Vegetable Items Research Articles

The Influence of Radiofrequency Heating on the Quality and Shelf of Life of Pepper

Pepper is one of the common vegetable food items that constitute most African dishes with beneficial health properties. This study investigates the influence of radiofrequency (RF) heating on the quality and shelf life of pepper. Utilising the measured dielectric properties of pepper, the research evaluates how microwave exposure at varying frequencies affects pepper's nutritional content and preservation duration. Fresh samples of pepper were subjected to radio waves ranging from 5 MHz to 25 MHz, and their dielectric properties, proximate composition, and shelf life were analysed. The dielectric properties were measured using a parallel plate capacitance method, revealing significant variations in conductivity, dielectric constant, and loss factor across different frequencies. Results demonstrated that RF treatment effectively extended the shelf life of pepper, with the highest frequency (25 MHz) achieving a shelf life of 36 days, compared to 20 days for the control. Moreover, the study observed minimal changes in the proximate composition of pepper, ensuring the retention of its nutritional quality. This research provides valuable insights into the use of RF heating as a viable technique for extending the shelf life of pepper while maintaining its nutritional integrity.

Automated pricing and replenishment forecasting for vegetable items

Firstly, the thesis extensively analysed and processed the vegetable sales data of a fresh food superstore. It integrated the product information and sales data of each vegetable category, and carried out data cleaning and analysis through Excel tools, including sorting out the outliers, listing the sales volume of each of the six categories, using pivot tables for statistical analysis, and establishing line graphs, heat maps, scatter plots, and other visual displays of the data characteristics and correlations. Secondly, the thesis investigated the relationship between sales volume and pricing, fitted the correlation between total sales volume and pricing using a random forest regression model, and predicted the daily replenishment volume for the coming week using an LSTM time series model. It also fitted quantitative fitting formulas for metrics for each vegetable category, classified profits into normal sales volume profits and discounted sales volume profits, and ultimately maximised profits through dynamic programming models and particle swarm optimisation algorithms. Finally, the thesis considered the impact of other factors such as weather, holidays, seasons and market environment on sales volume with specific data collection and analyses that help guide better pricing strategies. The findings will contribute to a better understanding of vegetable sales behaviour and optimise superstore operational strategies.

Research on Vegetable Pricing and Replenishment Model Based on Greedy Algorithm

This article analyzes the pricing and replenishment decisions of supermarkets, exploring the distribution patterns and correlations between different vegetable categories and individual products. On the other hand, a robust linear programming model is constructed, which can iteratively optimize the corresponding replenishment plan, significantly improving the profitability of supermarkets. This provides important practical significance for the operation and sales of vegetable products in fresh food supermarkets. Through Spearman correlation coefficient analysis, it was found that there is a close relationship between cauliflower and leafy vegetables. The correlation between various vegetable items was processed using K-means clustering method, and the correlation between each item was discussed after classifying the groups. Finally, the grey correlation analysis method was used to further explore the ranking of the correlation between each category and individual product.

Research on Pricing and Replenishment of Vegetables Based on Grey Relational Model and Seasonal Index Prediction Model

This article mainly studies the problem of automatic pricing and replenishment decisions for vegetable products in supermarkets. A grey correlation model and ARIMA time series model are comprehensively established, and based on clustering analysis and regression analysis methods, the distribution patterns of sales volume for each vegetable category and item over time, as well as the relationship with cost plus pricing and mutual correlation, are analyzed to make reasonable predictions for future sales volume. In order to make decisions on product pricing, sales combination, and replenishment, and maximize supermarket profits, the data is preprocessed through normal test and quartile method. Through screening and sorting analysis, the distribution patterns of sales volume for each vegetable category and item over time are obtained; In order to analyze the relationship between different categories of vegetables and between different single products, a grey correlation model is established to determine the analysis sequence, and the correlation coefficients between each category and each single product are calculated to analyze the mutual relationship. And combined with the hierarchical clustering analysis method, the vegetable categories were divided into three sales types: high, medium, and low, expanding the relationship between categories. At the same time, a periodic chart was drawn to determine the periodicity of sales volume distribution in each category. Then, statistical analysis was conducted on the data to determine the linear correlation between sales volume and cost plus pricing. Based on this, a linear regression model was established to analyze the linear relationship between sales volume and cost plus pricing for the six categories; In order to provide the total daily replenishment amount and pricing strategy for the next week, this article establishes a seasonal index prediction model for sales forecasting. Combining the seasonal distribution of each category, the seasonal index is used to predict the sales volume for the next week. Considering the goal of maximizing profits, the target function is listed to comprehensively analyze the replenishment and pricing strategy for the next week.

