Browsing by Author "Mac Cawley, Alejandro"

Now showing 1 - 4 of 4
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    An optimization approach for scheduling wine grape harvest operations
    (ELSEVIER, 2008) Ferrer, Juan Carlos; Mac Cawley, Alejandro; Maturana, Sergio; Toloza, Sergio; Vera, Jorge
    This article presents a practical tool for optimally scheduling wine grape harvesting operations taking into account both operational costs and grape quality. We solve a mixed-integer linear programming model to support harvest scheduling, labor allocation, and routing decisions. A quality loss function is used to represent wine quality reduction at each vineyard block due to premature or deferred harvest with respect to an optimal date. We present computational results which show that the proposed tool could be used to support grape harvest planning in a large vineyard, at both a tactical and operational level, (C) 2007 Elsevier B.V. All rights reserved.
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    Optimization Under Uncertainty in Sustainable Agriculture and Agrifood Industry
    (Springer Cham, 2024) Garafulic, Max; Mac Cawley, Alejandro F.; Péra, Thiago Guilherme; João, Abner Matheus; Caixeta Filho, José Vicente; Hampel, David; Tláskal, Martin; Janová, Jitka; Pires Ribeiro, João; Cruz, Lourenço; Barbosa Póvoa, Ana; Peinado Guerrero, Miguel; Ahumada, Omar; Ulloa, Rodrigo; Hernández Cruz, Xaimarie; Neal, Grace; Jayani, Abhay; Villalobos, J. Rene; Oliveira, Anderson; Firmino, Fabricio; Vieira Cruz, Pedro; Oliveira Sampaio, Jonice de; Serra da Cruz, Sérgio Manuel; Albornoz, Víctor M.; Zamora, Gabriel E.; Soto Silva, Wladimir E.; González Araya, Marcela C.; PlàAragonés, Lluís M.; Albornoz, Víctor M.; Mac Cawley, Alejandro; Plà-Aragonés, Lluis M.
    This book explores optimization under uncertainty and related applications in agriculture, sustainable supply chains and the agrifood industry. Rapid changes in the primary sector are leading to more and more industrialized structures, which require optimization methods in order to cope with today’s challenges. Addressing uncertainty in the agrifood industry may lead to more robust supply chain designs or to diversified risk. Sustainability requires interaction with the environmental or social sciences. This book bridges the gap between optimization theory, uncertainty, sustainability and primary-sector applications (mainly in the agriculture and food industry, but also fisheries, forestry and natural resources in general). Although it has been a major challenge for the operations research community, this urgently needed interdisciplinary collaboration is not adequately covered in most current curricula in applied mathematics, economics or (agronomic/industrial/forest) engineering. This book highlights research that can help fill this gap. The individual chapters cover applications of stochastic integer linear programming and multicriteria decision methods in agriculture. The topics addressed include uncertainty in areas such as the sugar cane industry, pig farming, and cold storage for perishable products. Large-scale sustainable food production is a growing concern; this book offers solutions to help meet global demand in agriculture by using and improving the methods of optimization theory and operations research.
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    Prediction of slaughterhouse workers' RULA scores and knife edge using low-cost inertial measurement sensor units and machine learning algorithms
    (ELSEVIER SCI LTD, 2022) Villalobos, Adolfo; Mac Cawley, Alejandro
    The high prevalence of work-related musculoskeletal disorders (WRMSDs) has been a concern in the meatprocessing industry, owing to the manual nature of the work and the high upper-limb and neck exposure to movements that can lead to WRMSD. The ability to perform an accurate and fast assessment of WRMSDs remains a challenge in industrial environments. Most assessment methodologies rely on standard survey-based methods, which are time- and labor-intensive. In this paper, we present an application of inertial measurement units (IMUs) to measure human activity, and the use of artificial intelligence and machine learning techniques to perform task classification and ergonomic assessments in workplace settings. We present the results obtained by using simple low-cost IMUs worn on slaughterhouse worker wrists to capture information on their movements. We describe the use of this information to detect the risk factors of the wrists/hands that can lead to WRMSDs. The results indicate that by using low-cost IMU-based sensors on the wrists of slaughterhouse workers, we can accurately classify the sharpness of the knife and predict the worker RULA score.
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