本科论文答辩中专家-学生匹配分组模型及算法  

Expert-student Matching Grouping Model and Algorithm in Undergraduate Thesis Oral Defense

作　　者：狄卫民[1] 徐文君 王然 DI Weimin;XU Wenjun;WANG Ran(School of Management Engineering,Zhengzhou University,Zhengzhou 450001,China)

机构地区：[1]郑州大学管理工程学院,郑州450001

出　　处：《实验室研究与探索》2020年第8期174-178,191,共6页Research and Exploration In Laboratory

基　　金：国家自然科学基金项目(U1604123);河南省教育科学规划课题([2018]-JKGHYB-0005);郑州大学管理工程学院创新发展基金项目(20170601)。

摘　　要：本科论文答辩是本科教学实践的重要一环。针对本科论文答辩特点,考虑导师和学生的公平性要求,建立了专家-学生匹配分组问题的两阶段序贯决策模型。其中,第一阶段确定最大的最小论文匹配度,第二阶段在保证上述最小论文匹配度的前提下,确定使平均论文匹配度最大化的专家-学生匹配分组方案。为便于模型求解,提出了以染色体库为纽带、嵌入线性整数规划精确算法的混合遗传算法,给出了算法的主要实现步骤。最后通过实例,验证了模型和算法的有效性。Undergraduate thesis oral defense is an important part of the undergraduate teaching practice. Considering the characteristics of undergraduate thesis oral defense and the fairness requirements of the tutors and the students,this paper established a two-stage sequential decision model for expert-student matching grouping problem. In the model,the first stage determined the maximum minimal thesis matching degree,and the second stage determined the expert-student collaborative grouping scheme that could maximize the average thesis matching degree,on the premise of ensuring the above-mentioned minimal thesis matching degree. To solve the model,this paper proposed a hybrid genetic algorithm with a chromosomal library as two-stage connection and an embedded exact solution method for linear integer programming,also gave main implementation steps of the algorithm. Finally,it verified the effectiveness of the model and algorithm by an example.

关 键 词：本科论文答辩 匹配分组 序贯决策模型 混合遗传算法 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]