Learning Kruskal’s Algorithm, Prim’s Algorithm and Dijkstra’s Algorithm by Board Game

Author(s):  
Wen-Chih Chang ◽  
Yan-Da Chiu ◽  
Mao-Fan Li
Author(s):  
Wen-Chih Chang ◽  
Te-Hua Wang ◽  
Yan-Da Chiu

The concept of minimum spanning tree algorithms in data structure is difficult for students to learn and to imagine without practice. Usually, learners need to diagram the spanning trees with pen to realize how the minimum spanning tree algorithm works. In this paper, the authors introduce a competitive board game to motivate students to learn the concept of minimum spanning tree algorithms. They discuss the reasons why it is beneficial to combine graph theories and board game for the Dijkstra and Prim minimum spanning tree theories. In the experimental results, this paper demonstrates the board game and examines the learning feedback for the mentioned two graph theories. Advantages summarizing the benefits of combining the graph theories with board game are discussed.


Author(s):  
Wen-Chih Chang ◽  
Te-Hua Wang ◽  
Yan-Da Chiu

The concept of minimum spanning tree algorithms in data structure is difficult for students to learn and to imagine without practice. Usually, learners need to diagram the spanning trees with pen to realize how the minimum spanning tree algorithm works. In this paper, the authors introduce a competitive board game to motivate students to learn the concept of minimum spanning tree algorithms. They discuss the reasons why it is beneficial to combine graph theories and board game for the Dijkstra and Prim minimum spanning tree theories. In the experimental results, this paper demonstrates the board game and examines the learning feedback for the mentioned two graph theories. Advantages summarizing the benefits of combining the graph theories with board game are discussed.


2020 ◽  
Author(s):  
Fredrik Ljunggren ◽  
Kristian Persson ◽  
Anders Peterson ◽  
Christiane Schmidt

Abstract We present an algorithm to insert a train path in an existing railway timetable close to operation, when we want to affect the existing (passenger) traffic as little as possible. Thus, we consider all other trains as fixed, and aim for a resulting train path that maximizes the bottleneck robustness, that is, a train path that maximizes the temporal distance to neighboring trains in the timetable. Our algorithm is based on a graph formulation of the problem and uses a variant of Dijkstra’s algorithm. We present an extensive experimental evaluation of our algorithm for the Swedish railway stretch from Malmö to Hallsberg. Moreover, we analyze the size of our constructed graph.


Author(s):  
Stevens Johnson ◽  
V.M. Midhun ◽  
Nithin Issac ◽  
Ann Mariya Mathew ◽  
Shinosh Mathew

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