Opportunities for reinforcement learning in stochastic dynamic vehicle routing
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 …
time, instant mobility and delivery services grows. This poses new challenges to logistic …
Deep reinforcement learning in transportation research: A review
Applying and adapting deep reinforcement learning (DRL) to tackle transportation problems
is an emerging interdisciplinary field. While rapidly growing, a comprehensive and synthetic …
is an emerging interdisciplinary field. While rapidly growing, a comprehensive and synthetic …
Analytics and machine learning in vehicle routing research
The Vehicle Routing Problem (VRP) is one of the most intensively studied combinatorial
optimisation problems for which numerous models and algorithms have been proposed. To …
optimisation problems for which numerous models and algorithms have been proposed. To …
An overview and experimental study of learning-based optimization algorithms for the vehicle routing problem
The vehicle routing problem (VRP) is a typical discrete combinatorial optimization problem,
and many models and algorithms have been proposed to solve the VRP and its variants …
and many models and algorithms have been proposed to solve the VRP and its variants …
Or-gym: A reinforcement learning library for operations research problems
Reinforcement learning (RL) has been widely applied to game-playing and surpassed the
best human-level performance in many domains, yet there are few use-cases in industrial or …
best human-level performance in many domains, yet there are few use-cases in industrial or …
Deep inventory management
This work provides a Deep Reinforcement Learning approach to solving a periodic review
inventory control system with stochastic vendor lead times, lost sales, correlated demand …
inventory control system with stochastic vendor lead times, lost sales, correlated demand …
Reinforcement learning for ridesharing: An extended survey
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 …
learning approaches to decision optimization problems in a typical ridesharing system …
A survey of dynamic pickup and delivery problems
Due to the ubiquitous real-world applications of logistics and supply chain management
over the past two decades, dynamic pickup and delivery problems (DPDPs), as a subclass …
over the past two decades, dynamic pickup and delivery problems (DPDPs), as a subclass …
Rl4co: an extensive reinforcement learning for combinatorial optimization benchmark
We introduce RL4CO, an extensive reinforcement learning (RL) for combinatorial
optimization (CO) benchmark. RL4CO employs state-of-the-art software libraries as well as …
optimization (CO) benchmark. RL4CO employs state-of-the-art software libraries as well as …
Learning to optimize industry-scale dynamic pickup and delivery problems
X Li, W Luo, M Yuan, J Wang, J Lu… - 2021 IEEE 37th …, 2021 - ieeexplore.ieee.org
The Dynamic Pickup and Delivery Problem (DPDP) is aimed at dynamically scheduling
vehicles among multiple sites in order to minimize the cost when delivery orders are not …
vehicles among multiple sites in order to minimize the cost when delivery orders are not …