Harvesting Efficient On-Demand Order Pooling from Skilled Couriers: Enhancing Graph Representation Learning for Refining Real-time Many-to-One Assignments

• URL: http://arxiv.org/abs/2406.14635v1
• Date: Thu, 20 Jun 2024 18:03:27 GMT
• Title: Harvesting Efficient On-Demand Order Pooling from Skilled Couriers: Enhancing Graph Representation Learning for Refining Real-time Many-to-One Assignments
• Authors: Yile Liang, Jiuxia Zhao, Donghui Li, Jie Feng, Chen Zhang, Xuetao Ding, Jinghua Hao, Renqing He,
• Abstract summary: On-demand food delivery (OFD) services offer delivery fulfillment within dozens of minutes after an order is placed. In OFD, pooling multiple orders for simultaneous delivery in real-time order assignment is a pivotal efficiency source. The complexity and real-time nature of order assignment, making extensive calculations impractical, significantly limit the potential for order consolidation. A SC delivery network (SCDN) is constructed, based on an enhanced attributed heterogeneous network embedding approach tailored for OFD.
• Score: 11.0829498096027
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The recent past has witnessed a notable surge in on-demand food delivery (OFD) services, offering delivery fulfillment within dozens of minutes after an order is placed. In OFD, pooling multiple orders for simultaneous delivery in real-time order assignment is a pivotal efficiency source, which may in turn extend delivery time. Constructing high-quality order pooling to harmonize platform efficiency with the experiences of consumers and couriers, is crucial to OFD platforms. However, the complexity and real-time nature of order assignment, making extensive calculations impractical, significantly limit the potential for order consolidation. Moreover, offline environment is frequently riddled with unknown factors, posing challenges for the platform's perceptibility and pooling decisions. Nevertheless, delivery behaviors of skilled couriers (SCs) who know the environment well, can improve system awareness and effectively inform decisions. Hence a SC delivery network (SCDN) is constructed, based on an enhanced attributed heterogeneous network embedding approach tailored for OFD. It aims to extract features from rich temporal and spatial information, and uncover the latent potential for order combinations embedded within SC trajectories. Accordingly, the vast search space of order assignment can be effectively pruned through scalable similarity calculations of low-dimensional vectors, making comprehensive and high-quality pooling outcomes more easily identified in real time. SCDN has now been deployed in Meituan dispatch system. Online tests reveal that with SCDN, the pooling quality and extent have been greatly improved. And our system can boost couriers'efficiency by 45-55% during noon peak hours, while upholding the timely delivery commitment.

