A novel approach for location selection in logistics: Ensemble SVM and improved genetic algorithm

Abstract

SummaryGathering logistics operations in an individual location offers various benefits at the macro level namely reducing environmental and community issues, replacing overflow of traffic, minimizing air pollution, and more. But the complexity in the selection of location is maximized and the logistic operation chooses the wrong location due to frequent variations in characteristics. Also, it is suitable for capturing long‐term dependencies. To overcome these difficulties, we propose ESVM‐IGS; an Ensemble support vector machine, and an Improved Genetic algorithm, with an initial search strategy to improve the efficiency. The Ensemble SVM is applied to produce a better outcome. To identify optimal configurations in the complex optimization problem an initial search‐based improved genetic algorithm is implemented. To conduct our experiments, the ESVM‐IGS is rigorously evaluated on the GIS real‐time Dataset and the efficiency of the model is validated with various performance measures. From the analysis, the proposed method results that it solved the complexity burden and improved the selection ability of long‐term dependency. The experimental results depict the better efficiency of the ESVM‐IGS method for the location selection strategy of logistics.