Browsing by Author "Perez-Galarce, Francisco"

Now showing 1 - 8 of 8
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    A multi-objective optimization model for planning emergency shelters after a tsunami
    (2024) Sotelo-Salas, Christian; Monardes-Concha, Carlos A.; Perez-Galarce, Francisco; Santa Gonzalez, Rosemarie
    Vertical evacuation helps people escape tsunami risks by elevating them above the level of tsunami inundation, usually by moving to higher ground or taking refuge in tall buildings or other elevated structures. Unlike horizontal evacuation, which involves moving away from the coast to higher ground, vertical evacuation reduces the demand for horizontal evacuation routes that can become congested and impede evacuation efforts. Therefore, investing in critical infrastructure that enables vertical evacuation is crucial in tsunami-prone areas. This study proposes a multi-objective optimization model to help decision -makers assign critical infrastructure for vertical evacuation in tsunami-prone areas. Critical infrastructure includes buildings that can provide shelter during a tsunami and road networks for rapid access to shelter points. The proposed model balances three objectives: (1) minimizing investment costs in critical infrastructure, (2) maximizing the population covered by shelters, and (3) minimizing the evacuation time for evacuees to reach the shelters. This model is tested on real-world data from the Coquimbo-La Serena coastal conurbation in the Coquimbo region of Chile. The study contributes to the literature on tsunami evacuation modeling and provides valuable information for decisionmakers to plan and invest in critical infrastructure for vertical evacuation during tsunamis. A sensitivity analysis of various parameters is conducted, and managerial insights are provided.
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    A Rolling Horizon scheme for rescheduling in agricultural harvest
    (2023) Santos, Fernando Montenegro-Dos; Perez-Galarce, Francisco; Monardes-Concha, Carlos; Candia-Vejar, Alfredo; Seido-Nagano, Marcelo; Gomez-Lagos, Javier
    Over the last decade, agriculture has evolved from a human-intensive activity to a highly automated process. Multiple technological advances (e.g., harvest machines, sensors, and drones) have been incorporated to collect and transmit information, increasing harvest efficiency and more accurate and timely decisions. These advances have opened new opportunities to apply optimization models during the harvest season. In this context, to apply these models, it is necessary to consider the underlying uncertainty in agricultural operations that comes mainly from weather conditions and the biological characteristics of crops. One of the traditional strategies used to reactively manage these uncertainties in optimization models is the Rolling Horizon (RH) strategy. However, RH is typically myopic about the future, and it can be challenging to implement this approach when commitments with suppliers are signed. This work proposes a non-myopic rolling horizon method to reschedule the agricultural harvest plan. Furthermore, our RH scheme is exemplified by means of olive oil harvesting and production. Our method is based on a baseline plan generation, and after that, an adaptive rescheduling scheme is generated under new conditions. A bi-objective rescheduling problem seeking to maximize production and minimize plan variability is formulated. Computational experiments are conducted to study our methodology's impact in several rescheduling periods. A good performance in two challenging agricultural scenarios is highlighted. This proposal offers the community a framework for reactively managing complex harvest operations.
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    Algorithms for the Minmax Regret Path problem with interval data
    (2018) Perez-Galarce, Francisco; Candia-Vejar, Alfredo; Astudillo, Cesar; Bardeen, Matthew
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    Distinguishing a planetary transit from false positives: a Transformer-based classification for planetary transit signals
    (2023) Salinas, Helem; Pichara, Karim; Brahm, Rafael; Perez-Galarce, Francisco; Mery, Domingo
    Current space-based missions, such as the Transiting Exoplanet Survey Satellite (TESS), provide a large database of light curves that must be analysed efficiently and systematically. In recent years, deep learning (DL) methods, particularly convolutional neural networks (CNN), have been used to classify transit signals of candidate exoplanets automatically. However, CNNs have some drawbacks; for example, they require many layers to capture dependencies on sequential data, such as light curves, making the network so large that it eventually becomes impractical. The self-attention mechanism is a DL technique that attempts to mimic the action of selectively focusing on some relevant things while ignoring others. Models, such as the Transformer architecture, were recently proposed for sequential data with successful results. Based on these successful models, we present a new architecture for the automatic classification of transit signals. Our proposed architecture is designed to capture the most significant features of a transit signal and stellar parameters through the self-attention mechanism. In addition to model prediction, we take advantage of attention map inspection, obtaining a more interpretable DL approach. Thus, we can identify the relevance of each element to differentiate a transit signal from false positives, simplifying the manual examination of candidates. We show that our architecture achieves competitive results concerning the CNNs applied for recognizing exoplanetary transit signals in data from the TESS telescope. Based on these results, we demonstrate that applying this state-of-the-art DL model to light curves can be a powerful technique for transit signal detection while offering a level of interpretability.
