Enhancing Courier Scheduling in Crowdsourced Last-Mile Delivery through Dynamic Shift Extensions: A Deep Reinforcement Learning Approach

📄 arXiv: 2402.09961v1 📥 PDF

作者: Zead Saleh, Ahmad Al Hanbali, Ahmad Baubaid

分类: cs.LG

发布日期: 2024-02-15

💡 一句话要点

通过动态班次延长提升众包最后一公里配送调度效率

🎯 匹配领域: 支柱二：RL算法与架构 (RL & Architecture)

关键词: 众包配送 调度优化 深度强化学习 动态调整 班次延长 物流管理 智能交通

📋 核心要点

  1. 现有众包配送平台在调度快递员与订单时面临需求不可预测性，导致调度效率低下。
  2. 本文提出通过动态班次延长来调整承诺快递员的离线调度，以最大化平台利润。
  3. 实验结果显示，允许班次延长显著提高了奖励，减少了订单损失和请求丢失，验证了DQN算法的有效性。

📝 摘要（中文）

众包配送平台面临复杂的调度挑战，需要将快递员与客户订单匹配。本文考虑了两类众包快递员：承诺快递员和偶尔快递员，二者有不同的补偿机制。由于需求的不可预测性，平台需要对离线调度进行在线调整。研究聚焦于通过班次延长动态调整承诺快递员的离线调度，目标是最大化平台利润。为此，提出了一种深度Q网络（DQN）学习方法。与不允许班次延长的基线策略相比，结果表明允许班次延长可以显著提高奖励，减少订单损失和请求丢失。此外，灵敏度分析显示，总补偿与请求到达率呈非线性关系，而与偶尔快递员到达率呈线性关系。这些发现证明了DQN算法成功学习了这些动态特性。

🔬 方法详解

问题定义：本文解决的是众包配送平台在面对不可预测需求时，如何动态调整承诺快递员的离线调度问题。现有方法通常依赖于静态调度，无法应对实时变化的需求，导致效率低下和资源浪费。

核心思路：论文的核心思路是通过动态班次延长来优化快递员的调度，利用深度Q网络（DQN）算法进行学习和决策，以实现利润最大化。这样的设计使得平台能够灵活应对需求波动，提高整体调度效率。

技术框架：整体架构包括需求预测模块、离线调度生成模块和动态调整模块。需求预测模块用于预测未来的订单需求，离线调度生成模块基于预测结果制定初步调度，而动态调整模块则根据实时数据进行班次延长和订单分配。

关键创新：最重要的技术创新在于将深度强化学习应用于众包配送调度中，尤其是动态班次延长的策略。这与传统的静态调度方法形成鲜明对比，能够更好地适应需求变化。

关键设计：在DQN算法中，设置了特定的奖励函数来鼓励班次延长，并设计了网络结构以处理高维状态空间。损失函数采用均方误差，确保学习过程的稳定性和收敛性。

📊 实验亮点

实验结果表明，允许班次延长的策略相比于基线策略，奖励提升了显著，减少了订单损失和请求丢失。具体而言，正常场景下的班次延长平均次数最高，丢失请求数量最少，验证了DQN算法在动态调度中的有效性。

🎯 应用场景

该研究的潜在应用领域包括众包配送、物流管理和智能交通系统。通过优化调度策略，平台可以提高服务效率，降低运营成本，进而提升用户满意度和市场竞争力。未来，该方法还可以扩展到其他动态调度问题，如共享出行和货物运输等。

📄 摘要（原文）

Crowdsourced delivery platforms face complex scheduling challenges to match couriers and customer orders. We consider two types of crowdsourced couriers, namely, committed and occasional couriers, each with different compensation schemes. Crowdsourced delivery platforms usually schedule committed courier shifts based on predicted demand. Therefore, platforms may devise an offline schedule for committed couriers before the planning period. However, due to the unpredictability of demand, there are instances where it becomes necessary to make online adjustments to the offline schedule. In this study, we focus on the problem of dynamically adjusting the offline schedule through shift extensions for committed couriers. This problem is modeled as a sequential decision process. The objective is to maximize platform profit by determining the shift extensions of couriers and the assignments of requests to couriers. To solve the model, a Deep Q-Network (DQN) learning approach is developed. Comparing this model with the baseline policy where no extensions are allowed demonstrates the benefits that platforms can gain from allowing shift extensions in terms of reward, reduced lost order costs, and lost requests. Additionally, sensitivity analysis showed that the total extension compensation increases in a nonlinear manner with the arrival rate of requests, and in a linear manner with the arrival rate of occasional couriers. On the compensation sensitivity, the results showed that the normal scenario exhibited the highest average number of shift extensions and, consequently, the fewest average number of lost requests. These findings serve as evidence of the successful learning of such dynamics by the DQN algorithm.