Integrating machine learning and real-time optimization for heterogeneous instant delivery orders scheduling and routing
DOI:
https://doi.org/10.35335/emod.v16i1.56Keywords:
Heterogeneous delivery, Machine learning, Real-time optimization, Routing, SchedulingAbstract
This research aims to integrate machine learning and real-time optimization for heterogeneous instant delivery order scheduling and routing. The objective is to minimize the total delivery time while considering factors such as demand, time windows, predicted demand, and vehicle capacity constraints. By leveraging machine learning algorithms and real-time data, the proposed approach provides adaptive decision-making capabilities, allowing for dynamic adjustments in response to changing conditions. A mathematical formulation is developed to model the problem, and an algorithm is proposed to solve it. A numerical example is presented to demonstrate the effectiveness of the approach. The results highlight the optimal assignment of orders to vehicles at different time periods, leading to efficient delivery routes and minimized delivery time. The integration of machine learning and real-time optimization offers promising opportunities for enhancing the efficiency and responsiveness of delivery operations. This research contributes to advancing the field of instant delivery order scheduling and routing and paves the way for further developments in real-time logistics optimization
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Copyright (c) 2022 Patrisia Teresa Marsoit, Patrisius Michaud Felix Marsoit, Ralp Varene
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