Cookies
O website necessita de alguns cookies e outros recursos semelhantes para funcionar. Caso o permita, o INESC TEC irá utilizar cookies para recolher dados sobre as suas visitas, contribuindo, assim, para estatísticas agregadas que permitem melhorar o nosso serviço. Ver mais
Aceitar Rejeitar
  • Menu
Publicações

Publicações por CESE

2022

A Novel Discrete Particle Swarm Optimization Algorithm for the Travelling Salesman Problems

Autores
Sequeiros, JA; Silva, R; Santos, AS; Bastos, J; Varela, MLR; Madureira, AM;

Publicação
INNOVATIONS IN INDUSTRIAL ENGINEERING

Abstract
There are Optimization Problems that are too complex to be solved efficiently by deterministic methods. For these problems, where deterministic methods have proven to be inefficient, if not completely unusable, it is common to use approximate methods, that is, optimization methods that solve the problems quickly, regardless of their size or complexity, even if they do not guarantee optimal solutions. In other words, methods that find “acceptable” solutions, efficiently. One particular type of approximate method, which is particularly effective in complex problems, are metaheuristics. Particle Swarm Optimization is a population-based metaheuristic, which has been particularly successful. In order to broaden the application and overcome the limitation of Particle Swarm Optimization, a discrete version of the metaheuristics is proposed. The Discrete Particle Swarm Optimization, DPSO, will change the PSO algorithm so it can be applied to discrete optimization problems. This alteration will focus on the velocity update equation. The DPSO was tested in an instance of the Traveling Salesman Problem, att48, 48 points problems proposed by Padberg and Rinaldi, which showed some promising results. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2022

Manufacturing and Management Paradigms, Methods and Tools for Sustainable Industry 4.0-Oriented Manufacturing Systems

Autores
Varela, L; Avila, P; Castro, H; Putnik, GD; Fonseca, LMC; Ferreira, L;

Publicação
SUSTAINABILITY

Abstract
In the current Industry 4 [...]

2022

Real-Time Detection of Vehicle-Based Logistics Operations

Autores
Ribeiro, J; Tavares, J; Fontes, T;

Publicação
INTELLIGENT TRANSPORT SYSTEMS (INTSYS 2021)

Abstract

2022

A Tool for Air Cargo Planning and Distribution

Autores
Costa, D; Santos, AS; Bastos, JA; Madureira, AM; Brito, MF;

Publicação
INNOVATIONS IN BIO-INSPIRED COMPUTING AND APPLICATIONS, IBICA 2021

Abstract
In this paper, a decision support application, for the air cargo planning and distribution, is proposed. The freight forwarding sector has been working to be assertive and efficient in responding to the market through an efficient approach to planning and allocation problems. The main goal is to minimize costs and improve performance. A real air cargo distribution problem for a freight forwarder was addressed. This project emerged from the need to efficiently plan and minimize costs for the distribution of thousands of m(3) (cubic meters) of air cargo, while considering the market restrictions, such as aircraft availability and transportation fees. Through the GRG algorithm adaptation to the real problem, it was possible to respond to the main goal of this paper. The development of an easy-to-use application ensures a quick response in the air distribution planning, focusing on cost reduction in transportation. With the application development it is possible to obtain real earnings with immediate effect.

2022

Detection of vehicle-based operations from geolocation data

Autores
Tavares, J; Ribeiro, J; Fontes, T;

Publicação
Transportation Research Procedia

Abstract

2022

Knowledge-based decision intelligence in street lighting management

Autores
Sousa, C; Teixeira, D; Carneiro, D; Nunes, D; Novais, P;

Publicação
INTEGRATED COMPUTER-AIDED ENGINEERING

Abstract
As the availability of computational power and communication technologies increases, Humans and systems are able to tackle increasingly challenging decision problems. Taking decisions over incomplete visions of a situation is particularly challenging and calls for a set of intertwined skills that must be put into place under a clear rationale. This work addresses how to deliver autonomous decisions for the management of a public street lighting network, to optimize energy consumption without compromising light quality patterns. Our approach is grounded in an holistic methodology, combining semantic and Artificial Intelligence principles to define methods and artefacts for supporting decisions to be taken in the context of an incomplete domain. That is, a domain with absence of data and of explicit domain assertions.

  • 1
  • 134