Authors : A. Sai Rama Raju; V.V.S. Kesava Rao

Volume/Issue : Volume 8 - 2023, Issue 8 - August

Google Scholar : https://bit.ly/3TmGbDi

Scribd : https://tinyurl.com/2ah4ekfw

DOI : https://doi.org/10.5281/zenodo.8340957

Abstract : Industries growth drives transportation development, leading to diverse methods. This expansion brings challenges, notably the stochastic fuzzy transportation problem (SFTP), a probabilistic chance- constrained programming (CCP) issue. SFTP handles fuzzy objectives amid supply-demand randomness. The transportation problem's (TP) core aim is efficient product movement between customers and producers to meet demand at a lower cost. TP's parameters encompass cost, supply, and demand. Uncertainties in reality include randomness and fuzziness. Randomness reflects potential outcomes and is quantified using random variables (RVs). Real-world scenarios involve multiple objectives, e.g., cost and time minimization. This study addresses the multi- objective transportation problem within a stochastic- fuzzy context, using Weibull distribution. The goal is to optimize transportation quantities considering real-world uncertainties.

Keywords : SFTP; CCP; TP.