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About

About

I am a researcher in the Center for Industrial Engineering and Management from INESC TEC, and a professor in the Department of Industrial Engineering and Management at FEUP. I hold M.Sc. and Ph.D. degrees in Industrial Engineering and Management from FEUP.

My research interests include operations management and decision support systems. I have published in international journals such as MSOM, Omega, IJPE, IJPR, COR and DSS - Google citation profile.

I have also been a researcher/consultant in several R&D projects, funded by different types of entities, in the areas of production planning, supply chain design, scheduling, inventory replenishment and artificial intelligence.

Interest
Topics
Details

Details

  • Name

    Gonçalo Reis Figueira
  • Role

    Senior Researcher
  • Since

    01st January 2014
008
Publications

2024

A cooperative coevolutionary hyper-heuristic approach to solve lot-sizing and job shop scheduling problems using genetic programming

Authors
Zeiträg, Y; Figueira, JR; Figueira, G;

Publication
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH

Abstract
Lot-sizing and scheduling in a job shop environment is a fundamental problem that appears in many industrial settings. The problem is very complex, and solutions are often needed fast. Although many solution methods have been proposed, with increasingly better results, their computational times are not suitable for decision-makers who want solutions instantly. Therefore, we propose a novel greedy heuristic to efficiently generate production plans and schedules of good quality. The main innovation of our approach represents the incorporation of a simulation-based technique, which directly generates schedules while simultaneously determining lot sizes. By utilising priority rules, this unique feature enables us to address the complexity of job shop scheduling environments and ensures the feasibility of the resulting schedules. Using a selection of well-known rules from the literature, experiments on a variety of shop configurations and complexities showed that the proposed heuristic is able to obtain solutions with an average gap to Cplex of 4.12%. To further improve the proposed heuristic, a cooperative coevolutionary genetic programming-based hyper-heuristic has been developed. The average gap to Cplex was reduced up to 1.92%. These solutions are generated in a small fraction of a second, regardless of the size of the instance.

2023

Hybrid MCDM and simulation-optimization for strategic supplier selection

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

Publication
EXPERT SYSTEMS WITH APPLICATIONS

Abstract
Supplier selection for strategic items requires a comprehensive framework dealing with qualitative and quantitative aspects of a company's competitive priorities and supply risk, decision scope, and uncertainty. In order to address these aspects, this study aims to tackle supplier selection for strategic items with a multi-sourcing, taking into account multi-criteria, incorporating uncertainty of decision-makers judgment and supplier-buyer parameters, and integrating with inventory management which the past studies have not addressed well. We develop a novel two-phase solution approach based on integrated multi-criteria decision -making (MCDM) and multi-objective simulation-optimization (S-O). First, MCDM methods, including fuzzy AHP and interval TOPSIS, are applied to calculate suppliers' scores, incorporating uncertain decision makers' judgment. S-O then combines the (quantitative) cost-related criteria and considers supply disruptions and uncertain supplier-buyer parameters. By running this approach on data generated based on previous studies, we evaluate the impact of the decision maker's and the objective's weight, which are considered important in supplier selection.

2023

Scheduling wagons to unload in bulk cargo ports with uncertain processing times

Authors
Ferreira, C; Figueira, G; Amorim, P; Pigatti, A;

Publication
COMPUTERS & OPERATIONS RESEARCH

Abstract
Optimising operations in bulk cargo ports is of great relevance due to their major participation in international trade. In inbound operations, which are critical to meet due dates, the product typically arrives by train and must be transferred to the stockyard. This process requires several machines and is subject to frequent disruptions leading to uncertain processing times. This work focuses on the scheduling problem of unloading the wagons to the stockyard, approaching both the deterministic and the stochastic versions. For the deterministic problem, we compare three solution approaches: a Mixed Integer Programming model, a Constraint Programming model and a Greedy Randomised algorithm. The selection rule of the latter is evolved by Genetic Programming. The stochastic version is tackled by dispatching rules, also evolved via Genetic Programming. The proposed approaches are validated using real data from a leading company in the mining sector. Results show that the new heuristic presents similar results to the company's algorithm in a considerably shorter computational time. Moreover, we perform extensive computational experiments to validate the methods on a wide spectrum of randomly generated instances. Finally, as managing uncertainty is fundamental for the effectiveness of these operations, distinct strategies are compared, ranging from purely predictive to completely reactive scheduling. We conclude that re-scheduling with high frequency is the best approach to avoid performance deterioration under schedule disruptions, and using the evolved dispatching rules incur fewer deviations from the original schedule.

2023

Using Supplier Networks to Handle Returns in Online Marketplaces

Authors
Pinto, C; Figueira, G; Amorim, P;

Publication
Springer Proceedings in Mathematics and Statistics

Abstract
To encourage customers to take a chance in finding the right product, retailers and marketplaces implement benevolent return policies that allow users to return items for free without a specific reason. These policies contribute to a high rate of returns, which result in high shipping costs for the retailer and a high environmental toll on the planet. This paper shows that these negative impacts can be significantly minimized if inventory is exchanged within the supplier network of marketplaces upon a return. We compare the performance of this proposal to the standard policy where items are always sent to the original supplier. Our results show that our proposal—returning to a closer supplier and using a predictive heuristic for fulfilment—can achieve a 16% cost reduction compared to the standard—returning to the original supplier and using a myopic rule for fulfilment. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2022

Fostering Customer Bargaining and E-Procurement Through a Decentralised Marketplace on the Blockchain

Authors
Martins, J; Parente, M; Amorim Lopes, M; Amaral, L; Figueira, G; Rocha, P; Amorim, P;

Publication
IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT

Abstract
Firms have available many forms of collaboration, including cooperatives or joint ventures, in this way leveraging their market power. Customers, however, are atomic agents with few mechanisms for collaborating, leading to an unbalanced buyer-supplier relationship and economic surpluses that shift to producers. Some group buying websites helped alleviate the problem by offering bulk discounts, but more advancements can be made with the emergence of technologies, such as the blockchain. In this article, we propose a customer-push e-marketplace built on top of Ethereum, where customers can aggregate their proposals, and suppliers try to outcompete each other in reverse auction bids to fulfil the order. Furthermore, smart contracts make it possible to automate many operational activities, such as payment escrows/release upon delivery confirmation, increasing the efficiency along the supply chain. The implementation of this network is expected to improve market efficiency by reducing transaction costs, time delays, and information asymmetry. Furthermore, concepts such as increased bargaining power and economies of scale, and their effects in buyer-supplier relationships, are also explored.

Supervised
thesis

2022

Information security monitoring systems in digital and mobile identification environments

Author
Paulo Miguel Novais Gameiro

Institution
UM

2022

Formalização da reconfiguração de protocolos de consenso usando Alloy

Author
Cecília da Conceição de Oliveira Soares

Institution
UM

2022

Towards fully autonomous detection of contextual fabric anomalies through supervised deep learning models

Author
Diogo Costa Cunha

Institution
UP-FEUP

2022

Orchestration and Distribution of Services in Hybrid Cloud/Edge Environments

Author
João Pedro Machado Vilaça

Institution
UM

2022

Characterizing Data Scientists in the Real World

Author
Paula Sofia da Cunha Pereira

Institution
UM