Vol.70, No.1, 2022, pp.817-829, doi:10.32604/cmc.2022.019685
OPEN ACCESS
ARTICLE
An Artificial Intelligence Approach for Solving Stochastic Transportation Problems
• Prachi Agrawal1, Khalid Alnowibet2, Talari Ganesh1, Adel F. Alrasheedi2, Hijaz Ahmad3, Ali Wagdy Mohamed4,5,*
1 Department of Mathematics and Scientific Computing, National Institute of Technology Hamirpur, Himachal Pradesh, 177005, India
2 Statistics and Operations Research Department, College of Science, King Saud University, Riyadh, 11451, Kingdom of Saudi Arabia
3 Section of Mathematics, International Telematic University Uninettuno, Roma, 00186, Italy
4 Operations Research Department, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza, 12613, Egypt
5 Wireless Intelligent Networks Center (WINC), School of Engineering and Applied Sciences, Nile University, Giza, Egypt
* Corresponding Author: Ali Wagdy Mohamed. Email:
(This article belongs to this Special Issue: Role of Computer in Modelling & Solving Real-World Problems)
Received 22 April 2021; Accepted 25 May 2021; Issue published 07 September 2021
Abstract
Recent years witness a great deal of interest in artificial intelligence (AI) tools in the area of optimization. AI has developed a large number of tools to solve the most difficult search-and-optimization problems in computer science and operations research. Indeed, metaheuristic-based algorithms are a sub-field of AI. This study presents the use of the metaheuristic algorithm, that is, water cycle algorithm (WCA), in the transportation problem. A stochastic transportation problem is considered in which the parameters supply and demand are considered as random variables that follow the Weibull distribution. Since the parameters are stochastic, the corresponding constraints are probabilistic. They are converted into deterministic constraints using the stochastic programming approach. In this study, we propose evolutionary algorithms to handle the difficulties of the complex high-dimensional optimization problems. WCA is influenced by the water cycle process of how streams and rivers flow toward the sea (optimal solution). WCA is applied to the stochastic transportation problem, and obtained results are compared with that of the new metaheuristic optimization algorithm, namely the neural network algorithm which is inspired by the biological nervous system. It is concluded that WCA presents better results when compared with the neural network algorithm.
Keywords
Artificial intelligence; metaheuristic algorithm; stochastic programming; transportation problem; water cycle algorithm; weibull distribution