Customer Experience Driven Assignment Logic for Online Food Delivery

Kottakki, Krishna Kumar; Rathee, Sunil; Adusumilli, Kranthi Mitra; Mathew, Jose; Nayak, Bharat; Ahuja, Saket

doi:10.1109/ieem45057.2020.9309783

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…Earlier works in this space have mostly attempted to minimize the order delivery time alone [Kottakki et al, 2020;Ulmer et al, 2021], by pre-placing delivery agents in different parts of the city anticipating demand [Xue et al, 2021], or by minimizing delivery agents' waiting time [Weng and Yu, 2021]. FOODMATCH [Joshi et al, 2022;Joshi et al, 2021] overcame many simplifying assumptions made in prior works (e.g., perfect order arrival information [Yildiz and Savelsbergh, 2019], neglecting the road network [Reyes et al, 2018] and food preparation time [Zeng et al, 2019]), and proposed a realistic and scalable solution.…”

Section: Work4food (G = )mentioning

confidence: 99%

Gigs with Guarantees: Achieving Fair Wage for Food Delivery Workers

Rezayi¹,

Liu²,

Wu³

et al. 2022

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence

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Pretraining domain-specific language models remains an important challenge which limits their applicability in various areas such as agriculture. This paper investigates the effectiveness of leveraging food related text corpora (e.g., food and agricultural literature) in pretraining transformer-based language models. We evaluate our trained language model, called AgriBERT, on the task of semantic matching, i.e., establishing mapping between food descriptions and nutrition data, which is a long-standing challenge in the agricultural domain. In particular, we formulate the task as an answer selection problem, fine-tune the trained language model with the help of an external source of knowledge (e.g., FoodOn ontology), and establish a baseline for this task. The experimental results reveal that our language model substantially outperforms other language models and baselines in the task of matching food description and nutrition.

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Section: Work4food (G = )mentioning

confidence: 99%

Gigs with Guarantees: Achieving Fair Wage for Food Delivery Workers

Rezayi¹,

Liu²,

Wu³

et al. 2022

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence

View full text Add to dashboard Cite

show abstract

“…[19][20][21]. For the OFD scenario in Swiggy, Kottakki et al [22] modeled the customer experience as a time-variant piece-wise linear function and built a multi-objective optimization model solved by Gurobi; Paul et al [23] proposed a generic optimization framework with a batching algorithm and a mathematical model to assign the order batches; Joshi et al [24] also proposed a FOODMATCH algorithm to group the orders and assign the order batches. For the meal delivery problem from Getir, Jahanshahi et al [25] and Bozanta et al [26] modeled the dynamic arrival of orders and the rider behaviors as a Markov decision process where the riders can accept or reject the orders.…”

Section: Introductionmentioning

confidence: 99%

A Matching Algorithm with Reinforcement Learning and Decoupling Strategy for Order Dispatching in On-Demand Food Delivery

Chen,

Wang,

Pan

et al. 2024

Tsinghua Sci. Technol.

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The on-demand food delivery (OFD) service has gained rapid development in the past decades but meanwhile encounters challenges for further improving operation quality. The order dispatching problem is one of the most concerning issues for the OFD platforms, which refer to dynamically dispatching a large number of orders to riders reasonably in very limited decision time. To solve such a challenging combinatorial optimization problem, an effective matching algorithm is proposed by fusing the reinforcement learning technique and the optimization method.First, to deal with the large-scale complexity, a decoupling method is designed by reducing the matching space between new orders and riders. Second, to overcome the high dynamism and satisfy the stringent requirements on decision time, a reinforcement learning based dispatching heuristic is presented. To be specific, a sequence-tosequence neural network is constructed based on the problem characteristic to generate an order priority sequence.Besides, a training approach is specially designed to improve learning performance. Furthermore, a greedy heuristic is employed to effectively dispatch new orders according to the order priority sequence. On real-world datasets, numerical experiments are conducted to validate the effectiveness of the proposed algorithm. Statistical results show that the proposed algorithm can effectively solve the problem by improving delivery efficiency and maintaining customer satisfaction.

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