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A comprehensive model of demand prediction based on hybrid artificial intelligence and metaheuristic algorithms: A case study in dairy industry

Author

Listed:
  • Goli, Alireza
  • Zare, Hasan Khademi
  • Moghaddam, RezaTavakkoli
  • Sadeghieh, Ahmad

Abstract

This paper presents a multi-stage model for accurate prediction of demand for dairy products (DDP) by the use of artificial intelligence tools including Multi- Layer Perceptron (MLP), Adaptive-Neural-based Fuzzy Inference System (ANFIS), and Support Vector Regression (SVR). The innovation of this work is the improvement of artificial intelligence tools with various meta-heuristic algorithms including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Invasive Weed Optimization (IWO), and Cultural Algorithm (CA). First, the best combination of factors with can affect the DDP is determined by solving a feature selection optimization problem. Then, the artificial intelligent tools are improved with the goal of making a prediction with minimal error. The results indicate that demographic behavior and inflation rate have the greatest impact on dairy consumption in Iran. Moreover, PSO still exhibits a better performance in feature selection in compare of newcomer meta-heuristic algorithms such as IWO and CA. However, IWO shows the best performance in improving the prediction tools by achieving an error of 0.008 and a coefficient of determination of 95%. The final analysis demonstrates the validity and reliability of the results of the proposed model, as it supports the simultaneous analysis and comparison of the outputs of different tools and methods.

Suggested Citation

  • Goli, Alireza & Zare, Hasan Khademi & Moghaddam, RezaTavakkoli & Sadeghieh, Ahmad, 2018. "A comprehensive model of demand prediction based on hybrid artificial intelligence and metaheuristic algorithms: A case study in dairy industry," MPRA Paper 101727, University Library of Munich, Germany, revised 15 Apr 2018.
    • Handle: RePEc:pra:mprapa:101727
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    References listed on IDEAS

    as
    1. Hartzel, Kathleen S. & Wood, Charles A., 2017. "Factors that affect the improvement of demand forecast accuracy through point-of-sale reporting," European Journal of Operational Research, Elsevier, vol. 260(1), pages 171-182.
    2. Atul Anand & L Suganthi, 2018. "Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand," Energies, MDPI, vol. 11(4), pages 1-15, March.
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    Cited by:

    1. Mehmet Onur Olgun, 2022. "Collaborative airline revenue sharing game with grey demand data," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 30(3), pages 861-882, September.
    2. He-Boong Kwon & Jooh Lee & Laee Choi, 2023. "Dynamic interplay of environmental sustainability and corporate reputation: a combined parametric and nonparametric approach," Annals of Operations Research, Springer, vol. 324(1), pages 687-719, May.

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    More about this item

    Keywords

    Multi-layer perceptron; adaptive-neural-based fuzzy inference system; support vector regression; invasive weed optimization algorithm; cultural algorithm; feature selection;
    All these keywords.

    JEL classification:

    • L20 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - General
    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights
    • Z00 - Other Special Topics - - General - - - General

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