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1 INTRODUCTION

2 BACKGROUND
2.1 Reinforcement Learning
2.2 Application Overview
2.3 Relationship between Two Stages

3 STAGE 1: DISPATCHING
3.1 Order Matching
3.2 Fleet Management
3.3 Joint Scheduling of Order Matching and Fleet Management

4 STAGE 2: ROUTING
4.1 Formulation of Typical CVRP
4.2 Realistic Routing Problems
4.3 Conventional Methods for Routing

5 OPEN SIMULATORS AND DATASETS FOR DDS

6 CHALLENGES
6.1 Coupled Spatial-Temporal Representations
6.2 Fleet Heterogeneous
6.3 Variant Constraints in MDP Modeling
6.4 Large-Scale Deployment
6.5 Dynamics and Real-time Scheduling

7 OPEN PROBLEMS
7.1 Advanced DRL Methods for DDS
7.2 Joint Optimization of Two Stages
7.3 Fairness from Workers’ Perspective
7.4 Partial Compliance Consideration
7.5 Pricing Problems
7.6 Simulation Environments
7.7 Large-Scale Online Scheduling System

8 CONCLUSION
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