Incorporating Fully Fuzzy Logic in Multi-Objective Transshipment Problems: A Study of Alternative Path Selection Using LR Flat Fuzzy Numbers

In a world where supply chains are increasingly complex and unpredictable, finding the optimal way to move goods through transshipment networks is more important and challenging than ever. In addition to addressing the complexity of transportation costs and demand, this study presents a novel method that offers flexible routing alternatives to manage these complexities. When real-world variables such as fluctuating costs, variable capacity, and unpredictable demand are considered, traditional transshipment models often prove inadequate. To overcome these challenges, we propose an innovative fully fuzzy-based framework using LR flat fuzzy numbers. This framework allows for more adaptable and flexible decision-making in multi-objective transshipment situations by effectively capturing uncertain parameters. To overcome these challenges, we develop an innovative, fully fuzzy-based framework using LR flat fuzzy numbers to effectively capture uncertainty in key parameters, offering more flexible and adaptive decision-making in multi-objective transshipment problems. The proposed model also presents alternative route options, giving decision-makers a range of choices to satisfy multiple requirements, including reducing costs, improving service quality, and expediting delivery. Through extensive numerical experiments, we demonstrate that the model can achieve greater adaptability, efficiency, and flexibility than standard approaches. This multi-path structure provides additional flexibility to adapt to dynamic network conditions. Using ranking strategies, we compared our multi-objective transshipment model with existing methods. The results indicate that, while traditional methods such as goal and fuzzy programming generate results close to the anti-ideal value, thus reducing their efficiency, our model produces solutions close to the ideal value, thereby facilitating better decision making. By combining dynamic routing alternatives with a fully fuzzy-based approach, this study offers an effective tool to improve decision-making and optimize complex networks under real-world conditions in practical settings. In this paper, we utilize LINGO 18 software to solve the provided numerical example, demonstrating the effectiveness of the proposed method.