Optimization Decision Model for Vegetable Stock and Pricing Using TCN-Attention and Genetic Algorithm

Core Concepts

This paper utilizes TCN-Attention and genetic algorithms to optimize vegetable stock and pricing decisions in supermarkets, aiming to enhance profitability through data-driven strategies.

Abstract

The paper addresses the challenges faced by supermarkets in determining procurement costs and distribution quantities of vegetables. It employs advanced technologies like TCN-Attention and genetic algorithms to optimize pricing and allocation strategies. By integrating historical data with time information based on solar terms, a cost prediction model is developed. The study also uses TOPSIS evaluation to identify market-demanded products for optimal allocation-pricing decisions. Furthermore, a genetic algorithm is applied to maximize profits under specific constraints.

Stats

Linear regression is utilized for modeling historical data of 61 products. The hierarchical one-hot encoding method reduces the dimension of solar term information training data by 53.8%. Each window for forecasting encompasses 15 days of data. The TCN-Attention feature fusion model outperforms LSTM, RNN, and TCN in MSE, MAE, and RMSE indicators.

Quotes

"An appealing pricing strategy can boost consumer footfall." "Advancements in big data offer more precise tools for tackling market dynamics." "The attention mechanism enhances prediction accuracy in pricing strategies."

Key Insights Distilled From

Optimization decision model of vegetable stock and pricing based on TCN-Attention and genetic algorithm

by Linhan Xia,J... at arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.01367.pdf

Deeper Inquiries

How can traditional strategies be improved upon using advanced technologies like TCN-Attention?

Traditional strategies in pricing and distribution often rely on experience or oversimplified forecasting models, which may not accurately capture the complex dynamics of the market. By incorporating advanced technologies like Temporal Convolutional Neural Network (TCN)-Attention, these limitations can be overcome. Improved Accuracy: TCN-Attention offers a fresh perspective on time series analysis by effectively capturing long-term dependencies across different time scales. Unlike traditional methods, TCN employs convolutional structures that enhance accuracy in predicting future trends. Enhanced Feature Fusion: The attention mechanism in TCN-Attention allows for dynamic adjustment and better interpretability of data features crucial for decision-making processes. This helps in focusing on key information relevant to pricing decisions, leading to more accurate predictions. Incorporation of Seasonal Factors: By integrating seasonal factors such as solar terms into the prediction model, TCN-Attention provides a more comprehensive understanding of how external factors influence vegetable costs over time. This leads to more informed decision-making based on historical patterns and seasonal variations. Optimized Decision Support: With the use of TCN-Attention, traditional strategies can be enhanced with precise cost forecasting models that consider both historical data and temporal information from solar terms. This optimization enables supermarkets to make data-driven decisions regarding pricing and allocation strategies for vegetable products.

What are the potential limitations of relying solely on statistical forecasting methods?

While statistical forecasting methods have been widely used in various industries including supply chain management and retail research, they come with certain limitations that may hinder their effectiveness: Limited Adaptability: Statistical methods often assume linear relationships between variables based on historical data patterns. However, they may struggle to adapt to sudden changes or disruptions in the market environment that deviate from past trends. Inability to Capture Complex Patterns: Statistical models like moving averages or exponential smoothing might overlook intricate market dynamics due to their simplistic calculations based solely on historical data points without considering external factors or seasonality adequately. 3 .Lack of Long-Term Predictive Power: Traditional statistical methods may lack long-term predictive power when dealing with extensive datasets or complex time series patterns where deep learning algorithms like recurrent neural networks (RNNs) or LSTM networks could perform better. 4 .Difficulty Handling Nonlinear Relationships: Statistical techniques might struggle with capturing nonlinear relationships within datasets efficiently compared to machine learning algorithms capable of handling complex interactions among variables.

How might the integration of solar terms impact decision-making processes beyond vegetable pricing?

The integration of solar terms into decision-making processes extends beyond just vegetable pricing by influencing various aspects related to procurement costs, sales volumes, and overall profitability: 1 .Seasonal Demand Forecasting: Solar terms provide insights into seasonal variations affecting consumer behavior and demand patterns throughout the year beyond just vegetables but also other products sold at supermarkets. 2 .Supply Chain Management: Understanding how solar terms impact agricultural cycles can help optimize supply chain operations by aligning procurement schedules with peak harvest seasons influenced by specific solar terms. 3 .Marketing Strategies: Leveraging knowledge about solar term influences allows supermarkets to tailor marketing campaigns around cultural events tied to these periods enhancing customer engagement. 4 .Operational Efficiency: Solar term integration aids in optimizing inventory management practices ensuring adequate stock levels during high-demand periods associated with specific times determined by these traditional Chinese calendar divisions. 5 .Strategic Planning: Incorporating solar term insights into decision-making enables supermarkets not only predict short-term fluctuations but also plan long-term growth strategies aligned with cultural traditions impacting consumer preferences over extended periods.