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Kottakki, K. K., Rathee, S., Adusumilli, K. M., Mathew, J., Nayak, B., & Ahuja, S. were the authors of the paper “Customer Experience Driven Assignment Logic for Online Food Delivery” which was presented in the 2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

Link to the paper : https://ieeexplore.ieee.org/document/9309783

Photo by Андрей Сизов on Unsplash

Abstract:
This paper presents an assignment algorithm for online food delivery which balances both customer experience and delivery costs. Authors propose a multi-objective optimization model to maximize customer experience and minimize delivery costs. For this objective, customer experience is modelled as a time-variant piece-wise linear function, resulting in customer experience meal-delivery routing-problem (CX-MDRP). The utility of the proposed methodology is demonstrated through a live experiment at one of the cities in India for 14 days, having approximately a hundred thousand food orders per day.

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Moghe, R. P., Rathee, S., Nayak, B., & Adusumilli, K. M. (2021) published a paper on Machine Learning based Batching Prediction System for Food Delivery in the 8th ACM IKDD CODS and 26th COMAD (pp. 316–322).

Link to the paper : https://dl.acm.org/doi/pdf/10.1145/3430984.3430999

Abstract:
Delivery time estimates are an important factor for online food delivery platforms. These platforms also depend on batching — delivering two orders together — to increase efficiency and reduce cost. In this paper we propose a novel system for enhanced delivery time estimates for batched orders. The system is based on multiple machine learning algorithms that work together to make the predictions. We observe that the system leads to an increase in the number of times the food is delivered within the estimated delivery times by about 6%.

Photo by Kelly Sikkema on Unsplash

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