2023 National Undergraduate Mathematical Modeling Competition Question C Evaluation Rules
Score results are for reference only
80+ points, stable in the country
65 points National Award (70 points safe)
60-50 minutes Shoichi-Shoji
50-40 minutes Shoji-Shozo
Prizes for scores above 40
1. Overall impression (10 points) : There is no starting score. Based on the rationality of the assumptions, the standardization of the paper, the characteristics and innovation of the modeling (the application of creative models and methods, reasonable conclusions).
Problem review, problem analysis, model assumptions, symbol description, model evaluation, references, appendix
(starting at 6 points)
2. Question 1 : 20 points
Outlier handling (5 points) 0-5 points
Time distribution (5 points) 0-5 points
Correlation analysis (10 points)
Principles (0-2 points), models and solution methods (0-5 points), results and tests (0-3 points).
Conditions for using the model
3. Question 2: 25 points
Relationship 10 points
Qualitative (3 minutes) Quantitative (7-10 minutes)
Functional relations start with 6 points
Optimize model 15 points
Principles (0-3 points), models and solution methods and algorithms (0-7 points), results and tests (0-5 points).
4. Question 3: 25 points
Setting the total range of products available for sale (5 points)
Optimization model (20 points)
Principles (0-3 points), models and solution methods and algorithms (0-7 points), results and tests (0-5 points).
Restrictions
5. Question 4: 10 points
New data collected + justification (8 points) + feasibility (2 points)
6. Model test results inspection and sensitivity analysis (10 points)
Starting at 5 minutes
Note: Don’t be fooled, pay attention to papers with innovative methods and model applications.
This topic analyzes the sales data of vegetable commodities, studies the distribution patterns and interrelationships of different categories and single product sales, formulates replenishment and pricing strategies for vegetable commodities, and explores single product optimization combination plans under space constraints. Product sales volume and price may be related, and reliable demand analysis is the basis for formulating replenishment and pricing strategies.
Question 1 When analyzing the distribution patterns and interrelationships of sales volume, key considerations should be made:
(1) Abnormal data and situations such as discounts, returns, and no-sale items should be handled.
(2) When studying the distribution and change patterns of categories and single products, time effects should be considered.
(3) In the correlation analysis of category and single product sales, the conditions for using the correlation analysis method should be considered. Discussion of category or item distribution types should be encouraged.
(4) Simply doing simple descriptive statistics and visual display is not enough.
Question 2: Establish and solve the replenishment quantity and pricing strategy model based on the principle of maximizing returns, which should be considered particularly ;
(1) Analyze whether there is a correlation between sales volume, replenishment volume and price of each category from both qualitative and quantitative aspects; if relevant, a quantitative relationship model should be established.
(2) It is a good way to consider the interdependence between replenishment quantity and price from a mechanism perspective.
(3) Time factors of data should be considered, such as seasonality, periodicity, holidays, trends, etc.
(4) Specific results of replenishment volume and pricing for each category in the coming week should be given, and the difference between working days and weekends must be reflected.
Question 3 When establishing a single product sales selection model in each category, key considerations should be made:
(1) Determine the substitutable or complementary single products in each category. For example, you can classify the single products in each crystal category through correlation analysis.
(2) On the premise of considering the substitutability and complementarity of single products, a quantitative method for commodity variety diversity is given to meet the constraints of commodity demand and variety diversity.
(3) The solution to question 3 can be based on the model and method of question 2, but the difference between category and single product decision-making must be clarified.
(4) Encourage comparison of results under different models or optimization schemes.
Question 4 When discussing the objects of data collection, key considerations should be made:
(1) Give suggestions for collecting new data, such as business data (daily replenishment volume, inventory table, daily loss rate, etc.),
External data (weather, etc.), consumer data, etc., and explain the reasons (how to use new data to improve the model).
(2) Analyze the feasibility, economy and other factors of data collection.
Note:
(1) In questions 2 and 3, it is encouraged to reasonably consider the coupling relationship between replenishment quantity and pricing and solve them simultaneously.
(2) Since the wholesale price of goods during the forecast period is unknown, the pricing of goods should focus on the cost mark-up rate.
(3) Since the historical replenishment volume is unknown, it is a feasible approach to use the loss rate to estimate the historical replenishment volume. To handle category loss, historical sales volume and single product loss rate can be comprehensively utilized.