Related papers

• Deep Reinforcement Learning based Online Scheduling Policy for Deep Neural Network Multi-Tenant Multi-Accelerator Systems [1.7724466261976437]
  This paper presents RELMAS, a low-overhead deep reinforcement learning algorithm designed for the online scheduling of DNNs in multi-tenant environments. The application of RELMAS to a heterogeneous multi-accelerator system resulted in up to a 173% improvement in SLA satisfaction rate.
  arXiv  Detail & Related papers  (2024-04-13T10:13:07Z)
• Neural Approximate Dynamic Programming for the Ultra-fast Order Dispatching Problem [1.519321208145928]
  We focus on the ultrafast Order Dispatch Problem (ODP), which involves matching and dispatching orders to couriers within a centralized warehouse setting. We introduce important extensions to ultra-fast ODP such as order policies and explicit courier assignments to provide a more realistic representation of operations and improve delivery efficiency. We test our proposed approach using four distinct realistic datasets tailored for ODP and compare the performance of NeurADP against myopic and DRL baselines.
  arXiv  Detail & Related papers  (2023-11-21T20:23:58Z)
• Multi-Agent Learning of Efficient Fulfilment and Routing Strategies in E-Commerce [11.421159751635667]
  paper presents an integrated algorithmic framework for minimising product delivery costs in e-commerce. One of the major challenges in e-commerce is the large volume of-temporally diverse orders from multiple customers. We propose an approach that combines graph neural networks and reinforcement learning to train the node selection and vehicle agents.
  arXiv  Detail & Related papers  (2023-11-20T10:32:28Z)
• A Fast Task Offloading Optimization Framework for IRS-Assisted Multi-Access Edge Computing System [14.82292289994152]
  We propose a deep learning-based optimization framework called Iterative Order-Preserving policy Optimization (IOPO) IOPO enables the generation of energy-efficient task-offloading decisions within milliseconds. Experimental results demonstrate that the proposed framework can generate energy-efficient task-offloading decisions within a very short time period.
  arXiv  Detail & Related papers  (2023-07-17T13:32:02Z)
• KAPLA: Pragmatic Representation and Fast Solving of Scalable NN Accelerator Dataflow [0.0]
  We build a generic, optimized, and fast dataflow solver, KAPLA, to explore the design space with effective validity check and efficiency estimation. KAPLA achieves within only 2.2% and 7.7% energy overheads on the result dataflow for training and inference. It also outperforms random and machine-learning-based approaches, with more optimized results and orders of magnitude faster search speedup.
  arXiv  Detail & Related papers  (2023-06-09T03:12:42Z)
• MARLIN: Soft Actor-Critic based Reinforcement Learning for Congestion Control in Real Networks [63.24965775030673]
  We propose a novel Reinforcement Learning (RL) approach to design generic Congestion Control (CC) algorithms. Our solution, MARLIN, uses the Soft Actor-Critic algorithm to maximize both entropy and return. We trained MARLIN on a real network with varying background traffic patterns to overcome the sim-to-real mismatch.
  arXiv  Detail & Related papers  (2023-02-02T18:27:20Z)
• Approaching sales forecasting using recurrent neural networks and transformers [57.43518732385863]
  We develop three alternatives to tackle the problem of forecasting the customer sales at day/store/item level using deep learning techniques. Our empirical results show how good performance can be achieved by using a simple sequence to sequence architecture with minimal data preprocessing effort. The proposed solution achieves a RMSLE of around 0.54, which is competitive with other more specific solutions to the problem proposed in the Kaggle competition.
  arXiv  Detail & Related papers  (2022-04-16T12:03:52Z)
• Semi-supervised Network Embedding with Differentiable Deep Quantisation [81.49184987430333]
  We develop d-SNEQ, a differentiable quantisation method for network embedding. d-SNEQ incorporates a rank loss to equip the learned quantisation codes with rich high-order information. It is able to substantially compress the size of trained embeddings, thus reducing storage footprint and accelerating retrieval speed.
  arXiv  Detail & Related papers  (2021-08-20T11:53:05Z)
• A Deep Value-network Based Approach for Multi-Driver Order Dispatching [55.36656442934531]
  We propose a deep reinforcement learning based solution for order dispatching. We conduct large scale online A/B tests on DiDi's ride-dispatching platform. Results show that CVNet consistently outperforms other recently proposed dispatching methods.
  arXiv  Detail & Related papers  (2021-06-08T16:27:04Z)
• Tailored Learning-Based Scheduling for Kubernetes-Oriented Edge-Cloud System [54.588242387136376]
  We introduce KaiS, a learning-based scheduling framework for edge-cloud systems. First, we design a coordinated multi-agent actor-critic algorithm to cater to decentralized request dispatch. Second, for diverse system scales and structures, we use graph neural networks to embed system state information. Third, we adopt a two-time-scale scheduling mechanism to harmonize request dispatch and service orchestration.
  arXiv  Detail & Related papers  (2021-01-17T03:45:25Z)
• Toward fast and accurate human pose estimation via soft-gated skip connections [97.06882200076096]
  This paper is on highly accurate and highly efficient human pose estimation. We re-analyze this design choice in the context of improving both the accuracy and the efficiency over the state-of-the-art. Our model achieves state-of-the-art results on the MPII and LSP datasets.
  arXiv  Detail & Related papers  (2020-02-25T18:51:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.