An improved teaching-learning-based optimization algorithm to solve job shop scheduling problems

Linna Li, Wei Weng, Shigeru Fujimura

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

Job shop scheduling problem (JSP) is a strongly NP-hard combinatorial optimization problem. It is difficult to solve the problem to the optimum in a reasonable time. Teaching-learning-based optimization (TLBO) algorithm is a novel population oriented meta-heuristic algorithm. It has been proved that TLBO has a considerable potential when compared to the best-known heuristic algorithms for scheduling problems. In this paper, the traditional TLBO is improved to enhance diversification and intensification when exploring solutions for JSP. The improvements include changing the coding method, increasing number of teachers, introducing new learners and performing local search around potentially optimal solutions. To show effectiveness of the improved TLBO algorithm, the simulation results obtained by the improved TLBO for benchmark problems are compared with results obtained by the traditional TLBO and the best known lower bounds.

本文言語English
ホスト出版物のタイトルProceedings - 16th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2017
編集者Xiaohui Cui, Shaowen Yao, Simon Xu, Guobin Zhu
出版社Institute of Electrical and Electronics Engineers Inc.
ページ797-801
ページ数5
ISBN(電子版)9781509055074
DOI
出版ステータスPublished - 2017 6 27
イベント16th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2017 - Wuhan, China
継続期間: 2017 5 242017 5 26

出版物シリーズ

名前Proceedings - 16th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2017

Other

Other16th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2017
CountryChina
CityWuhan
Period17/5/2417/5/26

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Information Systems and Management
  • Modelling and Simulation

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