About this Journal Submit a Manuscript Table of Contents
ISRN Artificial Intelligence
Volume 2013 (2013), Article ID 795752, 13 pages
http://dx.doi.org/10.1155/2013/795752
Research Article

Multiobjective Stochastic Programming for Mixed Integer Vendor Selection Problem Using Artificial Bee Colony Algorithm

1Department of Industrial Management, Management and Accounting, Shahid Beheshti University, Tehran, Iran
2Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran

Received 21 September 2013; Accepted 13 October 2013

Academic Editors: R.-C. Hwang, P. Kokol, and Q. K. Pan

Copyright © 2013 Mostafa Ekhtiari and Shahab Poursafary. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

It has been always critical and inevitable to select and assess the appropriate and efficient vendors for the companies such that all the aspects and factors leading to the importance of the select process should be considered. This paper studies the process of selecting the vendors simultaneously in three aspects of multiple criteria, random factors, and reaching efficient solutions with the objective of improvement. Thus, selecting the vendors is introduced in the form of a mixed integer multiobjective stochastic problem and for the first time it is converted by CCGC (min-max) model to a mixed integer nonlinear single objective deterministic problem. As the converted problem is nonlinear and solving it in large scale will be time-consuming then the artificial bee colony (ABC) algorithm is used to solve it. Also, in order to better understand ABC efficiency, a comparison is performed between this algorithm and the particle swarm optimization (PSO) and the imperialist competitive algorithm (ICA) and Lingo software output. The results obtained from a real example show that ABC offers more efficient solutions to the problem solving in large scale and PSO spends less time to solve the same problem.