Testing the performance of teaching-learning based optimization (TLBO) algorithm on combinatorial problems: Flow shop and job shop scheduling cases

Baykasoglu A., Hamzadayi A., Kose S. Y.

INFORMATION SCIENCES, vol.276, pp.204-218, 2014 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 276
  • Publication Date: 2014
  • Doi Number: 10.1016/j.ins.2014.02.056
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.204-218
  • Keywords: Teaching-learning based optimization, Flow shop scheduling, Job shop scheduling, Makespan, Meta-heuristic, Combinatorial optimization, PARTICLE SWARM OPTIMIZATION, HYBRID GENETIC ALGORITHM, MULTIOBJECTIVE OPTIMIZATION, MACHINE, DESIGN
  • Van Yüzüncü Yıl University Affiliated: No


Teaching-learning based optimization (TLBO) algorithm has been recently proposed in the literature as a novel population oriented meta-heuristic algorithm. It has been tested on some unconstrained and constrained non-linear programming problems, including some design optimization problems with considerable success. The main purpose of this paper is to analyze the performance of TLBO algorithm on combinatorial optimization problems first time in the literature. We also provided a detailed literature review about TLBO's applications. The performance of the TLBO algorithm is tested on some combinatorial optimization problems, namely flow shop (FSSP) and job shop scheduling problems (JSSP). It is a well-known fact that scheduling problems are amongst the most complicated combinatorial optimization problems. Therefore, performance of TLBO algorithm on these problems can give an idea about its possible performance for solving other combinatorial optimization problems. We also provided a comprehensive comparative study along with statistical analyses in order to present effectiveness of TLBO algorithm on solving scheduling problems. Experimental results show that the TLBO algorithm has a considerable potential when compared to the best-known heuristic algorithms for scheduling problems. (C) 2014 Elsevier Inc. All rights reserved.