An Artificial Intelligence Approach for Solving Stochastic Transportation Problems

Recent years witness a great deal of interest in artificial intelligence (AI) tools the area optimization. AI has developed large number to solve most difficult search-and-optimization problems computer science and operations research. Indeed, metaheuristic-based algorithms are sub-field AI. This study presents use metaheuristic algorithm, that is, water cycle algorithm (WCA), transportation problem. A stochastic problem is considered which parameters supply demand as random variables follow Weibull distribution. Since stochastic, corresponding constraints probabilistic. They converted into deterministic using programming approach. In this study, we propose evolutionary handle difficulties complex high-dimensional optimization problems. WCA influenced by process how streams rivers flow toward sea (optimal solution). applied problem, obtained results compared with new namely neural network inspired biological nervous system. It concluded better when algorithm.