Optimization of collaborative multi-depot pickup and delivery logistics networks (CMDPDLN)
with split loads and time windows involves a customer demand splitting strategy and a multi …

We propose a deep reinforcement learning (RL) method to learn large neighborhood search
(LNS) policy for integer programming (IP). The RL policy is trained as the destroy operator to …

The satellite range scheduling problem (SRSP) is a range of combinatory optimization,
which plays a vital role in the regular operation and mission accomplishment of in-orbit …

We propose a manager-worker framework (the implementation of our model is publically
available at: https://github. com/zcaicaros/manager-worker-mtsptwr) based on deep …

Abstract Cooperative Pickup and Delivery Problem (PDP), as a variant of the typical Vehicle
Routing Problems (VRP), is an important formulation in many real-world applications, such …

Multiple robots complete a cooperative hunting task by obtaining environmental information
and autonomously learning hunting decision-making strategies. However, with the increase …

We present an efficient Neural Neighborhood Search (N2S) approach for pickup and
delivery problems (PDPs). In specific, we design a powerful Synthesis Attention that allows …

This paper provides a systematic overview of machine learning methods applied to solve NP-
hard Vehicle Routing Problems (VRPs). Recently, there has been a great interest from both …

Recently, there is a growing attention on applying deep reinforcement learning (DRL) to
solve the 3-D bin packing problem (3-D BPP). However, due to the relatively less informative …

In marine container terminals, truck dispatching optimization is often considered as the
primary focus as it provides crucial synergy between the sea-side operations and yard-side …