An Intelligent Cooperative Approach Applied to Single Machine Total Weighted Tardiness Scheduling Problem
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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 = {2024},
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 = {1746-7659},
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 - 2024
DA - 2024/01
SN - 12
DO - http://doi.org/
UR - https://global-sci.org/intro/article_detail/jics/22470.html
KW - genetic simulated annealing, scheduling, genetic algorithm, simulated annealing, benchmarks.
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. (2024). 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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