A genetic algorithm for integrated lot sizing and supplier selection with defective items and storage and supplier capacity constraints Online publication date: Tue, 10-Jan-2017
by Mohammad Saeid Atabaki; Mohammad Mohammadi
International Journal of Operational Research (IJOR), Vol. 28, No. 2, 2017
Abstract: The single product, multi-period inventory lot-sizing problem is one of the most common and basic problems in the production and inventory management literature. In this paper, we consider an environment with multiple suppliers and multiple periods with supplier capacity and storage capacity constraints. Moreover, considering defective items, we move one-step toward a real environment of inventory problems. In this paper, we present the nonlinear programming of the problem. Since complexity of lot sizing problems belongs to a class of NP-hard problems, we propose a genetic algorithm to solve the problem. We develop a unique encoding-decoding procedure, which creates feasible solutions. Using the Taguchi experimental design method, the optimum parameters of the proposed genetic algorithm are selected. The result comparison between proposed GA and GAMS software as an exact solution for small and medium size problems shows that we can trust the proposed GA as a solution methodology for larger problems.
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 Operational Research (IJOR):
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
Our Blog 
Follow us on Twitter 
Visit us on Facebook