Browsing by Author "Candia-Vejar, Alfredo"

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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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    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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    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.