Vehicle route planning in e-waste mobile collection on demand supported by artificial intelligence algorithms

2018 ◽  
Vol 63 ◽  
pp. 1-22 ◽  
Author(s):  
Piotr Nowakowski ◽  
Krzysztof Szwarc ◽  
Urszula Boryczka
Keyword(s):  
Artificial Intelligence ◽  
Route Planning ◽  
On Demand ◽  
Vehicle Route

Commercial Vehicle Route Planning Design Based on Partition Coding Genetic Algorithm

2020 ◽  
Author(s):  
Kailang Chen ◽  
Hanyu Zhang ◽  
Xin Huang ◽  
Long Chen ◽  
Qi Shi
Keyword(s):  
Genetic Algorithm ◽  
Commercial Vehicle ◽  
Route Planning ◽  
Vehicle Route

scholarly journals An evolutionary approach to the vehicle route planning in e-waste mobile collection on demand

2021 ◽  
Vol 25 (8) ◽  
pp. 6665-6680
Author(s):  
Krzysztof Szwarc ◽  
Piotr Nowakowski ◽  
Urszula Boryczka
Keyword(s):  
Tabu Search ◽  
Quality Evaluation ◽  
Memetic Algorithm ◽  
Route Planning ◽  
Memetic Algorithms ◽  
Approximate Methods ◽  
On Demand ◽  
Suboptimal Solutions ◽  
Novel Method ◽  
The Given

AbstractThe article discusses the utilitarian problem of the mobile collection of waste electrical and electronic equipment. Due to its $$\mathcal {NP}$$ NP -hard nature, implies the application of approximate methods to discover suboptimal solutions in an acceptable time. The paper presents the proposal of a novel method of designing the Evolutionary and Memetic Algorithms, which determine favorable route plans. The recommended methods are determined using quality evaluation indicators for the techniques applied herein, subject to the limits characterizing the given company. The proposed Memetic Algorithm with Tabu Search provides much better results than the metaheuristics described in the available literature.

Artificial Intelligence Review

2019 ◽  
pp. 23-39
Author(s):  
Amal Kilani ◽  
Ahmed Ben Hamida ◽  
Habib Hamam
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Intelligent System ◽  
Route Planning ◽  
Turing Test ◽  
Future Research ◽  
Interaction Data ◽  
Research Directions ◽  
Future Research Directions

In this chapter, the authors present a profound literature review of artificial intelligence (AI). After defining it, they briefly cover its history and enumerate its principal fields of application. They name, for example, information system, commerce, image processing, human-computer interaction, data compression, robotics, route planning, etc. Moreover, the test that defines an artificially intelligent system, called the Turing test, is also defined and detailed. Afterwards, the authors describe some AI tools such as fuzzy logic, genetic algorithms, and swarm intelligence. Special attention will be given to neural networks and fuzzy logic. The authors also present the future research directions and ethics.

The platform as factory: Crowdwork and the hidden labour behind artificial intelligence

2020 ◽  
Vol 44 (2) ◽  
pp. 145-158 ◽  
Author(s):  
Moritz Altenried
Keyword(s):  
Artificial Intelligence ◽  
Automated Measurement ◽  
On Demand ◽  
Wage Labour ◽  
Production Development ◽  
Crucial Component ◽  
The World ◽  
Domestic Labour ◽  
Private Homes ◽  
And Control

This article analyses crowdwork platforms where various forms of digital labour are outsourced to digital workers across the globe. The labour of these workers is, among other things, a crucial component in the production, development and support of artificial intelligence. Crowdwork platforms are an extreme example of new forms of automated measurement, management and control of labour allowing, in turn, for the creation of hyperflexible and highly scalable workforces. Particularly on so-called microtask platforms, work is characterised by decomposition, standardisation, automated management and surveillance, as well as algorithmically organised cooperation between a great number of workers. Analysing these platforms as a paradigmatic example of an emerging digital Taylorism, the article goes on to argue that this allows the platforms to assemble a deeply heterogeneous set of workers while bypassing the need to spatially and subjectively homogenise them. These platforms create a global on-demand workforce, working in their private homes or Internet cafes. As a result, crowdwork taps into labour pools hitherto almost inaccessible to wage labour. The second part of the article investigates this tendency by looking at two sets of workers: women shouldering care responsibilities, who now can work on crowdwork platforms while performing domestic labour, as well as digital workers in the Global South. While there are clear specifics of digital crowdwork, it is also an expression of broader transformations within the world of work, concerning, for example, new forms of algorithmic management just as the return of very old forms of exploitation such as the piece wage.

scholarly journals An artificial intelligence based crowdsensing solution for on-demand accident scene monitoring

2020 ◽  
Vol 170 ◽  
pp. 303-310
Author(s):  
May El Barachi ◽  
Faouzi Kamoun ◽  
Jannatul Ferdaos ◽  
Mouna Makni ◽  
Imed Amri
Keyword(s):  
Artificial Intelligence ◽  
On Demand ◽  
Accident Scene
Sign in / Sign up

Export Citation Format

Share Document