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

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Title: Testing the performance of teaching–learning based optimization (TLBO) algorithm on combinatorial problems: Flow shop and job shop scheduling cases.
Authors: Baykasoğlu, Adil1 adil.baykasoglu@deu.edu.tr, Hamzadayi, Alper1, Köse, Simge Yelkenci1,2
Source: Information Sciences. Aug2014, Vol. 276, p204-218. 15p.
Subjects: Computer performance, Computers testing, Algorithms, Production scheduling, Combinatorics, Flow shop scheduling, Nonlinear programming
Abstract: Abstract: 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. [Copyright &y& Elsevier]
Copyright of Information Sciences is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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  Data: <searchLink fieldCode="DE" term="%22Computer+performance%22">Computer performance</searchLink><br /><searchLink fieldCode="DE" term="%22Computers+testing%22">Computers testing</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Production+scheduling%22">Production scheduling</searchLink><br /><searchLink fieldCode="DE" term="%22Combinatorics%22">Combinatorics</searchLink><br /><searchLink fieldCode="DE" term="%22Flow+shop+scheduling%22">Flow shop scheduling</searchLink><br /><searchLink fieldCode="DE" term="%22Nonlinear+programming%22">Nonlinear programming</searchLink>
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  Data: Abstract: 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. [Copyright &y& Elsevier]
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  Data: <i>Copyright of Information Sciences is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.ins.2014.02.056
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      – Code: eng
        Text: English
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        PageCount: 15
        StartPage: 204
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        Type: general
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      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Production scheduling
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      – SubjectFull: Combinatorics
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      – SubjectFull: Flow shop scheduling
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      – SubjectFull: Nonlinear programming
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      – TitleFull: Testing the performance of teaching–learning based optimization (TLBO) algorithm on combinatorial problems: Flow shop and job shop scheduling cases.
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              Text: Aug2014
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