Data-Driven Approach to Enhancing Supermarket Vegetable Sales Strategies

China's vegetable commodity production has developed rapidly and made great progress. Due to the short shelf life of most vegetable products, they cannot be sold the next day. Therefore, by studying the distribution law of sales volume and the relationship between categories and pricing, the supermarket can accurately predict the sales situation, reasonably arrange replenishment plans, and formulate pricing strategies. Comprehensive analysis of automatic replenishment and pricing strategies of vegetable commodities is of great significance to the profit of fresh supermarkets. Aiming at the automatic pricing and replenishment strategies of vegetable products, this paper visualizes each item and each category to obtain the distribution and relationship of time, sales volume, and sales frequency of different items and categories. Spearman grade correlation coefficient is used for correlation analysis, and a single linear regression model is established. The relationship between cost profit rate and cost, selling price, and total sales volume of each category is obtained. Then, the ARIMA prediction model is used to predict the automatic pricing and replenishment strategies for each category and each vegetable item during the week of July 1-7, 2023, and July 1, 2023.

Vegetable Automated Pricing Model Based on Particle Swarm Algorithm

Fresh grocery supermarkets face challenges such as the short shelf life of vegetables, quality degradation over time, and difficulty in selling products the following day. Considering these issues, they restock early in the morning without complete knowledge of the specific items or purchase prices, based on historical sales and demand data. To address this issue, this paper first visualizes the data to demonstrate a positive correlation between sales volume and time. Next, to address the issue of predicting the total daily restocking quantity and pricing strategy from July 1 to July 7, 2023, the document utilizes data from the previous three years to establish an ARIMA time series forecast model to predict the costs and sales volume of six vegetables for the first week of July 2023. Finally, to devise the replenishment quantity and pricing strategy for individual items on July 1, 2023, the paper filters the data for all vegetable items from June 24th to 30th, 2023, and combines it with the particle swarm optimization algorithm to obtain the results. This study is pragmatic, providing a feasible solution to the problem of automatic pricing for fresh vegetables.

Research on the correlation and distribution law of commodities

In the fresh supermarket, the supermarket will generally make replenishment plans according to the historical sales and demand of each commodity. There is a certain correlation between the sales volume of different commodities. It is crucial to deeply understand the distribution law and correlation relationship of commodity sales to optimize the replenishment strategy. In this paper, Spearman correlation coefficient analysis and entropy weight method-TOPSIS model are constructed to analyze the distribution law and correlation relationship of vegetable products and single product sales volume. First of all, this paper aggregates the sales volume of different vegetable categories and single items by month to get the average monthly sales volume. The average monthly sales data of each vegetable category was visualized, and the distribution pattern of each vegetable category was analyzed. The relation of vegetable categories was analyzed using Spearman's correlation coefficient. Then, for vegetable items, the TOPSIS model based on entropy weight method was constructed, and the 8 most representative items were found among 251 vegetable items. Finally, the average monthly sales data of the selected vegetable products was visualized, the distribution pattern of the sales of each vegetable product was analyzed and the correlation coefficient of Spearman was used to analyze the sales of different vegetables.

Optimisation of superstore single product replenishment and pricing strategy based on LSTM neural network and weighted multi-objective planning

Vegetable items in supermarkets usually have a short shelf life and their quality gradually deteriorates over time. Therefore, supermarkets usually need to make daily vegetable replenishment decisions based on historical sales data and demand. The purpose of this paper is to investigate the replenishment strategy and pricing strategy of individual items under many constraints in order to reach the goal of maximising supermarket revenue and market demand. Firstly, the original sales volume data is preprocessed to calculate the sales volume of each single item within 24-30 June, excluding the single item with 0 sales volume, the remaining 49 single items; secondly, the LSTM neural network model is established to predict the non-discounted sales volume, the discounted sales volume, and the cost of each single item on 1 July, respectively, and excluding the single item with total sales volume lower than 2.5 kg on 1, the remaining number of saleable single items is 32; finally, considering the three possibilities of the existing supply and demand relationship, a weighted multi-objective planning model is established to solve for each single item separately, and the optimal solution is selected from it. The results show that the maximum profit of the superstore on 1 July is 886.56 yuan.

