An Effective Storage Management for University Library using Weighted K-Nearest Neighbor Algorithm

The most fascinating topic in economic geography is the storage location-allocation problem. The storage serves as a transition point to lower the cost of transmission. To produce an accurate and approximate response, two a model based hybrid of k-means –Particle Swarm Optimization(KPSO) proposed in this work. When compared to the existing model, the proposed model formulation is simpler and easier to understand. The testing findings show that the proposed model makes better use of the computer's Random Access Memory (RAM), allowing us to solve medium-sized tasks. This approach cannot outperform the MIP model in terms of run time. The multi-assignment facility location queries are included in the extension of the CP formulations. Initial PSO solutions are produced using the well-known data clustering technique K-means. The experimental results demonstrate that in terms of time, objective value, and reliability of performance metrics, the KPSO method is superior to the PSO.