Abstract
This study introduces an innovative Hybrid Grid Partitioning and Fuzzy Goal Programming Model for Enhanced Performance and Decision Support. The proposed model combines grid partitioning and fuzzy goal programming techniques in order to address the challenges of complex systems and optimize performance while taking into account subjective preferences and uncertainties. Using grid partitioning, the hybrid model divides the problem space into smaller grid cells, facilitating localized analysis and efficient processing. Fuzzy goal programming handles ambiguous objectives and uncertain constraints, providing decision-making flexibility and robustness. The effectiveness of the model is demonstrated through a numerical example, highlighting enhanced performance and decision support for complex problems. The applicability of the hybrid paradigm to diverse fields, including transportation, logistics, and resource management, is discussed. The study also acknowledges limitations, such as computational complexity and the need for validation in the actual world. In the future, it will be necessary to resolve these limitations and improve the hybrid model. Overall, the research advances optimization methodologies and decision support systems by providing a comprehensive framework for confronting complex systems and facilitating well-informed decision making.
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