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About

Full Professor at Industrial Engineering and Management, FEUP, and Porto Business School. Co-founder of LTPlabs (spin-off of INESC TEC and FEUP). Member of the board of Trustees ("conselho de curadores") of Fundação Belmiro de Azevedo

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.

Advanced Management Programme, INSEAD. PhD in Industrial Engineering and Management, UP. Degree in Management and Industrial Engineering (5 years degree), FEUP. 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.

Former Member of the Board at INESC TEC Technology and Science. Former Vice-Academic Director of IBM Center for Advanced Studies Portugal (IBM-CAS). Co-founder of start-up Adjust Consulting (that was acquired by Glintt HealthCare).

Interest
Topics
Details

Details

023
Publications

2022

A comprehensive framework and literature review of supplier selection under different purchasing strategies

Authors
Saputro, TE; Figueira, G; Almada-Lobo, B;

Publication
Computers and Industrial Engineering

Abstract
Supplier selection has received substantial consideration in the literature since it is considered one of the key levers contributing to a firm's success. Selecting the right suppliers for different product items requires an appropriate problem framing and a suitable approach. Despite the vast literature on this topic, there is not a comprehensive framework underlying the supplier selection process that addresses those concerns. This paper formalizes a framework that provides guidance on how supplier selection should be formulated and approached for different types of items segmented in Kraljic's portfolio matrix and production policies. The framework derives from a thorough literature review, which explores the main dimensions in supplier selection, including sourcing strategy, decision scope and environment, selection criteria, and solution approaches. 326 papers, published from 2000 to 2021, were reviewed for said purpose. The results indicate that supplier selection regarding items with a high purchasing importance should lead to holistic selection criteria. In addition, items comprising a high complexity of supply and production activities should require integrated selection and different sources of uncertainty associated with decision scope and environment, respectively, to solve it, as well as hybrid approaches. There are still many research opportunities in the supplier selection area, particularly in the integrated selection problems and hybrid solution methods, as well as in the risk mitigation, sustainability goals, and new technology adoption.

2021

A green lateral collaborative problem under different transportation strategies and profit allocation methods

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

Publication
JOURNAL OF CLEANER PRODUCTION

Abstract
Collaboration between companies in transportation problems seeks to reduce empty running of vehicles and to increase the use of vehicles' capacity. Motivated by a case study in the food supply chain, this paper examines a lateral collaboration between a leading retailer (LR), a third party logistics provider (3 PL) and different producers. Three collaborative strategies may be implemented simultaneously, namely pickup-delivery, collection and cross-docking. The collaborative pickup-delivery allows an entity to serve customers of another in the backhaul trips of the vehicles. The collaborative collection allows loads to be picked up at the producers in the backhauling routes of the LR and the 3 PL, instead of the traditional outsourcing. The collaborative cross-docking allows the producers to cross-dock their cargo at the depot of another entity, which is then consolidated and shipped with other loads, either in linehaul or backhaul routes. The collaborative problem is formulated with three different objective functions: minimizing total operational costs, minimizing total fuel consumption and minimizing operational and CO2 emissions costs. The synergy value of collaborative solutions is assessed in terms of costs and environmental impact. Three proportional allocation methods from the literature are used to distribute the collaborative gains among the entities, and their limitations and capabilities to attend fairness criteria are analyzed. Collaboration is able to reduce the global fuel consumption in 26% and the global operational costs in 28%, independently of the objective function used to model the problem. The collaborative pickup-delivery strategy outperforms the other two in the majority of instances under different objectives and parameter settings. The collaborative collection is favoured when the ordering loads from producers increase. The collaborative cross-docking tends to be implemented when the producers are located close to the depot of the 3 PL.

2021

Improving picking performance at a large retailer warehouse by combining probabilistic simulation, optimization, and discrete-event simulation

Authors
Amorim Lopes, M; Guimaraes, L; Alves, J; Almada Lobo, B;

Publication
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH

Abstract
Distribution warehouses are a critical part of supply chains, representing a nonnegligible share of the operating costs. This is especially true for unautomated, labor-intensive warehouses, partially due to time-consuming activities such as picking up items or traveling. Inventory categorization techniques, as well as zone storage assignment policies, may help in improving operations, but may also be short-sighted. This work presents a three-step methodology that uses probabilistic simulation, optimization, and event-based simulation (SOS) to analyze and experiment with layout and storage assignment policies to improve the picking performance. In the first stage, picking performance is estimated under different storage assignment policies and zone configurations using a probabilistic model. In the second stage, a mixed integer optimization model defines the overall warehouse layout by selecting the configuration and storage assignment policy for each zone. Finally, the optimized layout solution is tested under demand uncertainty in the third, final simulation phase, through a discrete-event simulation model. The SOS methodology was validated with three months of operational data from a large retailer's warehouse, successfully illustrating how it may be successfully used for improving the performance of a distribution warehouse.

2021

Cold chain management in hierarchical operational hub networks*

Authors
Esmizadeh, Y; Bashiri, M; Jahani, H; Almada Lobo, B;

Publication
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW

Abstract
This paper proposes a multi-objective mixed-integer linear programming to model a cold chain with complementary operations on a hierarchical hub network. Central hubs are linked to each other in the first level of the network and to the star network of the lower-level hubs. As for a case study, different hub levels provide various refreshing or freezing operations to keep the perishable goods fresh along the network. Disruption is formulated by the consideration of stochastic demand and multi-level freshness time windows. Regarding the solution, a genetic algorithm is also developed and compared for competing the large-sized networks.

2020

Cooperative coevolution of expressions for (r,Q) inventory management policies using genetic programming

Authors
Lopes, RL; Figueira, G; Amorim, P; Almada Lobo, B;

Publication
International Journal of Production Research

Abstract
There are extensive studies in the literature about the reorder point/order quantity policies for inventory management, also known as (r,Q) policies. Over time different algorithms have been proposed to calculate the optimal parameters given the demand characteristics and a fixed cost structure, as well as several heuristics and meta-heuristics that calculate approximations with varying accuracy. This work proposes a new meta-heuristic that evolves closed-form expressions for both policy parameters simultaneously - Cooperative Coevolutionary Genetic Programming. The implementation used for the experimental work is verified with published results from the optimal algorithm, and a well-known hybrid heuristic. The evolved expressions are compared to those algorithms, and to the expressions of previous Genetic Programming approaches available in the literature. The results outperform the previous closed-form expressions and demonstrate competitiveness against numerical methods, reaching an optimality gap of less than (Formula presented.), while being two orders of magnitude faster. Moreover, the evolved expressions are compact, have good generalisation capabilities, and present an interesting structure resembling previous heuristics. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

Supervised
thesis

2021

Gestão e Marketing de Equipamentos Tecnológicos Suportados em Informação

Author
Luís Fernando Cosme Martins

Institution
UP-FEUP

2021

Using Business Intelligence to leverage field workforce capacity planning in Telco

Author
Salomé Pinto de Meneses Monteiro

Institution
UP-FEUP

2021

Application of growth hacking methods in the promotion of mobile applications

Author
João Nuno Alvim Leite Terra Figueiredo

Institution
UP-FEUP

2021

Business Intelligence para caracterização de clientes com base no consumo de televisão

Author
Mariana da Costa Fong Vieira Gomes

Institution
UP-FEUP

2021

Fulfillment in online retail: making real-time decisions with explainable artificial intelligence

Author
Sérgio Vasconcelos Castro

Institution
UP-FEUP