary Dahooie Jalil | Ghasemian Sahebi Iman | Issue Writer Certificate

Keywords

Demand forecasting‎

‎Emergency relief material‎

‎Case-based reasoning‎

‎Hidden Markov model‎

Abstract

‎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.

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