Vegetable Merchandise Analysis from a Quarterly Sales Data Perspective

In the field of fresh vegetable retailing, the category diversity and time-sensitive characteristics of commodities pose unique challenges to inventory management and pricing decisions. Comprehensively analyzing the distribution pattern of sales volume of different categories and individual items of vegetable commodities and their interrelationships is of great significance to retailers' scientific replenishment and pricing decisions. By categorizing vegetable sales every quarter, a comprehensive analysis is carried out on a category and individual item basis. Visualization was used to show the distribution pattern of sales volume by category and individual item. Subsequently, Kendall correlation analysis was utilized to explore the relationship between the sales volume of different vegetable categories. In addition, the interrelationships between the sales volumes of different vegetable individual items were investigated in conjunction with waterfall diagrams. Through in-depth analysis of sales data, understanding the sales relationship between different vegetable categories can help retailers develop a more refined pricing strategy, make pricing adjustments based on the relative sales of goods, and improve sales revenue and profit levels. Such scientific inventory management and pricing strategies not only help meet customer demand and enhance customer experience, but also strengthen customer loyalty and promote long-term stable business development.

Research on the Optimization of Pricing and the Replenishment Decision-Making Problem Based on LightGBM and Dynamic Programming

Vegetables have a short period of freshness, and therefore, the purchase of vegetables has to be carefully matched with sales, especially in the “small production and big market” setting prevalent in China. Therefore, it is worthwhile to develop a systematic and comprehensive mathematical model of replenishment plans and pricing strategies for each category of vegetables and individual products. In this paper, we analyze the following three questions: Question One: What is the distribution law and relationship between the sales volume of vegetable categories and single products? Question Two: What is the relationship between total sales volume and cost-plus pricing of vegetable categories? And is it possible to provide the daily total replenishment and pricing strategy of each vegetable category for the following week to maximize supermarket profit? Question Three: How can we incorporate the market demand for single vegetable products into a profit-maximizing program for supermarkets? Is it possible to further formulate the replenishment plan requirements for single products? To answer the first question, we created pivot tables to analyze occupancy. We found that mosaic leaves, peppers, and edible mushrooms accounted for a larger proportion of occupacy, while cauliflowers, aquatic rhizomes, and eggplants accounted for a smaller proportion. For the single items, lettuce, cabbage, green pepper, screw pepper, enoki mushroom, and shiitake mushroom accounted for a large proportion of their respective categories. We used the Pearson correlation coefficient and the Mfuzz package based on fuzzy c-means (FCM) algorithm to analyze the correlation between vegetable categories and single products. We found that there was a strong correlation between vegetable categories. Moreover, the sale of vegetable items belonging to the same category exhibited the same patterns of change over time. In order to address the second question, we established the LightGBM sales forecasting model. Combined with previous sales data, we forecasted and planned an efficient daily replenishment volume for each vegetable category in the coming week. In addition, we developed a pricing strategy for vegetable categories to maximize supermarket profits. For the third question, we built a dynamic programming model combining an optimal replenishment volume with a product pricing strategy for single items, which let the supermarket maximize its expected profits.

Examining and Comparing the Validity and Reproducibility of Scales to Determine the Variety of Vegetables Consumed: Validation Study.

Previous studies have reported that vegetable variety reduces the risk for noncommunicable diseases independent of the amount consumed. This study aimed to examine and compare the validity and reproducibility of several scales to determine vegetable variety. In total, 23 nutrition students in Japan reported their vegetable intake over the past month using a self-administered questionnaire between July and August 2021. Specifically, four scales were used: (1) a single question regarding the number of vegetables consumed (scale A); (2) a scale containing 9 vegetable subgroups included in the brief-type self-administered diet history questionnaire (scale B); (3) a scale containing 19 vegetable items included in a self-administered diet history questionnaire (scale C); and (4) a scale containing 20 vegetable items from the Ranking of Vegetable Consumers in Japan, which was analyzed based on a report on the National Health and Nutrition Survey in Japan (scale D). Scale validity was assessed by correlation with the number of vegetables consumed, which was collected from dietary records for 7 consecutive days. Reproducibility was assessed by test-retest reliability. Regarding the validity of the 4 scales, significant correlations were found between scales C (ρ=0.51) and D (ρ=0.44) with vegetable variety based on dietary records, but scales A (ρ=0.28) and B (ρ=0.22) were not significantly correlated. Reproducibility showed a significant correlation in scale B (ρ=0.45) and strong correlations in scales C (ρ=0.73) and D (ρ=0.75). The scales for vegetable items have acceptable validity and reproducibility compared to the scales that used a single question or vegetable subgroup and, therefore, may determine the variety of vegetables consumed.

