Journal Article

Decision-Making Teaching Practice Based on the Maximum Entropy Method in a Water Engineering Economics Course

Sun, Yingke, Zhang, Kuo, Shao, Shuai, Zhou, Runjuan, Zhang, Ming

2023

Overview

The purpose of this paper is to put forward a decision model with wide applicability and differentiated decision scheme scores so as to improve the ability of students to learn during a water engineering economics course. The main novelty and contributions of this paper are that the multi-attribute decision-making method proposed is more objective and does not require rich subjective experience from decision-makers in the application process, which is particularly suitable for beginners who are learning in a water engineering economics course. The method involves standardizing each index value of the decision scheme first, constructing the objective function of maximum entropy distribution, calculating the weight of each index by the genetic algorithm, and finally ranking the pros and cons of the scheme according to the score of each scheme. The example results of three water engineering scheme decisions show that the maximum entropy model proposed in this paper can achieve reasonable decision results, and there is a large degree of differentiation between the decision schemes. The proposed scheme, a decision maximum entropy model, has wide applicability, can improve the rationality of the decisions made regarding water engineering schemes, and can be popularized and applied when teaching decision-making in water engineering economics courses.

• Publisher: MDPI AG,MDPI
• Subject: Decision making / Economics / Educational aspects / Engineering / Engineering economy / Entropy (Information theory) / genetic algorithm / Genetic algorithms / maximum entropy / Maximum entropy method / Methods / Optimization / Probability distribution / Students / Study and teaching / Teaching / Water engineering / water engineering economics / Water supply engineering