Hybrid Grid Partitioning and Fuzzy Goal Programming Model To Manufacturing Systems Modelling
DOI:
https://doi.org/10.35335/emod.v15i2.46Keywords:
Hybrid Grid Partitioning, Fuzzy Goal Programming, Manufacturing Systems, Resource Allocation, Spatial Optimization, Decision SupportAbstract
This research develops a Hybrid Grid Partitioning and Fuzzy Goal Programming manufacturing systems model. The model optimizes resource allocation while considering system spatial layout and production target imprecision. The model supports industrial system decision-making by integrating grid partitioning with fuzzy goal fulfilment. The model's binary resource allocation and fuzzy goal fulfillment decision variables are mathematically formulated. It optimizes resource allocation costs while meeting ambiguous goal constraints. The model estimates grid cell satisfaction levels for numerous manufacturing goals, such as cost minimization and production rate maximization, depending on production rates. The research shows that Hybrid Grid Partitioning with Fuzzy Goal Programming optimizes resource allocation and system performance. The model shows localized resource distribution within grid cells, including spatial limits and workstation distances. Fuzzy goal satisfaction tackles manufacturing goal imprecision and unpredictability, allowing decision-makers to adjust to changing market conditions and optimize goal satisfaction. The simplified production system and computational complexity of the model are constraints of the research. Real-world case studies must test the model's efficacy and applicability, and further study must examine scalability, parameter sensitivity, and goal integration. The research advances manufacturing systems modeling by considering spatial allocation and fuzzy goal fulfilment. In dynamic and uncertain contexts, the hybrid model optimizes resource allocation, efficiency, and manufacturing system competitiveness.
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