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

Linna Li, Wei Weng, Shigeru Fujimura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 16th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages797-801
Number of pages5
ISBN (Electronic)9781509055074
DOIs
Publication statusPublished - 2017 Jun 27
Event16th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2017 - Wuhan, China
Duration: 2017 May 242017 May 26

Other

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

Fingerprint

Job Shop Scheduling Problem
Optimization Algorithm
Teaching
Optimization
Heuristic algorithms
Heuristic algorithm
Diversification
Combinatorial optimization
Combinatorial Optimization Problem
Metaheuristics
Local Search
Learning
Job shop scheduling
Scheduling Problem
NP-complete problem
Optimal Solution
Coding
Scheduling
Benchmark
Lower bound

Keywords

  • Job shop scheduling
  • Optimization
  • Teaching-learning-based optimization algorithm

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

Cite this

Li, L., Weng, W., & Fujimura, S. (2017). An improved teaching-learning-based optimization algorithm to solve job shop scheduling problems. In Proceedings - 16th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2017 (pp. 797-801). [7960101] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIS.2017.7960101

An improved teaching-learning-based optimization algorithm to solve job shop scheduling problems. / Li, Linna; Weng, Wei; Fujimura, Shigeru.

Proceedings - 16th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 797-801 7960101.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Li, L, Weng, W & Fujimura, S 2017, An improved teaching-learning-based optimization algorithm to solve job shop scheduling problems. in Proceedings - 16th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2017., 7960101, Institute of Electrical and Electronics Engineers Inc., pp. 797-801, 16th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2017, Wuhan, China, 17/5/24. https://doi.org/10.1109/ICIS.2017.7960101
Li L, Weng W, Fujimura S. An improved teaching-learning-based optimization algorithm to solve job shop scheduling problems. In Proceedings - 16th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 797-801. 7960101 https://doi.org/10.1109/ICIS.2017.7960101
Li, Linna ; Weng, Wei ; Fujimura, Shigeru. / An improved teaching-learning-based optimization algorithm to solve job shop scheduling problems. Proceedings - 16th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 797-801
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