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Reducing Delivery Time in E-Commerce Orders With Advanced Ant Colony Hybridization by GRU Algorithm

Year: 2025
Type: article
Abstract: The efficiency of the supply chain is highly corelated with the acceleration of preparing orders from the warehouse to meet customer requests on time. This objective can be activated by guiding via UAV to sketch the shortest route collect all customer requests’. A unified framework has been built consisting of three sequential stages guaranteeing quick customer responses to meet SDG 8.0 about economic growth via Working Capital Turnover. The preamble stage aims to sort the items according to XYZ/ABC pattern. The 2ndstage classifies the customers’ orders according to similarity via hybridising the (Extreme Gradient Boosting) XGboost and GRU (gate recurrent unit) algorithms that generate all possible orders’ lists in 1.2586 sec with RMSE 0.01395, which is filtered in the 3rdstage via creating their shortest route via hybridising (Ant Colony Optimisation) ACO and GRU algorithms to give workers to pick up more items in minimum time to accelerate preparing orders. The challenge lies in assigning a new order to one of the lists that belongs to Map (A) and creating Map (Bi). The paper presents innovative tuning parameters for ACO that help in accelerating orders preparation by 152.49%/worker/day and achieve overall equipment effectiveness (OEE) approximate to 88%. The ACO_GRU superior to Regular Ant Colony (RgACO), Elitist Ant Colony (EACO), Ranked Ant Colony (RnACO), Best-Worst Ant Colony (BWACO), Min-Max Ant Colony (MMACO), Mat-ACO, A* algorithm, Dijkstra’s algorithm, and MRL-SA with 19%, 16%, 9%, 12%, 6%, 6%, 5%, 5%, 3%, respectively. Therefore, the proposed model called “Acceleration Customer Orders’ Preparation” (ACOP).
Source: IEEE Access
DOI: 10.1109/access.2025.3582800
Language: English
Open Access status: gold
Estimated APC paid: $2,075
OpenAlex ID: W4411599617
FWCI: 0.427
Cites: 57
Cited by: 1
Related to: 10