Research on Vegetable Sales Data Analysis and Cost Pricing Modeling

Vegetable commodities have a shorter freshness period, many varieties, and a special time of purchase, so it is important to comprehensively analyze the automatic replenishment and pricing strategy of vegetable commodities for the profitability of fresh food superstores. First of all, in the correlation of vegetable categories and single product sales volume, Pearson correlation coefficient analysis is used, to obtain the strong correlation between chili peppers and foliage categories and various vegetable categories. Then based on correlation the best clustering results were obtained by K-means clustering analysis divided into three main categories, namely solanula; flowers and leaves and cauliflower; peppers, aquatic rhizomes, and edible fungi. Next using entropy weights TOPSIS method is used to give the weights of four indicators: total sales, average unit price, average wholesale price, and average wastage rate of each category of vegetable commodities for seven days, and the top 27 vegetable items are sorted to get the best sellable vegetables. Based on the above ranking of the individual vegetable items, Excel was used to forecast the cost pricing of the vegetable commodities to get the cost pricing of the 27 individual vegetable items for the next seven days.

Research on Vegetable Pricing and Replenishment Strategies Based on ARMA Models

This paper explores the importance of pricing and replenishment issues of vegetable items for merchant operations. First, data related to vegetable stocking were systematically collected to assess the correlation between different categories. Through Q-Q plot analysis, it was determined that the data did not conform to a normal distribution, so the Spearman correlation coefficient was chosen for further quantitative analysis. Considering the large number of individual vegetable categories, in order to deal with the correlation between different individual items, a hierarchical clustering method was introduced in this paper, which classified them into three categories: low volume, medium volume and high volume. In order to predict the future vegetable intake, this study adopted the seasonal ARMA model and applied the Pearson's correlation coefficient for correlation analysis by combining the cost utilization rate as well as the total sales volume. In addition, the relationship between sales and cost margin was fitted with the help of support vector machine. Finally, this study constructs a single optimization model with the objective of maximizing the revenue of the superstore. The innovation of this paper is the combination of various statistical and machine learning methods in order to solve the pricing and replenishment problem of vegetable items, aiming to improve the efficiency of merchant operations.

Optimization of replenishment and sales strategy of vegetable products based on ARIMA time series model and combination sales

Due to the short shelf life of vegetable items, supermarkets usually restock daily based on the historical sales and demand of each item. In order to develop an optimal strategy for replenishment and sales of vegetable items, this study firstly predicted the total daily replenishment in the coming week using ARIMA time series model, and then this study took the hypermarket revenue as the objective function and considered the correlation between vegetable categories in the sales data, and adopted a combination sales strategy to increase the sales volume. This study establishes the combination sales formula, and uses the third-party library function combined with the genetic algorithm to obtain the optimal coefficient combination to maximize the superstore's revenue, and finally constructs the optimization model of pricing strategy for each category of vegetables, and solves the optimal pricing strategy, and ultimately obtains the daily replenishment of total amount of various categories of vegetables in the coming week.

LR-GA Algorithm Based Study on Vegetable Replenishment and Pricing Decision Making

The sales volume and sales price of vegetable products fluctuate greatly due to factors such as origin, variety and freshness period. Therefore, rational replenishment and pricing decisions are particularly important for supermarkets. Therefore, this paper proposes a replenishment and pricing strategy model for vegetable products based on linear regression-genetic algorithm. In this paper, all vegetable commodities are first classified into four categories using K-mean cluster analysis, and the sales performance of the four categories is observed to understand their sales behaviors and provide references for sales strategies. Then, the cost, price and profit margin of each vegetable commodity are calculated using the weighted method, and the linear regression equation between the sales volume of vegetable commodities and the weighted sales price is given. Finally, using the total profit formula considering the loss rate of each item as the objective function and the linear regression equation between the total sales volume of each vegetable item and the weighted sales price as the constraints, the optimization search is carried out by using linear regression and Genetic Algorithm (LR-GA) to find out the sales price and the sales volume under the maximum profit so as to realize the sales strategy.

Optimizing Vegetable Commodity Pricing and Replenishment: An Integration of ARIMA and Nonlinear Programming Approaches

Automated pricing and replenishment decision-making for vegetable items in supermarkets has attracted the attention of many operators and become a trend, and has become an indispensable part of supermarkets for many operators. In order to develop reliable automatic pricing and replenishment decisions for vegetable products, this paper firstly uses logistic regression analysis to obtain the regression equations of total sales and cost-plus pricing for each vegetable category, and then uses the ARIMA time-series forecasting model to forecast the average wholesale prices of vegetable categories for the next 7 days. After selecting the average wholesale price and pricing-sales volume model for each vegetable category, a nonlinear programming model is constructed to solve the total daily replenishment and pricing strategy for each vegetable category in the coming week. Considering the constraints of limited merchandising categories, this paper introduces three constraints: the number of saleable items, the minimum display volume and the selling space, and estimates the future wholesale price by using the historical average to make a prediction. The optimal replenishment quantity and price are derived by combining the nonlinear programming model and guided by the constraint function.

