Navigating Complexity in Multiobjective Green Capacitated Transportation Problem With Type‐2 Fuzziness and Interactive Bipolar Neutrosophic Programming

This research addresses the pressing need for affordable, ecofriendly modes of transportation. The work proposes a novel framework of multiobjective green capacitated transportation problem with Type‐2 fuzzy parameters to optimize transportation variables related to shipping cost, damage, labor, and time and accounts for multiple shipping carbon emissions. Critical values are used to reduce model uncertainty by using crisp uncertain values. Next, uncertain parameters are defuzzified using interval‐based estimation, and for decision‐making, interactive bipolar neutrosophic programming that considers neutral ideas and degrees of indeterminacy is used. Through an industrial scenario, this study demonstrates the framework’s feasibility and usefulness in handling the multiobjective problem. Comparative research shows that the approach provides near‐optimal solutions and a more decisive multiobjective model solution. The study results have important implications for developing sustainable transportation strategies that help businesses meet their cost and environmental goals while considering transportation planning uncertainties and future research investigations.