1 citations · 1 across the 6 of their papers we have counts for
6 papers
Sequential Offering in On-Demand Platforms: On the Optimality of Greedy Ranking
Hongyao Ma, Will Ma, Matias Romero
On-demand platforms face the fundamental challenge of fulfilling time-sensitive jobs with independent workers who may decline offers. To minimize delays and unfulfilled jobs, platf…
RLCascadeRouter: Quality-Estimator-Free Cascade Routing via Reinforcement Learning
Shihong Huang, Shengjie Wang, Hong Ma +1
The growing ecosystem of large language models (LLMs) offers huge potential to optimize performance-cost trade-offs. However, their heterogeneous capabilities and inference costs m…
Vehicle-as-Prompt: A Unified Deep Reinforcement Learning Framework for Heterogeneous Fleet Vehicle Routing Problem
Shihong Huang, Shengjie Wang, Lei Gao +4
Unlike traditional homogeneous routing problems, the Heterogeneous Fleet Vehicle Routing Problem (HFVRP) involves heterogeneous fixed costs, variable travel costs, and capacity con…
One4Many-StablePacker: An Efficient Deep Reinforcement Learning Framework for the 3D Bin Packing Problem
Lei Gao, Shihong Huang, Shengjie Wang +5
The three-dimensional bin packing problem (3D-BPP) is widely applied in logistics and warehousing. Existing learning-based approaches often neglect practical stability-related cons…
Potential-Based Greedy Matching for Dynamic Delivery Pooling
Hongyao Ma, Will Ma, Matias Romero
We study the dynamic pooling of multiple orders into a single trip, a strategy widely adopted by online delivery platforms. When an order has to be dispatched, the platform must de…
Iterative Network Pricing for Ridesharing Platforms
Chenkai Yu, Hongyao Ma
Ridesharing platforms match riders and drivers, using dynamic pricing to balance supply and demand. The origin-based "surge pricing", however, does not take into consideration mark…