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Study on Supermarket Replenishment and Pricing Based on Time Series Forecasting Algorithm and Nonlinear Multi-Objective Optimization Model

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DOI: 10.23977/ferm.2024.070318 | Downloads: 11 | Views: 338

Author(s)

Lige Deng 1

Affiliation(s)

1 School of Mathematics and Information Sciences, Guangzhou University, Guangzhou, China

Corresponding Author

Lige Deng

ABSTRACT

This study explores the correlation between the total sales volume of various vegetable categories and cost-plus pricing in supermarkets. It encompasses data processing for weekly totals across six categories, establishing the relationship between sales volume and pricing, predicting future replenishment volume and pricing strategies using a time series forecasting algorithm and nonlinear multi-objective optimization models, and integrating constraints to maximize total profits. The research aims to optimize pricing and replenishment decisions, offering insights for improving profitability and resource utilization in the retail industry.

KEYWORDS

Time series forecasting algorithm, Nonlinear multi-objective optimization model, Pricing and replenishment decision

CITE THIS PAPER

Lige Deng, Study on Supermarket Replenishment and Pricing Based on Time Series Forecasting Algorithm and Nonlinear Multi-Objective Optimization Model. Financial Engineering and Risk Management (2024) Vol. 7: 142-148. DOI: http://dx.doi.org/10.23977/ferm.2024.070318.

REFERENCES

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[4] Zorbas, C. et al. The frequency and magnitude of price‐promoted beverages available for sale in Australian supermarkets. Australian and New Zealand Journal of Public Health, (2019) 43, 346–351.
[5] Stuber, J. M., Hoenink, J. C., Beulens, J. W., Mackenbach, J. D. & Lakerveld, J. Shifting toward a healthier dietary pattern through nudging and pricing strategies: A secondary analysis of a randomized virtual supermarket experiment. The American Journal of Clinical Nutrition, (2021) 114, 628–637. 

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