Implementation Of Pricing Strategy and Replenishment Strategy for Fresh Products Based on Particle Swarm Optimization Algorithm

Qing Wang; Haoxiang Wang

doi:10.54097/qf21mk69

Authors

Qing Wang
Haoxiang Wang

DOI:

https://doi.org/10.54097/qf21mk69

Keywords:

Dynamic Time Planning, Particle Swarm Optimization Algorithm (PSO), ARIMA Model.

Abstract

Vegetable pricing and replenishment strategies are crucial issues in the retail industry. Globally, as populations grow and market competition intensifies, research into how to determine prices and optimize replenishment strategies is increasingly critical. In this article, we focus on pricing and replenishment strategies for fresh vegetables. First, this paper uses the FP-growth algorithm to calculate multiple vegetable combinations that are often purchased together. Then, to explore the relationship between sales volume and cost-based mark-up pricing, we used the ARIMA model to predict and then used linear regression to derive a fitting regression equation for the relationship between overall sales and price. Finally, the particle swarm optimization algorithm is employed to maximize total profit through optimal price and replenishment strategies. Based on this result, the final pricing strategy is formulated and the corresponding replenishment strategy is formulated. The significance of our research on this project is to improve the accuracy and efficiency of price and replenishment decisions through optimization algorithms, thereby maximizing profits and improving the effectiveness of supply chain management. This research contributes to the ongoing development of data-driven decision-making models in the retail industry, particularly in the area of perishable goods management.

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References

[1] Li H, Liu J, Qiu J, et al. ARIMA-driven vegetable pricing and restocking strategy for dual optimization of freshness and profitability in supermarket perishables[J]. Sustainability, 2024, 16(10): 4071.

[2] Keswani M, Khedlekar U. Optimizing pricing and promotions for sustained profitability in declining markets: A Green-Centric inventory model[J]. Data Science in Finance and Economics, 2024, 4(1): 83-131.

[3] Boone T, Ganeshan R, Jain A, et al. Forecasting sales in the supply chain: Consumer analytics in the big data era[J]. International journal of forecasting, 2019, 35(1): 170-180.

[4] Keswani M, Khedlekar U K. A fuzzy stochastic model incorporating advance sales, discounts, and carbon emission factors with comparative analysis of tuned meta-heuristic algorithms[J]. Soft Computing, 2024: 1-37.

[5] Sinha A. Implying Association Rule Mining and Market Basket Analysis for Knowing Consumer Behavior and Buying Pattern in Lockdown-A Data Mining Approach[J]. 2021.

[6] Mohammadi Z, Barzinpour F, Teimoury E. A location-inventory model for the sustainable supply chain of perishable products based on pricing and replenishment decisions: A case study[J]. PloS one, 2023, 18(7): e0288915.

[7] Gad A G. Particle swarm optimization algorithm and its applications: a systematic review[J]. Archives of computational methods in engineering, 2022, 29(5): 2531-2561.

[8] Long L N B, Kim H S, Cuong T N, et al. Intelligent decision support system for optimizing inventory management under stochastic events[J]. Applied Intelligence, 2023, 53(20): 23675-23697.

[9] Ping H, Li Z, Shen X, et al. Optimization of vegetable restocking and pricing strategies for innovating supermarket operations utilizing a combination of ARIMA, LSTM, and FP-Growth algorithms[J]. Mathematics, 2024, 12(7): 1054.

[10] Tort Ö Ö, Vayvay Ö, Çobanoğlu E. A systematic review of sustainable fresh fruit and vegetable supply chains[J]. Sustainability, 2022, 14(3): 1573.