Stochastic Fuzzy Transportation Problem in Deliveries – A Case Study

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.