Das, Jyotirmoy Nirupam ; Tiwari, Manoj Kumar ; Sinha, Ashesh Kumar ; Khanzode, Vivek (2023) Integrated warehouse assignment and carton configuration optimization using deep clustering-based evolutionary algorithms Expert Systems with Applications, 212 . p. 118680. ISSN 0957-4174
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Official URL: https://doi.org/10.1016/j.eswa.2022.118680
Related URL: http://dx.doi.org/10.1016/j.eswa.2022.118680
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.
Item Type: | Article |
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Source: | Copyright of this article belongs to Elsevier Science. |
ID Code: | 139821 |
Deposited On: | 29 Aug 2025 14:46 |
Last Modified: | 29 Aug 2025 14:46 |
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