Improved Marine Predators Algorithm and Extreme Gradient Boosting (XGBoost) for shipment status time prediction

Abstract

Shipment Status Time Prediction (STP) is a complex problem requiring expertise in many disciplines, including Machine Learning (ML) and logistics management, to develop effective solutions. Estimating every possible shipment step before creation plays a vital role in e-commerce sale channels. In this point of view, a novel STP approach was proposed to predict shipment status times. The proposed approach involves two phases. The first leg of the novel STP approach is to build an ML model for estimating shipment statuses using a dataset acquired from a real-world application. The Extreme Gradient Boosting (XGB) and popular ML algorithms were compared for the classification of shipment status prediction first. The XGB algorithm performed best among the compared algorithms, with 99.92% and 96.16% accuracy for training and test sets. Moreover, the ML algorithms were run on the public New York City taxi trip dataset. The XGB algorithm exhibited the best performance. The accuracy for training and test sets are 97.40% and 97.33%, respectively. The second phase of the proposed approach is shipment status time estimation, designed as an optimization problem. The Marine Predators Algorithm (MPA) is a recently proposed optimization algorithm for numerical function optimization. An improved MPA algorithm for STP (STPMPA) was proposed in this study. The performance of the STPMPA algorithm was scrutinized on numerical benchmark problems first. The STPMPA algorithm outperformed all the algorithms in the experiment. Then, the most feasible shipment status times are searched by optimizers using the XGB model. The proposed STPMPA algorithm put forth a superior performance for the STP problem than the compared optimization algorithms. Consequently, experimental studies reveal that the proposed STP approach is able to generate efficient estimations in reasonable times for real-time systems.