This paper proposes a deep reinforcement learning (DRL)-based approach to the dynamic
on-demand crowdshipping problem in which requests constantly arrive in a crowdshipping …

Customized bus can meet many passengers' personalized travel demand in a public
transportation system by providing an innovative shared travel service. Customized bus …

The mathematical methods developed so far for addressing truck dispatching problems in
fleet management systems (FMSs) of open-pit mines fail to capture the autonomy and …

The paper introduces a transformer-based deep reinforcement learning (T-DRL) approach
designed to address the multiobjective multihydropower reservoir operation optimization …

Effective scheduling of multiple agricultural machines in emergencies can reduce crop
losses to a great extent. In this paper, cooperative scheduling based on deep reinforcement …

Many ports worldwide continue to expand their capacity by developing a multiterminal
system to catch up with the global containerized trade demand. However, this expansion …

Abstract Vehicle Routing Problem (VRP) is a widespread problem in the transportation field,
which challenges the intelligent level of vehicle decisions. Multi‐Trip Vehicle Routing …

This article presents a novel algorithm based on the cluster first-route second method, which
executes a solution through K-means and Optics clustering techniques and Nearest …

This paper proposes a real-life application of deep reinforcement learning to address the
order dispatching problem of a Turkish ultra-fast delivery company, Getir. Before applying off …

Traffic congestion is deteriorating the sustainability and livability of the metropolis. However,
most existing solutions are expected utility-based, ie, making routing decisions based on …