TY - GEN
T1 - An improved teaching-learning-based optimization algorithm to solve job shop scheduling problems
AU - Li, Linna
AU - Weng, Wei
AU - Fujimura, Shigeru
N1 - Funding Information:
ACKNOWLEDGEMENT The authors acknowledge the support of JSPS Grants-in-Aid for Scientific Research (KAKENHI) Grant Number 15K16296.
PY - 2017/6/27
Y1 - 2017/6/27
N2 - 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.
AB - 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.
KW - Job shop scheduling
KW - Optimization
KW - Teaching-learning-based optimization algorithm
UR - http://www.scopus.com/inward/record.url?scp=85030648290&partnerID=8YFLogxK
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U2 - 10.1109/ICIS.2017.7960101
DO - 10.1109/ICIS.2017.7960101
M3 - Conference contribution
AN - SCOPUS:85030648290
T3 - Proceedings - 16th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2017
SP - 797
EP - 801
BT - Proceedings - 16th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2017
A2 - Cui, Xiaohui
A2 - Yao, Shaowen
A2 - Xu, Simon
A2 - Zhu, Guobin
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2017
Y2 - 24 May 2017 through 26 May 2017
ER -