23 research outputs found
The bi-objective travelling salesman problem with profits and its connection to computer networks.
This is an interdisciplinary work in Computer Science and Operational Research. As it is
well known, these two very important research fields are strictly connected. Among other
aspects, one of the main areas where this interplay is strongly evident is Networking. As far
as most recent decades have seen a constant growing of every kind of network computer connections,
the need for advanced algorithms that help in optimizing the network performances
became extremely relevant. Classical Optimization-based approaches have been deeply studied
and applied since long time. However, the technology evolution asks for more flexible and
advanced algorithmic approaches to model increasingly complex network configurations. In
this thesis we study an extension of the well known Traveling Salesman Problem (TSP): the
Traveling Salesman Problem with Profits (TSPP). In this generalization, a profit is associated
with each vertex and it is not necessary to visit all vertices. The goal is to determine
a route through a subset of nodes that simultaneously minimizes the travel cost and maximizes
the collected profit. The TSPP models the problem of sending a piece of information
through a network where, in addition to the sending costs, it is also important to consider
what “profit” this information can get during its routing. Because of its formulation, the
right way to tackled the TSPP is by Multiobjective Optimization algorithms. Within this
context, the aim of this work is to study new ways to solve the problem in both the exact
and the approximated settings, giving all feasible instruments that can help to solve it, and
to provide experimental insights into feasible networking instances
Problemas de localização-distribuição de serviços semiobnóxios: aproximações e apoio à decisão
Doutoramento em Gestão IndustrialA presente tese resulta de um trabalho de investigação cujo objectivo se
centrou no problema de localização-distribuição (PLD) que pretende abordar,
de forma integrada, duas actividades logísticas intimamente relacionadas: a
localização de equipamentos e a distribuição de produtos.
O PLD, nomeadamente a sua modelação matemática, tem sido estudado na
literatura, dando origem a diversas aproximações que resultam de diferentes
cenários reais. Importa portanto agrupar as diferentes variantes por forma a
facilitar e potenciar a sua investigação. Após fazer uma revisão e propor uma
taxonomia dos modelos de localização-distribuição, este trabalho foca-se na
resolução de alguns modelos considerados como mais representativos. É feita
assim a análise de dois dos PLDs mais básicos (os problema capacitados com
procura nos nós e nos arcos), sendo apresentadas, para ambos, propostas de
resolução. Posteriormente, é abordada a localização-distribuição de serviços
semiobnóxios. Este tipo de serviços, ainda que seja necessário e
indispensável para o público em geral, dada a sua natureza, exerce um efeito
desagradável sobre as comunidades contíguas. Assim, aos critérios
tipicamente utilizados na tomada de decisão sobre a localização destes
serviços (habitualmente a minimização de custo) é necessário adicionar
preocupações que reflectem a manutenção da qualidade de vida das regiões
que sofrem o impacto do resultado da referida decisão.
A abordagem da localização-distribuição de serviços semiobnóxios requer
portanto uma análise multi-objectivo. Esta análise pode ser feita com recurso a
dois métodos distintos: não interactivos e interactivos. Ambos são abordados
nesta tese, com novas propostas, sendo o método interactivo proposto
aplicável a outros problemas de programação inteira mista multi-objectivo.
Por último, é desenvolvida uma ferramenta de apoio à decisão para os
problemas abordados nesta tese, sendo apresentada a metodologia adoptada
e as suas principais funcionalidades. A ferramenta desenvolvida tem grandes
preocupações com a interface de utilizador, visto ser direccionada para
decisores que tipicamente não têm conhecimentos sobre os modelos
matemáticos subjacentes a este tipo de problemas.This thesis main objective is to address the location-routing problem (LRP)
which intends to tackle, using an integrated approach, two highly related
logistics activities: the location of facilities and the distribution of materials.
The LRP, namely its mathematical formulation, has been studied in the
literature, and several approaches have emerged, corresponding to different
real-world scenarios. Therefore, it is important to identify and group the
different LRP variants, in order to segment current research and foster future
studies. After presenting a review and a taxonomy of location-routing models,
the following research focuses on solving some of its variants. Thus, a study of
two of the most basic LRPs (capacitated problems with demand either on the
nodes or on the arcs) is performed, and new approaches are presented.
Afterwards, the location-routing of semi-obnoxious facilities is addressed.
These are facilities that, although providing useful and indispensible services,
given their nature, bring about an undesirable effect to adjacent communities.
Consequently, to the usual objectives when considering their location (cost
minimization), new ones must be added that are able to reflect concerns
regarding the quality of life of the communities impacted by the outcome of
these decisions.
The location-routing of semi-obnoxious facilities therefore requires to be
analysed using multi-objective approaches, which can be of two types: noninteractive
or interactive. Both are discussed and new methods proposed in this
thesis; the proposed interactive method is suitable to other multi-objective
mixed integer programming problems.
