Machine Learning based Batching Prediction System for Food Delivery

Moghe, Ritwik Prashant; Rathee, Sunil; Nayak, Bharath; Adusumilli, Kranthi Mitra

doi:10.1145/3430984.3430999

Cited by 4 publications

(1 citation statement)

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“…In one of these studies, the authors of [37] suggested a solution for planning capacity based on the delivery area's distances, payments per delivery for couriers, and delivery time to enhance multiple food deliveries in the short term. Machine learning research focusing on food delivery aims to predict the delivery time and minimize costs [38]. An early study [39] in the field of logistics used multiple forecasting methods (a moving average method, autoregressive (AR) model, and the autoregressive integrated moving average (ARIMA)) to forecast the delivery volumes for different products based on historical shipment data and input constraints.…”

Section: Capacity Planning With Machine Learningmentioning

confidence: 99%

An Integrated Framework for Dynamic Vehicle Routing Problems with Pick-up and Delivery Time Windows and Shared Fleet Capacity Planning

Küp,

Cebeci,

Bayram

et al. 2024

Symmetry

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This paper proposes a novel route optimization framework to solve the problem of instant pick-up and delivery for e-grocery orders. The proposed framework extends the traditional time-windowed package delivery problem. We demonstrate the effectiveness of our approach for this integrated problem using actual delivery data from HepsiJet, a leading e-commerce logistics provider in Turkey. We first employ several machine learning algorithms and simulations to investigate the capacity of the courier. Subsequently, a dynamic route planning workflow is executed with a highly specialized and novel routing algorithm. Our proposed heuristic approach considers combined fleet operations for delivering regular packages originating from a central depot and dynamic e-grocery orders picked up at local supermarkets and delivered to the customers. The heuristic algorithm constitutes k-opt and node transfer operation variations customized for this integrated problem. We report the performance of our approach in problem instances from the literature and instances from HepsiJet’s daily operations, which we also publicly share as new route optimization problem instances. Our results suggest that, despite the more complex nature of the integrated problem, our proposed algorithm and solution framework produce more efficient and cost-effective solutions that offer additional business opportunities for companies such as HepsiJet. The computational analyses reveal that implementing our proposed approach yields significant efficiency gains and cost reductions for the company, with a distance reduction of over 30%, underscoring our approach’s effectiveness in achieving substantial cost savings and enhanced efficiency through integrating two distinct delivery operations.

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Section: Capacity Planning With Machine Learningmentioning

confidence: 99%