Toward a modern last-mile delivery: Consequences and obstacles of intelligent technology
S Sorooshian, S Khademi Sharifabad… - Applied System …, 2022 - mdpi.com
Last-mile delivery (LMD) is essential in supply chains, and providers of logistics services are
aware that they must adapt to changing customer and society expectations, competition …
aware that they must adapt to changing customer and society expectations, competition …
Public transport for smart cities: Recent innovations and future challenges
The idea of a smart city is one that utilises Internet-of-Things (IoT) technologies and data
analytics to optimise the efficiency of city operations and services, so as to provide a high …
analytics to optimise the efficiency of city operations and services, so as to provide a high …
[HTML][HTML] AIS data-driven ship trajectory prediction modelling and analysis based on machine learning and deep learning methods
Maritime transport faces new safety challenges in an increasingly complex traffic
environment caused by large-scale and high-speed ships, particularly with the introduction …
environment caused by large-scale and high-speed ships, particularly with the introduction …
Operational Research: methods and applications
Abstract Throughout its history, Operational Research has evolved to include methods,
models and algorithms that have been applied to a wide range of contexts. This …
models and algorithms that have been applied to a wide range of contexts. This …
Vehicle routing problems with drones equipped with multi-package payload compartments
MA Masmoudi, S Mancini, R Baldacci… - … Research Part E: Logistics …, 2022 - Elsevier
The vehicle routing problem with drones (VRP-D) consists of designing combined truck-
drone routes and schedules to serve a set of customers with specific requests and time …
drone routes and schedules to serve a set of customers with specific requests and time …
Towards efficient airline disruption recovery with reinforcement learning
Disruptions to airline schedules precipitate flight delays/cancellations and significant losses
for airline operations. The goal of the integrated airline recovery problem is to develop an …
for airline operations. The goal of the integrated airline recovery problem is to develop an …
[HTML][HTML] Deep bi-directional information-empowered ship trajectory prediction for maritime autonomous surface ships
It is critical to have accurate ship trajectory prediction for collision avoidance and intelligent
traffic management of manned ships and emerging Maritime Autonomous Surface Ships …
traffic management of manned ships and emerging Maritime Autonomous Surface Ships …
Deep reinforcement learning-based active flow control of vortex-induced vibration of a square cylinder
We mitigate vortex-induced vibrations of a square cylinder at a Reynolds number of 100
using deep reinforcement learning (DRL)-based active flow control (AFC). The proposed …
using deep reinforcement learning (DRL)-based active flow control (AFC). The proposed …
Neural multi-objective combinatorial optimization with diversity enhancement
Most of existing neural methods for multi-objective combinatorial optimization (MOCO)
problems solely rely on decomposition, which often leads to repetitive solutions for the …
problems solely rely on decomposition, which often leads to repetitive solutions for the …
[HTML][HTML] Reinforcement learning for humanitarian relief distribution with trucks and UAVs under travel time uncertainty
Effective humanitarian relief operations are challenging in the aftermath of disasters, as
trucks are often faced with considerable travel time uncertainties due to damaged …
trucks are often faced with considerable travel time uncertainties due to damaged …