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Publicações

Publicações por CEGI

2025

A GRASP-based multi-objective approach for the tuna purse seine fishing fleet routing problem

Autores
Granado, I; Silva, E; Carravilla, MA; Oliveira, JF; Hernando, L; Fernandes-Salvador, JA;

Publicação
COMPUTERS & OPERATIONS RESEARCH

Abstract
Nowadays, the world's fishing fleet uses 20% more fuel to catch the same amount offish compared to 30 years ago. Addressing this negative environmental and economic performance is crucial due to stricter emission regulations, rising fuel costs, and predicted declines in fish biomass and body sizes due to climate change. Investment in more efficient engines, larger ships and better fuel has been the main response, but this is only feasible in the long term at high infrastructure cost. An alternative is to optimize operations such as the routing of a fleet, which is an extremely complex problem due to its dynamic (time-dependent) moving target characteristics. To date, no other scientific work has approached this problem in its full complexity, i.e., as a dynamic vehicle routing problem with multiple time windows and moving targets. In this paper, two bi-objective mixed linear integer programming (MIP) models are presented, one for the static variant and another for the time-dependent variant. The bi-objective approaches allow to trade off the economic (e.g., probability of high catches) and environmental (e.g., fuel consumption) objectives. To overcome the limitations of exact solutions of the MIP models, a greedy randomized adaptive search procedure for the multi-objective problem (MO-GRASP) is proposed. The computational experiments demonstrate the good performance of the MO-GRASP algorithm with clearly different results when the importance of each objective is varied. In addition, computational experiments conducted on historical data prove the feasibility of applying the MO-GRASP algorithm in a real context and explore the benefits of joint planning (collaborative approach) compared to a non-collaborative strategy. Collaborative approaches enable the definition of better routes that may select slightly worse fishing and planting areas (2.9%), but in exchange fora significant reduction in fuel consumption (17.3%) and time at sea (10.1%) compared to non-collaborative strategies. The final experiment examines the importance of the collaborative approach when the number of available drifting fishing aggregation devices (dFADs) per vessel is reduced.

2025

Local stability in kidney exchange programs

Autores
Baratto, M; Crama, Y; Pedroso, JP; Viana, A;

Publicação
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH

Abstract
When each patient of a kidney exchange program has a preference ranking over its set of compatible donors, questions naturally arise surrounding the stability of the proposed exchanges. We extend recent work on stable exchanges by introducing and underlining the relevance of a new concept of locally stable, or L-stable, exchanges. We show that locally stable exchanges in a compatibility digraph are exactly the so-called local kernels (L-kernels) of an associated blocking digraph (whereas the stable exchanges are the kernels of the blocking digraph), and we prove that finding a nonempty L-kernel in an arbitrary digraph is NP-complete. Based on these insights, we propose several integer programming formulations for computing an L-stable exchange of maximum size. We conduct numerical experiments to assess the quality of our formulations and to compare the size of maximum L-stable exchanges with the size of maximum stable exchanges. It turns out that nonempty L-stable exchanges frequently exist in digraphs which do not have any stable exchange. All the above results and observations carry over when the concept of (locally) stable exchanges is extended to the concept of (locally) strongly stable exchanges.

2025

Biomimicry for sustainability: Upframing service ecosystems

Autores
Gallan, S; Alkire, L; Teixeira, JG; Heinonen, K; Fisk, P;

Publicação
AMS Review

Abstract
Amidst an urgent need for sustainability, novel approaches are required to address environmental challenges. In this context, biomimicry offers a promising logic for catalyzing nature’s wisdom to address this complexity. The purpose of this research is to (1) establish a biomimetic understanding and vocabulary for sustainability and (2) apply biomimicry to upframe service ecosystems as a foundation for sustainability. Our research question is: How can the principles of natural ecosystems inform and enhance the sustainability of service ecosystems? The findings highlight upframed service ecosystems as embodying a set of practices that (1) promote mutualistic interactions, (2) build on local biotic and abiotic components supporting emergence processes, (3) leverage (bio)diversity to build resilience, (4) foster resource sharing for regeneration, and (5) bridge individual roles to optimize the community rather than individual well-being. Our upframed definition of a service ecosystem is a system of resource-integrating biotic actors and abiotic resources functioning according to ecocentric principles for mutualistic and regenerative value creation. The discussion emphasizes the implications of this upframed definition for sustainability practices, advocating for a shift in understanding and interacting with service ecosystems. It emphasizes the potential for immediate mutualistic benefits and long-term regenerative impacts. © Academy of Marketing Science 2025.

