In recent years, online ride-hailing services have emerged as an important component of
urban transportation system, which not only provide significant ease for residents' travel …

There has been a paradigm-shift in urban logistic services in the last years; demand for real-
time, instant mobility and delivery services grows. This poses new challenges to logistic …

Recent neural methods for vehicle routing problems always train and test the deep models
on the same instance distribution (ie, uniform). To tackle the consequent cross-distribution …

Applying and adapting deep reinforcement learning (DRL) to tackle transportation problems
is an emerging interdisciplinary field. While rapidly growing, a comprehensive and synthetic …

Failing to sell fresh produce before expiration not only hurts the bottom line of grocery
retailers, but also leads to food waste. This work combines dynamic pricing and information …

Internal combustion engine vehicles, although conventionally playing essential roles in
seaport logistic operation, are major sources of carbon emissions. To guarantee the “green …

In this paper, we present a comprehensive, in-depth survey of the literature on reinforcement
learning approaches to decision optimization problems in a typical ridesharing system …

The rebalancing of idle vehicles is critical to mitigating the supply–demand imbalance in on-
demand ride services. Motivated by a ride service platform, this paper investigates a short …

We study a Markov matching market involving a planner and a set of strategic agents on the
two sides of the market. At each step, the agents are presented with a dynamical context …

In today's economy, it becomes important for Internet platforms to consider the sequential
information design problem to align its long term interest with incentives of the gig service …