Introduction Due to the menacing increase in the number of vehicles on a daily basis,
abating road congestion is becoming a key challenge these years. To cope-up with the …

The rapid development of urban cities coupled with the rise in population has led to an
exponentially growing number of vehicles on the roads for the latter to commute. This is …

This paper provides a novel intelligent scheduling strategy for a real-world transportation
dynamic scheduling case from an engine workshop of general motor company (GMEW) …

Knowledge transfer plays a vastly important role in solving multitask optimization problems
(MTOPs). Many existing methods transfer task-specific knowledge, such as the high-quality …

This paper addresses urban traffic light scheduling problems (UTLSP) with eight phases.
The objective is to minimize the total vehicle delay time by assigning traffic phases and …

An urban traffic light scheduling problem (UTLSP) is studied by using problem feature based
meta-heuristics with Q-learning. The goal is to minimize the network-wise total delay time …

With the increasing demand for surgeries, surgery scheduling become an important problem
in hospital management. Efficient surgery scheduling can enhance the optimal use of …

A new trend of using deep reinforcement learning for traffic signal control has become a
spotlight in the Intelligent Transportation System (ITS). However, the traditional intelligent …

The exponential increase in vehicles, quick urbanization, and rising demand for
transportation are straining the world's road infrastructure today. To have a sustainable …

The usage of social media around the world is ever-increasing. Social media statistics from
2019 show that there are 3.5 billion social media users worldwide. However, the existence …