@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}
}