An Intelligent Cooperative Approach Applied to Single Machine Total Weighted Tardiness Scheduling Problem

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Volume 12, Issue 4

Lamiche Chaabane

J. Info. Comput. Sci. , 12 (2017), pp. 270-279.

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Abstract

In this research work, we propose an intelligent search technique called genetic simulated annealing algorithm (GASA) to obtain an approximate solution to the single machine total weighted tardiness job scheduling problem, which is a strong NP-hard. The developed approach is based on two metaheuristics: genetic algorithm (GA) and simulated annealing (SA) algorithm. In this context, when GA is exploited as a global search strategy to discover solution space, SA algorithm is used as a local search technique to enhance more efficiently the visited attractive regions to improve solution quality. Numerical results using a set of benchmarks have shown the capability of the proposed method to produce better solutions compared to results given by some other recently literature works.

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@Article{JICS-12-270, author = {Lamiche Chaabane}, title = {An Intelligent Cooperative Approach Applied to Single Machine Total Weighted Tardiness Scheduling Problem}, journal = {Journal of Information and Computing Science}, year = {2017}, volume = {12}, number = {4}, pages = {270--279}, abstract = { In this research work, we propose an intelligent search technique called genetic simulated annealing algorithm (GASA) to obtain an approximate solution to the single machine total weighted tardiness job scheduling problem, which is a strong NP-hard. The developed approach is based on two metaheuristics: genetic algorithm (GA) and simulated annealing (SA) algorithm. In this context, when GA is exploited as a global search strategy to discover solution space, SA algorithm is used as a local search technique to enhance more efficiently the visited attractive regions to improve solution quality. Numerical results using a set of benchmarks have shown the capability of the proposed method to produce better solutions compared to results given by some other recently literature works. }, issn = {3080-180X}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/jics/22470.html} }

TY - JOUR T1 - An Intelligent Cooperative Approach Applied to Single Machine Total Weighted Tardiness Scheduling Problem AU - Lamiche Chaabane JO - Journal of Information and Computing Science VL - 4 SP - 270 EP - 279 PY - 2017 DA - 2017/12 SN - 12 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/jics/22470.html KW - AB - In this research work, we propose an intelligent search technique called genetic simulated annealing algorithm (GASA) to obtain an approximate solution to the single machine total weighted tardiness job scheduling problem, which is a strong NP-hard. The developed approach is based on two metaheuristics: genetic algorithm (GA) and simulated annealing (SA) algorithm. In this context, when GA is exploited as a global search strategy to discover solution space, SA algorithm is used as a local search technique to enhance more efficiently the visited attractive regions to improve solution quality. Numerical results using a set of benchmarks have shown the capability of the proposed method to produce better solutions compared to results given by some other recently literature works.

Lamiche Chaabane. (2017). An Intelligent Cooperative Approach Applied to Single Machine Total Weighted Tardiness Scheduling Problem. Journal of Information and Computing Science. 12 (4). 270-279. doi:

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