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About

About

His main area of activity is Management Science/Operations Research. He develops and applies advanced analytical models and methods to help make better decisions, solving managerial problems in various domains (manufacturing, health, retail and mobility), with a special focus on Operations Management.

Associate Professor (with “Agregação”) at Industrial Engineering and Management, FEUP. Member of the Board at INESC TEC Technology and Science. Visiting Professor at University of São Paulo. Vice-Academic Director of IBM Center for Advanced Studies Portugal (IBM-CAS). Co-founder of INESC TEC spin-off LTPlabs  and of start-up Adjust Consulting (that was merged into Glintt HealthCare). Member of the board of Trustees ("conselho de curadores") of Fundação Belmiro de Azevedo.

Degree in Management and Industrial Engineering (5 years degree), FEUP. PhD in Industrial Engineering and Management, UP. Former researcher at Operations Research Center of Massachusetts Institute of Technology – MIT/ORC. Certified Analytics Professional from The Institute for Operations Research and the Management Sciences.

Interest
Topics
Details

Details

016
Publications

2018

A computational study of the general lot-sizing and scheduling model under demand uncertainty via robust and stochastic approaches

Authors
Alem, D; Curcio, E; Amorim, P; Almada Lobo, B;

Publication
COMPUTERS & OPERATIONS RESEARCH

Abstract
This paper presents an empirical assessment of the General Lot-Sizing and Scheduling Problem (GLSP) under demand uncertainty by means of a budget-uncertainty set robust optimization and a two-stage stochastic programming with recourse model. We have also developed a systematic procedure based on Monte Carlo simulation to compare both models in terms of protection against uncertainty and computational tractability. The extensive computational experiments cover different instances characteristics, a considerable number of combinations between budgets of uncertainty and variability levels for the robust optimization model, as well as an increasing number of scenarios and probability distribution functions for the stochastic programming model. Furthermore, we have devised some guidelines for decision-makers to evaluate a priori the most suitable uncertainty modeling approach according to their preferences.

2018

Designing new heuristics for the capacitated lot sizing problem by genetic programming

Authors
Hein, F; Almeder, C; Figueira, G; Almada Lobo, B;

Publication
Computers and Operations Research

Abstract
This work addresses the well-known capacitated lot sizing problem (CLSP) which is proven to be an NP-hard optimization problem. Simple period-by-period heuristics are popular solution approaches due to the extremely low computational effort and their suitability for rolling planning horizons. The aim of this work is to apply genetic programming (GP) to automatically generate specialized heuristics specific to the instance class. Experiments show that we are able to obtain better solutions when using GP evolved lot sizing rules compared to state-of-the-art constructive heuristics. © 2018 Elsevier Ltd

2018

The time window assignment vehicle routing problem with product dependent deliveries

Authors
Neves Moreira, F; da Silva, DP; Guimaraes, L; Amorim, P; Almada Lobo, B;

Publication
Transportation Research Part E: Logistics and Transportation Review

Abstract

2018

Adaptation and approximate strategies for solving the lot-sizing and scheduling problem under multistage demand uncertainty

Authors
Curcio, E; Amorim, P; Zhang, Q; Almada Lobo, B;

Publication
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS

Abstract
This work addresses the lot-sizing and scheduling problem under multistage demand uncertainty. A flexible production system is considered, with the possibility to adjust the size and the schedule of lots in every time period based on a rolling-horizon planning scheme. Computationally intractable multistage stochastic programming models are often employed on this problem. An adaptation strategy to the multistage setting for two-stage programming and robust optimization models is proposed. We also present an approximate heuristic strategy to address the problem more efficiently, relying on multistage stochastic programming and adjustable robust optimization. In order to evaluate each strategy and model proposed, a Monte Carlo simulation experiment under a rolling-horizon scheme is performed. Results show that the strategies are promising in solving large-scale problems: the approximate strategy based on adjustable robust optimization has, on average, 6.72% better performance and is 7.9 times faster than the deterministic model.

2018

Delivery mode planning for distribution to brick-and-mortar retail stores: discussion and literature review

Authors
Martins, S; Amorim, P; Almada Lobo, B;

Publication
Flexible Services and Manufacturing Journal

Abstract
In the retail industry, there are multiple products flowing from different distribution centers to brick-and-mortar stores with distinct characteristics. This industry has been suffering radical changes along the years and new market dynamics are making distribution more and more challenging. Consequently, there is a pressure to reduce shipment sizes and increase the delivery frequency. In such a context, defining the most efficient way to supply each store is a critical task. However, the supply chain planning decision that tackles this type of problem, delivery mode planning, is not well defined in the literature. This paper proposes a definition for delivery mode planning and analyzes multiple ways retailers can efficiently supply their brick-and-mortar stores from their distribution centers. The literature addressing this planning problem is reviewed and the main interdependencies with other supply chain planning decisions are discussed. © 2017 Springer Science+Business Media New York

Supervised
thesis

2017

Master Production Planning for the Glass Container Industry: Scenario Analysis

Author
Filipe Fernandes Rocha

Institution
UP-FEUP

2017

Optimization of Returnable Packaging Flows Planning

Author
Maria Manuel Pires Afonso dos Santos

Institution
UP-FEUP

2017

Raw Materials Sourcing Optimization in the Tire Industry

Author
Marta Ribeiro Vaz da Silveira

Institution
UP-FEUP

2016

Definição do modelo de aprovisionamento de matérias-primas

Author
Francisco Jorge Freitas Salazar de Oliveira

Institution
UP-FEUP

2016

Redução do consumo de vidro nas linhas de enchimento de uma unidade cervejeira

Author
Miguel Ângelo da Silva Duarte

Institution
UP-FEUP