An improved flower pollination algorithm for optimization of intelligent logistics distribution center

Abstract

It is easy to fall into local optimal solution in solving the optimal location of intelligent logistics distribution center by traditional method and the result of optimization is not ideal. For this, the study puts forward an optimization method of intelligent logistics distribution center based on improved flower pollination algorithm. This method uses the logic self-mapping function to carry out chaotic disturbance to the pollen grains, so that the pollen grain set lacking the mutation mechanism has strong self-adaptability, and the convergence of the optimal solution in the later stage of the algorithm is effectively prevented. The boundary buffer factor is used to buffer the cross-boundary pollen grains adaptively so as to prevent the algorithm from the local optimization, and the convergence speed and the optimization accuracy of the algorithm can be improved obviously in processing the optimal location of intelligent logistics distribution center. The convergence of the algorithm is analyzed theoretically by using the real number coding method, and the biological model and theoretical basis of the algorithm are given. The experimental results show that the proposed method has better performance than the traditional one, and the algorithm outperforms a genetic algorithm and particle swarm algorithm. It provides a feasible solution for the intelligent logistics distribution center location strategy. It affords a good reference for improving and optimizing the internal logistics of the manufacturing system and the operational efficiency of the entire intelligent logistics system.

Optimization on Medical Material Distribution Management System Based on Artificial Intelligence Robot

“…Equation (7) indicates that the target value of the model is the minimum value taken by the target function at the confidence level. Constraint (8) indicates that all nodes satisfy the conservation of traffic when the confidence level is ‖. e constraint (9) indicates that the inventory of warehouses and departments must be higher than the safety stock when the confidence level is five.…”

“…In the background of multidrug ordering problems in hospitals, based on the theory of metamorphic inventory, the literature [ 7 ] established a mathematical model with inventory space as the constraint condition under the premise of allowing out-of-stock and having lead time and verified the validity of the model through numerical examples. The literature [ 8 ] proposed an optimized medical equipment ordering strategy and gave several formulas to determine the most reasonable order form. The literature [ 9 ] combines Six Sigma management and lean production methods to improve the hospital's inventory management of important medical equipment.…”

An Advanced System to Enhance and Optimize Delivery Operations in a Smart Logistics Environment

“…Though, in the solution process, this approach is susceptible to fall into local optimal solution, which damages the optimization results. To avoid the local optimal solution, Hu et al [35] presented an improved algorithm based on flower pollination. Although this algorithm ensures meeting the optimization requirements in smart logistics service, in reality, the optimization of smart logistics needs to carefully consider the effects of the e-commerce sector.…”

Regional logistics demand forecasting: a BP neural network approach

“…BP neural network has been applied to multi-disciplinary prediction. For example, Qin et al [43], Zhang et al [44], Hu [45], respectively, used this method to simulate and predict behavioral recognition, job-shop scheduling problem, and optimization of intelligent logistics distribution center. In view of the extensiveness and reliability of the model in the field of prediction, this study will use this method to predict the logistics demand scale of Guangdong province.…”