<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Driss Imen</style></author><author><style face="normal" font="default" size="100%">Mouss kinza Nadia</style></author><author><style face="normal" font="default" size="100%">Laggoun Assia</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A New Genetic Algorithm or the Flexible Job Shop scheduling problems, ISSN/ISBN 1738-494X / 1976-3824</style></title><secondary-title><style face="normal" font="default" size="100%">J MECH SCI TECHNOL</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://link.springer.com/article/10.1007/s12206-015-0242-7</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">Volume 29</style></volume><pages><style face="normal" font="default" size="100%"> pp 1273–1281</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Flexible job-shop scheduling problem (FJSP), which is proved to be NP-hard, is an extension of the classical job-shop scheduling problem. In this paper, we propose a new genetic algorithm (NGA) to solve FJSP to minimize makespan. This new algorithm uses a new chromosome representation and adopts different strategies for crossover and mutation. The proposed algorithm is validated on a series of benchmark data sets and tested on data from a drug manufacturing company. Experimental results prove that the NGA is more efficient and competitive than some other existing algorithms.</style></abstract><issue><style face="normal" font="default" size="100%">Issue 3</style></issue></record></records></xml>