2025

Predicting demand for new products in fashion retailing using censored data

Autores
Sousa, MS; Loureiro, ALD; Miguéis, VL;

Publicação
EXPERT SYSTEMS WITH APPLICATIONS

Abstract
In today's highly competitive fashion retail market, it is crucial to have accurate demand forecasting systems, namely for new products. Many experts have used machine learning techniques to forecast product sales. However, sales that do not happen due to lack of product availability are often ignored, resulting in censored demand and service levels that are lower than expected. Motivated by the relevance of this issue, we developed a two-stage approach to forecast the demand for new products in the fashion retail industry. In the first stage, we compared four methods of transforming historical sales into historical demand for products already commercialized. Three methods used sales-weighted averages to estimate demand on the days with stock-outs, while the fourth method employed an Expectation-Maximization (EM) algorithm to account for potential substitute products affected by stock-outs of preferred products. We then evaluated the performance of these methods and selected the most accurate one for calculating the primary demand for these historical products. In the second stage, we predicted the demand for the products of the following collection using Random Forest, Deep Neural Networks, and Support Vector Regression algorithms. In addition, we applied a model that consisted of weighting the demands previously calculated for the products of past collections that were most similar to the new products. We validated the proposed methodology using a European fashion retailer case study. The results revealed that the method using the Expectation-Maximization algorithm had the highest potential, followed by the Random Forest algorithm. We believe that this approach will lead to more assertive and better-aligned decisions in production management.

2025

Aligning priorities: A Comparative analysis of scientific and policy perspectives on municipal solid waste management

Autores
Rodrigues, M; Antunes, JA; Miguéis, V;

Publicação
Waste Management

Abstract
Municipal solid waste (MSW) management has become a critical issue today, posing substantial economic, environmental, and social challenges. Identifying and analyzing dominant themes in this field is essential for advancing research and policies towards sustainable MSW management practices. This study aims to explore the key issues related to MSW management that have been addressed by both the scientific community and policymakers through funded projects. By doing so, the study seeks to guide the scientific community as a knowledge producer and the EU as a key funder. Two Latent Dirichlet Allocation (LDA) models were applied to analyze the themes from two corpora: one representing scientific literature and another focusing on EU-funded projects. Additionally, this analysis was complemented by a quantitative estimation of the similarity between the two corpora, providing a measure of alignment between the scientific community and policymakers. The results generally indicate that the two spheres are aligned and highlight the diversity of topics explored by the scientific community. Nevertheless, it is concluded that there are opportunities for further research on specific topics, such as leaching and the extraction of heavy metals. Additionally, the popularity of topics identified in European Union-funded projects has fluctuated considerably over time, focusing primarily on waste management rather than its prevention. In light of these findings, waste prevention emerges as a promising avenue for future EU-funded research initiatives. © 2024 The Authors

2025

A citywide TD-learning based intelligent traffic signal control for autonomous vehicles: Performance evaluation using SUMO

Autores
Reza, S; Ferreira, MC; Machado, JJM; Tavares, JMRS;

Publicação
EXPERT SYSTEMS

Abstract
An autonomous vehicle can sense its environment and operate without human involvement. Its adequate management in an intelligent transportation system could significantly reduce traffic congestion and overall travel time in a network. Adaptive traffic signal controller (ATSC) based on multi-agent systems using state-action-reward-state-action (SARSA (lambda)) are well-known state-of-the-art models to manage autonomous vehicles within urban areas. However, this study found inefficient weights updating mechanisms of the conventional SARSA (lambda) models. Therefore, it proposes a Gaussian function to regulate the eligibility trace vector's decay mechanism effectively. On the other hand, an efficient understanding of the state of the traffic environment is crucial for an agent to take optimal actions. The conventional models feed the state values to the agents through the MinMax normalization technique, which sometimes shows less efficiency and robustness. So, this study suggests the MaxAbs scaled state values instead of MinMax to address the problem. Furthermore, the combination of the A-star routing algorithm and proposed model demonstrated a good increase in performance relatively to the conventional SARSA (lambda)-based routing algorithms. The proposed model and the baselines were implemented in a microscopic traffic simulation environment using the SUMO package over a complex real-world-like 21-intersections network to evaluate their performance. The results showed a reduction of the vehicle's average total waiting time and total stops by a mean value of 59.9% and 17.55% compared to the considered baselines. Also, the A-star combined with the proposed controller outperformed the conventional approaches by increasing the vehicle's average trip speed by 3.4%.

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