An Optimization Framework for On-Demand Meal Delivery System

Paul, Siddhartha; Rathee, Sunil; Matthew, Jose; Adusumilli, Kranthi Mitra

doi:10.1109/ieem45057.2020.9309922

Cited by 14 publications

(2 citation statements)

References 8 publications

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“…Steever et al [13] provided a mixed integer linear programming formulation for the virtual food court delivery problem and developed an auction-based heuristic to implement the dynamic setting. Paul et al [14] proposed a generic framework for the optimization of first-mile and last-mile of an on-demand meal delivery system. Their objective was to minimize the overall cost per delivery and the delay in order delivery time.…”

Section: Dynamic Food Delivery Problemmentioning

confidence: 99%

Reinforcement Learning-Based Dynamic Order Recommendation for On-Demand Food Delivery

Wang,

Dong

et al. 2024

Tsinghua Sci. Technol.

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On-demand food delivery (OFD) is gaining more and more popularity in modern society. As a kernel order assignment manner in OFD scenario, order recommendation directly influences the delivery efficiency of the platform and the delivery experience of riders. This paper addresses the dynamism of the order recommendation problem and proposes a reinforcement learning solution method. An actor-critic network based on long short term memory (LSTM) unit is designed to deal with the order-grabbing conflict between different riders. Besides, three rider sequencing rules are accordingly proposed to match different time steps of the LSTM unit with different riders. To test the performance of the proposed method, extensive experiments are conducted based on real data from Meituan delivery platform. The results demonstrate that the proposed reinforcement learning based order recommendation method can significantly increase the number of grabbed orders and reduce the number of order-grabbing conflicts, resulting in better delivery efficiency and experience for the platform and riders.

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Section: Dynamic Food Delivery Problemmentioning

confidence: 99%

Reinforcement Learning-Based Dynamic Order Recommendation for On-Demand Food Delivery

Wang,

Dong

et al. 2024

Tsinghua Sci. Technol.

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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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A simulation-based study to determine the negative externalities of hyper-local food delivery

Sinha

Pandit

2021

Transportation Research Part D: Transport and Environment

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An Optimization Framework for On-Demand Meal Delivery System

Cited by 14 publications

References 8 publications

Reinforcement Learning-Based Dynamic Order Recommendation for On-Demand Food Delivery

Reinforcement Learning-Based Dynamic Order Recommendation for On-Demand Food Delivery

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

A simulation-based study to determine the negative externalities of hyper-local food delivery

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