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    Improved robust shortest paths by penalized investments
    (2021) Perez-Galarce, Francisco; Candia-Vejar, Alfredo; Maculan, Guido; Maculan, Nelson
    Connectivity after disasters has become a critical problem in the management of modern cities. This comes from the need of the decision-makers to ensure urgent medical attention by providing access to health facilities and to other relevant services needed by the population. Managing congestion could help maintain some routes operative even in complex scenarios such as natural disasters, terrorist attacks, protests, or riots. Recent advances in Humanitarian Logistics have handled this problem using different modeling approaches but have principally focused on the response phase. In this paper, firstly, we propose a penalized variant of an existing mathematical model for the robust s-t path problem with investments. With the aim of solving the robust several-to-one path problem with investments, and due to the high complexity of this new problem, a heuristic is proposed. Moreover, this approach allows us to improve travel times in both specific paths and in a set of routes in a systemic framework. The new problem and the proposed heuristic are illustrated by an example, which corresponds to a typical city network, that provides a concrete vision of the potential application of the framework. Lastly, some managerial insights are given by the analysis of results exhibited in the example network.
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    Informative regularization for a multi-layer perceptron RR Lyrae classifier under data shift
    (2023) Perez-Galarce, Francisco; Pichara, Karim; Huijse, Pablo; Catelan, Marcio; Mery Quiroz, Domingo Arturo
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    ML models for severity classification and length-of-stay forecasting in emergency units
    (2023) Moya-Carvajal, Jonathan; Perez-Galarce, Francisco; Taramasco, Carla; Astudillo, Cesar A.; Candia-Vejar, Alfredo
    Length-of-stay (LoS) prediction and severity classification for patients in emergency units in a clinic or hospital are crucial problems for public and private health networks. An accurate estimation of these parameters is essential for better planning resources, which are usually scarce. Although it is possible to find several works that propose traditional Machine Learning (ML) models to face these challenges, few works have exploited advances in Natural Language Processing (NLP) on Spanish raw-text vector representations. Consequently, we take advantage of those advances, incorporating sentence embeddings in traditional ML models to improve predictions. Moreover, we apply a strategy based on SHapley Additive exPlanations (SHAP) values to provide explanations for these predictions. The results of our case study demonstrate an increase in the accuracy of the predictions using raw text with a minimum preprocessing. The precision increased by up to 2% in the classification of the patient's post-care destination and by up to 8% in the prediction of LoS in the hospital. This evidence encourages practitioners to use available text to anticipate the patient's need for hospitalization more accurately at the earliest stage of the care process.
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    Optimising the storage assignment and order-picking for the compact drive-in storage system
    (2020) Revillot-Narvaez, David; Perez-Galarce, Francisco; Alvarez-Miranda, Eduardo
    One of the most common systems in non-automated warehouses, is drive-in pallet racking with a shared storage policy (which is usually based on the duration-of-stay). Such scheme targets towards an efficient use of storage space, since its operation costs are directly related to the size and layout of the warehouse. In this paper, two mathematical programming models and two greedy-randomised based heuristics for finding (nearly) optimal storage and retrieval operation sequences for this type of storage system are proposed. The computational effectiveness of the proposed approaches is measured by considering two sets of synthetic instances. The obtained results show that the proposed heuristics are not only able to compute high-quality solutions (as observed when being compared with the optimal solutions attained by the mathematical programming models), but it is also capable of providing solutions in very short running times even for large instances for which the mathematical programming model failed to find feasible solutions. At the light of these results, the best heuristic is also tested using a rolling-horizon planning strategy in a real-world case study, obtained from a Chilean company. It turns out that the attained results are more effective than the company's current storage policy.