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

Linna Li; Wei Weng; Shigeru Fujimura

doi:10.1109/ICIS.2017.7960101

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

Linna Li, Wei Weng, Shigeru Fujimura

Graduate School of Information, Production and Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (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 language	English
Title of host publication	Proceedings - 16th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	797-801
Number of pages	5
ISBN (Electronic)	9781509055074
DOIs	https://doi.org/10.1109/ICIS.2017.7960101
Publication status	Published - 2017 Jun 27
Event	16th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2017 - Wuhan, China Duration: 2017 May 24 → 2017 May 26

Other

Other	16th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2017
Country	China
City	Wuhan
Period	17/5/24 → 17/5/26

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

Access to Document

10.1109/ICIS.2017.7960101

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Cite this

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 proceeding › Conference 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

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