Integrated warehouse assignment and carton configuration optimization using deep clustering-based evolutionary algorithms

Abstract

A rapid rise in e-commerce has forced logistic companies to invest in efficiency and reduce last-mile delivery costs. A significant part of last-mile delivery operations is the cartonization of orders and economically delivering them. An optimal carton configuration leads to a better cartonization and reduces the carton manufacturing costs and carbon footprint, whereas the optimal warehouse allocation directly reduces the transportation costs. Therefore, a multiobjective formulation has been proposed in this study to address the warehouse assignment and carton configuration optimization problem. A novel Interdependent Pareto Ant Colony Optimization (IPACO) has been integrated with a Deep Embedded Clustering algorithm (DEC) to form a DEC-based IPACO (DECIPACO) model to solve the proposed formulation. The integrated model was tested on 54 different datasets and compared against other clustering-based evolutionary algorithm models. The DECIPACO model provided an optimal or a non-dominated solution in all cases against the k-means clustering-based evolutionary algorithm models. Hence, the proposed DECIPACO model was able to explore an optimal trade-off between fuel costs and total carton volume while fulfilling the customer demand from inventory-constrained warehouses.