TASNOP: A tool for teaching algorithms to solve network optimization problems

被引:7
|
作者
Lourenco, Wilson da Silva [1 ]
De Araujo Lima, Stanley Jefferson [2 ]
De Araujo, Sidnei Alves [2 ]
机构
[1] Nove Julho Univ, Dept Informat, Barra Funda, SP, Brazil
[2] Nove Julho Univ, Informat & Knowledge Management Grad Program, Agua Branca, SP, Brazil
关键词
max flow problem; network optimization problems; shortest path problem; teaching support tool; traveling salesman problem;
D O I
10.1002/cae.21864
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
This work presents the TASNOP-Teaching Algorithms for Solving Network Optimization Problems, a tool for teaching concepts and operation of some algorithms to solve Shortest Path, Maximum Flow, and Traveling Salesman problems, which are widely known and studied in graduate and undergraduate courses such as Industrial Engineering, Computer Science, Information Systems, and Logistics. Experiments carried out with undergraduate computer science students allowed to verify that the group who used TASNOP obtained better performance in the resolution of the proposed exercise, corroborating the idea that it contributed to improve the students' understanding about the addressed content. In addition, a questionnaire answered by the same group of students evidenced the potential of TASNOP as a teaching support tool.
引用
收藏
页码:101 / 110
页数:10
相关论文
共 50 条
  • [21] USING GENERALISED ANNOTATED PROGRAMS TO SOLVE SOCIAL NETWORK OPTIMIZATION PROBLEMS
    Shakarian, Paulo
    Subrahmanian, V. S.
    Sapino, Maria Luisa
    [J]. TECHNICAL COMMUNICATIONS OF THE 26TH INTERNATIONAL CONFERENCE ON LOGIC PROGRAMMING (ICLP'10), 2010, 7 : 182 - 191
  • [22] DISTRIBUTED NONLINEAR OPTIMIZATION ALGORITHMS FOR GENERAL NETWORK PROBLEMS
    SUBRAMANIAN, DK
    [J]. LECTURE NOTES IN CONTROL AND INFORMATION SCIENCES, 1988, 113 : 165 - 176
  • [23] Hybrid SGD algorithms to solve stochastic composite optimization problems with application in sparse portfolio selection problems
    Yang, Zhen-Ping
    Zhao, Yong
    [J]. JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS, 2024, 436
  • [24] E-BRM: A constraint handling technique to solve optimization problems with evolutionary algorithms
    Rodrigues, Max de Castro
    Guimaraes, Solange
    Leite Pires de Lima, Beatriz Souza
    [J]. APPLIED SOFT COMPUTING, 2018, 72 : 14 - 29
  • [25] Team of Bayesian Optimization Algorithms to Solve Task Assignment Problems in Heterogeneous Computing Systems
    Li, Jie
    Zhang, JunQi
    Kang, Qi
    Jiang, ChangJun
    [J]. 2014 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC), 2014, : 127 - 132
  • [26] Using multiobjective optimization algorithms and decision making support to solve polymer extrusion problems
    Denysiuk, Roman
    Recio, Gustavo
    Covas, Jose Antonio
    Gaspar-Cunha, Antonio
    [J]. POLYMER ENGINEERING AND SCIENCE, 2018, 58 (04): : 493 - 502
  • [27] Using the min-max method to solve multiobjective optimization problems with genetic algorithms
    Coello, CAC
    [J]. PROGRESS IN ARTIFICIAL INTELLIGENCE-IBERAMIA 98, 1998, 1484 : 303 - 314
  • [28] Using genetic algorithms to solve large-scale airline network planning problems
    Koelker, Katrin
    Luetjens, Klaus
    [J]. 18TH EURO WORKING GROUP ON TRANSPORTATION, EWGT 2015, 2015, 10 : 900 - 909
  • [29] Using Generalized Annotated Programs to Solve Social Network Diffusion Optimization Problems
    Shakarian, Paulo
    Broecheler, Matthias
    Subrahmanian, V. S.
    Molinaro, Cristian
    [J]. ACM TRANSACTIONS ON COMPUTATIONAL LOGIC, 2013, 14 (02)
  • [30] An Enhanced 1440 Coupled CMOS Oscillator Network to Solve Combinatorial Optimization Problems
    Graber, Markus
    Hofmann, Klaus
    [J]. 2023 IEEE 36TH INTERNATIONAL SYSTEM-ON-CHIP CONFERENCE, SOCC, 2023, : 1 - 6