activity
20162024
most citedA Framework for the Joint Optimization of Assignment and Pricing in Mobility-on-Demand Systems with Shared Rides

3 citations · 6 across the 7 of their papers we have counts for

collaborators

7 papers

math.OC2024

Optimization research on shared bicycle route planning problem

Qiwei Luo

In bicycle share networks, the balance between demand and supply is disrupted. As a result, shared resources are wasted and management costs for operators increase. Therefore, in t…

math.OC2022

Dynamic Order Fulfillment in Last-Mile Drone Delivery under Demand Uncertainty

Linxuan Shi, Zhengtian Xu, Miguel Lejeune +1

Drones have attracted growing interest in last-mile delivery due to their potential to significantly reduce costs and enhance operational flexibility, particularly in areas of spar…

eess.SY2021★ 3 cited

A Framework for the Joint Optimization of Assignment and Pricing in Mobility-on-Demand Systems with Shared Rides

Yang Liu, Qi Luo, Raga Gopalakrishnan +1

Mobility-on-Demand (MoD) systems have become a fixture in urban transportation networks, with the rapid growth of ride-hailing services such as Uber and Lyft. Ride-hailing is typic…

math.OC2021★ 3 cited

Efficient Algorithms for Stochastic Ridepooling Assignment with Mixed Fleets

Qi Luo, Viswanath Nagarajan, Alexander Sundt +3

Ride-pooling, which accommodates multiple passenger requests in a single trip, has the potential to significantly increase fleet utilization in shared mobility platforms. The ride-…

cs.CE2021

Multimodal Mobility Systems: Joint Optimization of Transit Network Design and Pricing

Qi Luo, Samitha Samaranayake, Siddhartha Banerjee

The performance of multimodal mobility systems relies on the seamless integration of conventional mass transit services and the advent of Mobility-on-Demand (MoD) services. Prior w…

econ.EM2017

A Note on the Multi-Agent Contracts in Continuous Time

Qi Luo, Romesh Saigal

Dynamic contracts with multiple agents is a classical decentralized decision-making problem with asymmetric information. In this paper, we extend the single-agent dynamic incentive…