Data Clustering-based Metaheuristic for Physical Internet Supply Chain Network

In this study, a data clustering-driven technique is proposed for a Physical Internet Supply Chain Network (PI-SCN) to reduce data complexity, process time compression, and lankness of process optimization. Given a set of data points, a clustering algorithm aims to classify each data-points into a specific group. Each group should have similar properties and/or features, while data points in different groups should have highly dissimilar properties and/or features. The motivation of this study follows. Firstly, an improved metaheuristic algorithm named ISCA is proposed as a new data clustering technique to improve and incorporate a variety of PI-SCN decisions. By this framework, we propose a tool to make clear decisions for enterprise proprietors. The robustness of the proposed approach is tested against five recent metaheuristics using twelve benchmark datasets. The presented technique performs more satisfactory accurateness and complete coverage of search space in comparison to the existing methods.