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GMM clustering for in-depth food accessibility pattern exploration and prediction model of food demand behavior

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  • Sucharitha, Rahul Srinivas
  • Lee, Seokcheon

Abstract

Understanding the dynamics of food banks' demand from food insecurity is essential in optimizing operational costs and equitable distribution of food, especially when demand is uncertain. Hence, Gaussian Mixture Model (GMM) clustering is selected to extract patterns. The novelty is that GMM clustering is applied to identify the possible causes of food insecurity in a given region, understanding the characteristics and structure of the food assistance network in a particular region, and the clustering result is further utilized to explore the patterns of uncertain food demand behavior and its significant importance in inventory management and redistribution of surplus food thereby developing a two-stage hybrid food demand estimation model. Data obtained from a food bank network in Cleveland, Ohio, is used, and the clusters developed are studied and visualized. The results reveal that this proposed framework can make an in-depth identification of food accessibility and assistance patterns and provides better prediction accuracies of the leveraged statistical and machine learning algorithms by utilizing the GMM clustering results. Also, implementing the proposed framework for case studies based on different levels of planning led to practical results with remarkable ease and comfort intended for the respective planning team.

Suggested Citation

  • Sucharitha, Rahul Srinivas & Lee, Seokcheon, 2022. "GMM clustering for in-depth food accessibility pattern exploration and prediction model of food demand behavior," Socio-Economic Planning Sciences, Elsevier, vol. 83(C).
    • Handle: RePEc:eee:soceps:v:83:y:2022:i:c:s0038012122001410
    DOI: 10.1016/j.seps.2022.101351
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    References listed on IDEAS

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    2. Tanzid Hasnain & Irem Sengul Orgut & Julie Simmons Ivy, 2021. "Elicitation of Preference among Multiple Criteria in Food Distribution by Food Banks," Production and Operations Management, Production and Operations Management Society, vol. 30(12), pages 4475-4500, December.
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    1. Akkerman, Renzo & Buisman, Marjolein & Cruijssen, Frans & de Leeuw, Sander & Haijema, Rene, 2023. "Dealing with donations: Supply chain management challenges for food banks," International Journal of Production Economics, Elsevier, vol. 262(C).

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