Constraint Programming Algorithms for Route Planning Exploiting
Geometrical Information
- URL: http://arxiv.org/abs/2009.10253v1
- Date: Tue, 22 Sep 2020 00:51:45 GMT
- Title: Constraint Programming Algorithms for Route Planning Exploiting
Geometrical Information
- Authors: Alessandro Bertagnon (University of Ferrara)
- Abstract summary: We present an overview of our current research activities concerning the development of new algorithms for route planning problems.
The research so far has focused in particular on the Euclidean Traveling Salesperson Problem (Euclidean TSP)
The aim is to exploit the results obtained also to other problems of the same category, such as the Euclidean Vehicle Problem (Euclidean VRP), in the future.
- Score: 91.3755431537592
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Problems affecting the transport of people or goods are plentiful in industry
and commerce and they also appear to be at the origin of much more complex
problems. In recent years, the logistics and transport sector keeps growing
supported by technological progress, i.e. companies to be competitive are
resorting to innovative technologies aimed at efficiency and effectiveness.
This is why companies are increasingly using technologies such as Artificial
Intelligence (AI), Blockchain and Internet of Things (IoT). Artificial
intelligence, in particular, is often used to solve optimization problems in
order to provide users with the most efficient ways to exploit available
resources. In this work we present an overview of our current research
activities concerning the development of new algorithms, based on CLP
techniques, for route planning problems exploiting the geometric information
intrinsically present in many of them or in some of their variants. The
research so far has focused in particular on the Euclidean Traveling
Salesperson Problem (Euclidean TSP) with the aim to exploit the results
obtained also to other problems of the same category, such as the Euclidean
Vehicle Routing Problem (Euclidean VRP), in the future.
Related papers
- Blockchain-based AI Methods for Managing Industrial IoT: Recent Developments, Integration Challenges and Opportunities [3.3030080038744947]
Authors present a comprehensive survey on the AI approaches with BC in the smart IIoT.
We focus on state-of-the-art overviews regarding AI, BC, and smart IoT applications.
We highlight the various issues--security, stability, scalability, and confidentiality.
arXiv Detail & Related papers (2024-05-21T07:34:49Z) - Introduction to Algogens [0.0]
Algogens is a promising integration of generative AI with traditional algorithms.
The book explores the basics of Algogens, their development, applications, and advantages.
It offers a balanced look at the prospects and obstacles facing Algogens.
arXiv Detail & Related papers (2024-03-03T07:52:10Z) - A Survey of Generative AI for Intelligent Transportation Systems: Road Transportation Perspective [7.770651543578893]
We introduce the principles of different generative AI techniques.
We classify tasks in ITS into four types: traffic perception, traffic prediction, traffic simulation, and traffic decision-making.
We illustrate how generative AI techniques addresses key issues in these four different types of tasks.
arXiv Detail & Related papers (2023-12-13T16:13:23Z) - The Efficiency Spectrum of Large Language Models: An Algorithmic Survey [54.19942426544731]
The rapid growth of Large Language Models (LLMs) has been a driving force in transforming various domains.
This paper examines the multi-faceted dimensions of efficiency essential for the end-to-end algorithmic development of LLMs.
arXiv Detail & Related papers (2023-12-01T16:00:25Z) - AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities
and Challenges [60.56413461109281]
Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes.
We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful.
We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions.
arXiv Detail & Related papers (2023-04-10T15:38:12Z) - Selected Trends in Artificial Intelligence for Space Applications [69.3474006357492]
This chapter focuses on differentiable intelligence and on-board machine learning.
We discuss a few selected projects originating from the European Space Agency's (ESA) Advanced Concepts Team (ACT)
arXiv Detail & Related papers (2022-12-10T07:49:50Z) - Physical Computing for Materials Acceleration Platforms [81.09376948478891]
We argue that the same simulation and AI tools that will accelerate the search for new materials, as part of the MAPs research program, also make possible the design of fundamentally new computing mediums.
We outline a simulation-based MAP program to design computers that use physics itself to solve optimization problems.
We expect to introduce a new era of innovative collaboration between materials researchers and computer scientists.
arXiv Detail & Related papers (2022-08-17T23:03:54Z) - Big Data Analytics Applying the Fusion Approach of Multicriteria
Decision Making with Deep Learning Algorithms [0.0]
Multicriteria based decision making is one of the key issues to solve for various issues related to the alternative effects in big data analysis.
It tends to find a solution based on the latest machine learning techniques that include algorithms like decision making and deep learning mechanism based on multicriteria.
In essence, several fields, including business, agriculture, information technology, and computer science, use deep learning and multicriteria-based decision-making problems.
arXiv Detail & Related papers (2021-02-02T05:56:03Z) - Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper [50.25428141435537]
Artificial Intelligence for IT Operations (AIOps) is an emerging interdisciplinary field arising in the intersection between machine learning, big data, streaming analytics, and the management of IT operations.
Main aim of the AIOPS workshop is to bring together researchers from both academia and industry to present their experiences, results, and work in progress in this field.
arXiv Detail & Related papers (2021-01-15T10:43:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.