Adaptive genetic algorithm for scheduling problem in flexible workshop with low carbon constraints Online publication date: Wed, 24-Feb-2021
by Haixia Liu; Renwang Li; Yichao He
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 20, No. 1, 2021
Abstract: Taking the completion time, energy carbon emission and total machine load as independent time factors into consideration, a flexible workshop scheduling model is established to minimise the maximum completion time, energy emissions and the total machine load. The population could be initialised by greedy algorithm and random number method, and this model could be solved by the crossover probability and genetic probability adaptive method. The feasibility and effectiveness of the improved genetic algorithm are verified by testing the data set and comparing several single-objective data and normalised multi-objective data.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Wireless and Mobile Computing (IJWMC):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com