Research on pricing and replenishment strategy of superstore goods based on linear regression and gray prediction models

The pricing and replenishment strategy of various commodities in superstores have a significant impact on the revenue. This paper aims to address the automatic decision-making problem for pricing and replenishment of vegetable commodities in superstores. It applies the linear regression model to predict commodity prices, aiming to maximize the revenue of superstores. Additionally, it utilizes the gray prediction model to forecast replenishment volume. Based on marketing data from 2020 to 2023 for vegetable goods at a specific superstore, this study provides the replenishment quantity and pricing strategy for each vegetable category from July 1-7, 2023, as well as for each individual vegetable item on July 1, 2023.

Replenishment And Automatic Pricing Strategy for Vegetable Products Based on Genetic Algorithm

In order to improve the accuracy of replenishment and pricing of vegetable commodities sold in supermarkets, and to maximise the benefits of vegetable commodities sold in supermarkets, this paper comprehensively establishes a strategic planning model for replenishment and automatic pricing of supermarkets. Firstly, we carry out descriptive statistical analysis and non-linear fitting on the historical sales data of supermarkets to find the distribution pattern of the sales of each single product of vegetables, and then find out the relationship between each single product of vegetables through correlation analysis. and then find out the relationship between each vegetable item through correlation analysis. After that, we built an LSTM time series forecasting model based on the cost data to predict the replenishment volume of the six vegetable categories in the coming week. Finally, in order to maximise the revenue of the superstore, we constructed a pricing planning model for the combination of vegetable categories, and solved the optimal purchase quantity and pricing strategy based on genetic algorithm.

Optimization Of Replenishment and Sales Strategies of Vegetable Commodities Based on Multiple Linear Regression and Non-Linear Programming

Automated pricing of vegetable items generates suggested selling prices for the items based on the provided item data, considering the structure of the data. The aim of this paper is to better help superstores plan future replenishment volumes and pricing strategies by analysing sales data for each category and individual item in the vegetable category. Specifically, this paper takes the category as a unit, uses the processed cost-plus pricing, sales volume, purchase price, and loss rate data of each vegetable category to establish a linear regression model, and based on the least squares method, the relationship between cost-plus pricing and total sales volume of each vegetable category is fitted to solve the problem, and the multivariate linear relationship expression with high fit is obtained; based on the cost-plus pricing rule, the future replenishment volume and pricing decision is established based on each category. Based on the cost-plus pricing rule, a nonlinear programming model is established based on the total daily replenishment and pricing decision for each category in the coming week, with the revenue of the superstore as the optimization objective, and considering the constraint of the transport loss rate, the total daily replenishment in the coming week under the maximal revenue is solved by using MATLAB to get the corresponding replenishment strategy, and then the total daily replenishment is substituted into the multivariate linear expression to calculate the corresponding pricing strategy.

Vegetable Automatic Pricing and Replenishment Decision-Making Problem Based on Cost-pricing Model

Automatic pricing and replenishment decision based on vegetable items is a key prediction and decision-making problem in fresh food superstores. Solving this problem is of great practical significance for the retail industry, which can not only improve the sales efficiency and customer satisfaction, but also reduce the operation cost, optimise the management of the superstore, and promote the digital transformation and intelligent development of the retail industry. Firstly, we derived the interrelationships between categories as well as individual items by calculating the Pearson's correlation coefficient, and the results were: the correlation between eggplant & aquatic rhizomes, eggplant & edible mushrooms was extremely weak or no correlation; the correlation between foliar & eggplant, chilli & eggplant, cauliflower & eggplant was weak; the correlation between cauliflower & edible mushrooms, cauliflower & aquatic rhizomes, cauliflower & chilli, cauliflower & aquatic rhizomes were moderately correlated; chilli & aquatic rhizomes, cauliflower & cauliflower, cauliflower & edible mushrooms, cauliflower & chilli, edible mushrooms & aquatic rhizomes, chilli & the correlation for aquatic rhizomes is strong. Finally, we calculated the selling price and cost by category, and obtained the relationship between cost-plus pricing and sales volume by fitting the "price-sales volume" curve. In order to maximise the revenue of the superstore, we make the results close to the ideal value, and predict the daily replenishment volume and pricing decision in the coming week by fitting the curve, which shows that the daily replenishment volume of cauliflower, foliage, chilli, eggplant, edible fungus, and aquatic rootstalks are 41.33, 195.96, 28.89, 76.15, 48.86, and 29.11 respectively, and the price are 0.53119, 0.71435, 0.59513, 0.62312, 0.61153, 0.51531.