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About

Full Professor at Industrial Engineering and Management, FEUP. Member of the Board at INESC TEC Technology and Science. 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.

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.

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).

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021
Publications

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.

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.

2019

Consistent Consolidation Strategies in Grocery Retail Distribution

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

Publication
Springer Proceedings in Mathematics and Statistics

Abstract
In the food retail sector, maintaining the food quality across the supply chain is of vital importance. The quality of the products is dependent on its storage and transportation conditions and this peculiarity increases the supply chain complexity relatively to other types of retailers. Actually, in this industry there are three types of food supply chains: frozen, chilled and ambient. Moreover, food retailers run different store formats, of different sizes, assortments and sales volume. In this study we research the trade-off between consolidating a range of products in order to perform direct deliveries to the stores versus performing separate delivery routes for products with different transportation requirements. A new consistency dimension is proposed regarding the periodicity that a consolidation strategy is implemented. The aim of this paper is to define a consolidation strategy for the delivery mode planning that allows to smooth the complexity of grocery retail operations. A three-step approach is proposed to tackle a real size problem in a case-study with a major Portuguese grocery retailer. By changing the consolidation strategy with a complete consistent plan the company could reach annual savings of around 4%. © 2019, Springer Nature Switzerland AG.

2019

Consistent vehicle routing problem with service level agreements: A case study in the pharmaceutical distribution sector

Authors
Campelo, P; Neves Moreira, F; Amorim, P; Almada Lobo, B;

Publication
European Journal of Operational Research

Abstract
In this paper, a mathematical model is developed to tackle a Consistent Vehicle Routing Problem, which considers customers with multiple daily deliveries and different service level agreements such as time windows, and release dates. In order to solve this problem, an instance size reduction algorithm and a mathematical programming based decomposition approach are developed. This solution approach is benchmarked against a commercial solver. Results indicate that the method solves instances of large size, enabling its application to real-life scenarios. A case study in a pharmaceutical distribution company is analyzed. Consistent routes are planned for several warehouses, comprising hundreds of orders. A simulation model evaluates the performance of the generated route plans. Significant improvements in terms of the total distance traveled and the total travel times are obtained when compared to the company's current planning process. © 2018 Elsevier B.V.

Supervised
thesis

2020

The demand for healthcare services and resources: patterns, trends and challenges in healthcare delivery

Author
Sofia Cristina Guedes de Sousa e Cruz Gomes

Institution
UP-FEUP

2020

Leveraging Supplier Selection Within Supply Chain Managememt Under Uncertainty

Author
Thomy Eko Saputro

Institution
UP-FEUP

2020

Servitization of manufacturing firms over time: An empirical investigation in the elevator industry

Author
Miguel Leichsenring Franco

Institution
UP-FEUP

2020

Supply Chain Modelling and Supplier Selection under Supply Chain Risk Management in the Aerospace Industry

Author
Nuno Bernardo Gonçalves Falcão e Cunha

Institution
UP-FEUP

2019

Order Cycle Optimization in a Luxury Fashion Marketplace

Author
Tiago Manuel Medeiros Furtado

Institution
UP-FEUP