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Sobre

Sobre

Ana Viana é Doutorada em Engenharia Electrotécnica e de Computadores pela Univesidade do Porto (2004).

É coordenadora do Centro de Engenharia e Gestão Industrial do INESC TEC e Professora Coordenadora do Instituto Superior de Engenharia do Porto.

A sua principal área de investigação é Investigação Operacional, com foco em problemas de optimização combinatória. Como técnicas de resolução deste tipo de problemas explora quer abordagens baseadas em técnicas exactas, quer heurísticas.

Liderou vários projectos financiados por fundos públicos nas áreas de Saúde, Logística e Energia e publica regularmente em revistas científicas de referência, na sua área de actividade.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Ana Viana
  • Cargo

    Investigador Coordenador
  • Desde

    09 dezembro 1997
014
Publicações

2023

Stochastic crowd shipping last-mile delivery with correlated marginals and probabilistic constraints

Autores
Silva, M; Pedroso, JP; Viana, A;

Publicação
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH

Abstract
In this work, we study last-mile delivery with the option of crowd shipping. A company uses occasional drivers to complement its fleet in the activity of delivering products to its customers. We model it as a variant of the stochastic capacitated vehicle routing problem. Our approach is data-driven, where not only customer orders but also the availability of occasional drivers are uncertain. It is assumed that marginal distributions of the uncertainty vector are known, but the joint distribution is difficult to estimate. We optimize considering a worst-case joint distribution and model with a strategic planning perspective, where we calculate an optimal a priori solution before the uncertainty is revealed. A limit on the infea-sibility of the routes due to the capacity is imposed using probabilistic constraints. We propose an extended formulation for the problem using column-dependent rows and implement a branch-price-and-cut algorithm to solve it. We also develop a heuristic approximation to cope with larger instances of the problem. Through computational experiments, we analyze the solution and performance of the implemented algorithms.

2023

Preface to the Special Issue on Operations Research in Healthcare

Autores
Viana, A; Marques, I; Dias, JM;

Publicação
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH

Abstract

2023

A data-driven compensation scheme for last-mile delivery with crowdsourcing

Autores
Barbosa, M; Pedroso, JP; Viana, A;

Publicação
COMPUTERS & OPERATIONS RESEARCH

Abstract
A recent relevant innovation in last-mile delivery is to consider the possibility of goods being delivered by couriers appointed through crowdsourcing. In this paper we focus on the setting of in-store customers delivering goods, ordered by online customers, on their way home. We assume that not all the proposed delivery tasks will necessarily be accepted, and use logistic regression to model the crowd agents' willingness to undertake a delivery. This model is then used to build a novel compensation scheme that determines reward values, based on the current plan for the professional fleet's routes and on the couriers' probabilities of acceptance, by employing a direct search algorithm that seeks to minimise the expected cost.

2023

Deep reinforcement learning for stochastic last-mile delivery with crowdshipping

Autores
Silva, M; Pedroso, JP; Viana, A;

Publicação
EURO JOURNAL ON TRANSPORTATION AND LOGISTICS

Abstract
We study a setting in which a company not only has a fleet of capacitated vehicles and drivers available to make deliveries but may also use the services of occasional drivers (ODs) willing to make deliveries using their own vehicles in return for a small fee. Under such a business model, a.k.a crowdshipping, the company seeks to make all the deliveries at the minimum total cost, i.e., the cost associated with their vehicles plus the compensation paid to the ODs.We consider a stochastic and dynamic last-mile delivery environment in which customer delivery orders, as well as ODs available for deliveries, arrive randomly throughout the day, within fixed time windows.We present a novel deep reinforcement learning (DRL) approach to the problem that can deal with large problem instances. We formulate the action selection problem as a mixed-integer optimization program.The DRL approach is compared against other optimization under uncertainty approaches, namely, sample -average approximation (SAA) and distributionally robust optimization (DRO). The results show the effective-ness of the DRL approach by examining out-of-sample performance.

2023

Novel integer programming models for the stable kidney exchange problem

Autores
Klimentova, X; Biro, P; Viana, A; Costa, V; Pedroso, JP;

Publicação
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH

Abstract
Kidney exchange programs (KEPs) represent an additional possibility of transplant for patients suffering from end-stage kidney disease. If a patient has a willing living donor with whom the patient is not compatible, the pair recipient-donor can join a pool of incompatible pairs and, if compatibility between recipient and donor in two or more pairs exists, organs can be exchanged between them. The problem can be modelled as an integer program that in general aims at finding the pairs that should be selected for transplant such that maximum number of transplants is performed. In this paper, we consider that for each recipient there may exist a preference order over the organs that he/she can receive, since a recipient may be compatible with several donors but the level of compatibility with the recipient might vary for different donors. Under this setting, the aim is to find the maximum cardinality stable exchange, a solution where no blocking cycle exists, i.e., there is no cycle such that all recipients prefer the donor in that cycle rather than that in the exchange. For this purpose we propose four novel integer programming models based on the well-known edge and cycle formulations, and also on the position-indexed formulation. These formulations are adjusted for both finding stable and strongly stable exchanges under strict preferences and for the case when ties in preferences may exist. Further-more, we study a situation when the stability requirement can be relaxed by addressing the trade-off between maximum cardinality versus number of blocking cycles allowed in a solution. The effectiveness of the proposed models is assessed through extensive computational experiments on a wide set of in-stances. Results show that the cycle-edge and position-indexed formulations outperform the other two formulations. Another important practical outcome is that targeting strongly stable solutions has a much higher negative impact on the number of transplants (with an average reduction of up to 20% for the bigger instances), when compared to stable solutions.

Teses
supervisionadas

2019

Study of control of protein abundance using multi-omics data from cancer samples

Autor
Abel Ernesto Fernandes de Sous

Instituição

2018

RESTRIÇÕES DE GARRAS MANIPULADORAS DENTRO DO PROBLEMA DE EMPACOTAMENTO DE PALETES DO PRODUTOR ? UMA ABORDAGEM HEURÍSTICA

Autor
JOÃO PEDRO WOITCHY BARBOSA

Instituição
IPP-ISEP

2017

Controlo de locomoção do veículo robótico submarino TURTLE com recurso a sistema de variação de flutuabilidade

Autor
HÉLDER JOEL FERNANDES OLIVEIRA

Instituição
IPP-ISEP

2016

Embedded Scheduler for Dynamically Reconfigurable Accelerators

Autor
Carlos Jorge Matos Carneiro de Sousa

Instituição
UP-FEUP

2015

Balancing and lot-sizing mixed-model lines in the footwear industry

Autor
PARISA SADEGHI

Instituição
IPP-ISEP