A sustainable Fermatean fuzzy bi-objective four-dimensional transportation model for automated electric vehicle logistics planning using a Fermatean fuzzy game-theoretic method
Abstract
The transportation problem is a significant optimization model in supply chain management and logistics that aims to determine the most effective allocation of goods from multiple sources to various destinations. In a logistics plan, shipments are frequently transported through different routes using automated electric vehicles (AEVs) instead of regular vehicles, which offers a sustainable alternative by reducing transportation-related emissions, improving air quality, eliminating noise pollution, supporting green logistics, and reducing dependence on fossil fuels. The simultaneous consideration of objectives and parameters, such as origins, destinations, routes, and vehicles, extends the classical transportation problem to a bi-objective four-dimensional transportation problem, providing a realistic framework for complex logistics networks. However, in a real-world scenario, parameters are often uncertain due to dynamic traffic conditions, fluctuating energy prices, varying road conditions, and adverse weather conditions. The triangular Fermatean fuzzy environment effectively captures uncertainty associated with the parameters by allowing greater flexibility, thereby providing more reliable decision-making under uncertain conditions. For these reasons, this study develops a bi-objective four-dimensional transportation model under a triangular Fermatean fuzzy environment for AEV-based logistics. The proposed model is solved using a hybrid Fermatean fuzzy game-theoretic method implemented in LINGO (LINDO Systems Inc., IL, USA) to obtain a Pareto-optimal solution. The results indicate that the proposed approach effectively handles the uncertainty and optimizes the logistics plan, which supports environmentally sustainable transportation by minimizing dependence on fossil fuels, minimizing transportation costs and delivery time, and supporting the development of sustainable supply chain management.