Finally, a newly developed decision-support tool to address the LRP is
presented (being the adopted methodology discussed, and its main
functionalities shown). This tool has great concerns regarding the user
interface, as it is directed at decision makers who typically don’t have specific
knowledge of the underlying models of this type of problems
Exact models for selection problems: from clinical trials to network design
Discrete optimization is becoming an increasingly important tool for solving problems in the real world. The matching problem and the network design problem are two well studied selection problems in this area. They can be considered as modelling relations between nodes of a bi-graph or a graph, respectively. Using acute stroke trials as a context, the assignment algorithm is utilized to investigate a complex relationship between the overall degree of individual matching, the size of samples, and the quality of matching on variables. It is concluded that the post-hoc individual matching in parallel group randomized clinical trials cannot be recommended as a technique for treatment effect estimation. Based on the concept of the transshipment problem we proposed a mixed integer programming model to solve the asymmetric traveling salesman problems. The formulation is extendable to other transportation scheduling problems which are related to the traveling salesman problem (TSP) such as the Multiple TSP (m-TSP) and the Selective TSP (STSP). In addition to avoiding any cycles and being easy to implement, the model has a reasonable order of space complexity. It can be built on either a directed graph or an undirected graph. The reserve network design problem is a variation of the STSP which maximizes some utilities subject to various constraints. These constraints include a budget limitation and spatial attributes such as connectivity and compactness. The proposed model achieves the contiguity and to some extent compactness attributes. It does this without incurring the problem of sub-tours and requiring any regular shape assumptions. Furthermore, where full connectivity is not required, the model enables the trade-off between the number of contiguous areas and utility to be determined easily. The combinatorial structure of the reserve network design problem places it in the category of NP-hard problems which have exponential time complexity. We explored approaches to reduce the computational effort and introduced an approach with improved efficiency. Using this approach, the experimental results show the solution time significantly reduced on average
Online planning for multi-robot active perception with self-organising maps
© 2017, Springer Science+Business Media, LLC, part of Springer Nature. We propose a self-organising map (SOM) algorithm as a solution to a new multi-goal path planning problem for active perception and data collection tasks. We optimise paths for a multi-robot team that aims to maximally observe a set of nodes in the environment. The selected nodes are observed by visiting associated viewpoint regions defined by a sensor model. The key problem characteristics are that the viewpoint regions are overlapping polygonal continuous regions, each node has an observation reward, and the robots are constrained by travel budgets. The SOM algorithm jointly selects and allocates nodes to the robots and finds favourable sequences of sensing locations. The algorithm has a runtime complexity that is polynomial in the number of nodes to be observed and the magnitude of the relative weighting of rewards. We show empirically the runtime is sublinear in the number of robots. We demonstrate feasibility for the active perception task of observing a set of 3D objects. The viewpoint regions consider sensing ranges and self-occlusions, and the rewards are measured as discriminability in the ensemble of shape functions feature space. Exploration objectives for online tasks where the environment is only partially known in advance are modelled by introducing goal regions in unexplored space. Online replanning is performed efficiently by adapting previous solutions as new information becomes available. Simulations were performed using a 3D point-cloud dataset from a real robot in a large outdoor environment. Our results show the proposed methods enable multi-robot planning for online active perception tasks with continuous sets of candidate viewpoints and long planning horizons
Approximation results for a min–max location-routing problem
AbstractThis paper studies a min–max location-routing problem, which aims to determine both the home depots and the tours for a set of vehicles to service all the customers in a given weighted graph, so that the maximum working time of the vehicles is minimized. The min–max objective is motivated by the needs of balancing or fairness in vehicle routing applications. We have proved that unless NP=P, it is impossible for the problem to have an approximation algorithm that achieves an approximation ratio of less than 4/3. Thus, we have developed the first constant ratio approximation algorithm for the problem. Moreover, we have developed new approximation algorithms for several variants, which improve the existing best approximation ratios in the previous literature
Exploring an Infinite Space with Finite Memory Scouts
Consider a small number of scouts exploring the infinite -dimensional grid
with the aim of hitting a hidden target point. Each scout is controlled by a
probabilistic finite automaton that determines its movement (to a neighboring
grid point) based on its current state. The scouts, that operate under a fully
synchronous schedule, communicate with each other (in a way that affects their
respective states) when they share the same grid point and operate
independently otherwise. Our main research question is: How many scouts are
required to guarantee that the target admits a finite mean hitting time?
Recently, it was shown that is an upper bound on the answer to this
question for any dimension and the main contribution of this paper
comes in the form of proving that this bound is tight for .Comment: Added (forgotten) acknowledgement