The Experts below are selected from a list of 122985 Experts worldwide ranked by ideXlab platform
Nacima Labadie - One of the best experts on this subject based on the ideXlab platform.
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A multi-step rolled forward chance-constrained model and a proactive dynamic approach for the wheat Crop Quality control problem
European Journal of Operational Research, 2015Co-Authors: Valeria Borodin, Jean Bourtembourg, Faicel Hnaien, Nacima LabadieAbstract:Handling weather uncertainty during the harvest season is an indispensable aspect of seed gathering activities. More precisely, the focus of this study refers to the multi-period wheat Quality control problem during the Crop harvest season under meteorological uncertainty. In order to alleviate the problem curse of dimensionality and to reflect faithfully exogenous uncertainties revealed progressively over time, we propose a multi-step joint chance-constrained model rolled forward step-by-step. This model is subsequently solved by a proactive dynamic approach, specially conceived for this purpose. Based on real-world derived instances, the obtained computational results exhibit proactive and accurate harvest scheduling solutions for the wheat Crop Quality control problem.
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A Quality risk management problem: case of annual Crop harvest scheduling
International Journal of Production Research, 2013Co-Authors: Valeria Borodin, Jean Bourtembourg, Faicel Hnaien, Nacima LabadieAbstract:This paper presents a stochastic optimisation model for the annual harvest scheduling problem of the farmers’ entire cereal Crop production at optimum maturity. Gathering the harvest represents an important stage for both agricultural cooperatives and individual farmers due to its high cost and considerable impact on seed Quality and yield. The meteorological conditions represent the deciding factor that affects the harvest scheduling and progress. Using chance-constrained programming, a mixed-integer probabilistically constrained model is proposed, with a view to minimising the risk of Crop Quality degradation under climate uncertainty with a safe confidence level. The chance-constrained optimisation problem is tackled and solved via an equivalent linear mixed-integer reformulation jointly with scenario-based approaches. Moreover, a new concept of -scenario pertinence is introduced in order to defy efficiently the probabilistically constrained problem complexity and time limitations. From the practical standpoint, this study is aimed at helping an agricultural cooperative in decision-making on Crop Quality risk management and harvest scheduling over a medium time horizon (10–15 time periods).
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A scenario-based approach for Quality risk management: case of annual Crops scheduling
2012Co-Authors: Valeria Borodin, Jean Bourtembourg, Faicel Hnaien, Nacima LabadieAbstract:This paper presents a stochastic optimization model that establishes the harvest scheduling of the entire farmers Crop at optimum maturity. Gathering the harvest represents an important stage for the agricultural cooperatives and individual farmers production that involves the risk control of Crop Quality and safety degradation. In this sense, meteorological conditions represent the determinative factor that affects the harvest scheduling during which Crops are gathered. Hereby, using chance constraints programming, we propose a mixed integer stochastic model with a view to minimizing the risk of Crop Quality degradation under the climate uncertainty with a safe confidence level.
Valeria Borodin - One of the best experts on this subject based on the ideXlab platform.
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A multi-step rolled forward chance-constrained model and a proactive dynamic approach for the wheat Crop Quality control problem
European Journal of Operational Research, 2015Co-Authors: Valeria Borodin, Jean Bourtembourg, Faicel Hnaien, Nacima LabadieAbstract:Handling weather uncertainty during the harvest season is an indispensable aspect of seed gathering activities. More precisely, the focus of this study refers to the multi-period wheat Quality control problem during the Crop harvest season under meteorological uncertainty. In order to alleviate the problem curse of dimensionality and to reflect faithfully exogenous uncertainties revealed progressively over time, we propose a multi-step joint chance-constrained model rolled forward step-by-step. This model is subsequently solved by a proactive dynamic approach, specially conceived for this purpose. Based on real-world derived instances, the obtained computational results exhibit proactive and accurate harvest scheduling solutions for the wheat Crop Quality control problem.
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A Quality risk management problem: case of annual Crop harvest scheduling
International Journal of Production Research, 2013Co-Authors: Valeria Borodin, Jean Bourtembourg, Faicel Hnaien, Nacima LabadieAbstract:This paper presents a stochastic optimisation model for the annual harvest scheduling problem of the farmers’ entire cereal Crop production at optimum maturity. Gathering the harvest represents an important stage for both agricultural cooperatives and individual farmers due to its high cost and considerable impact on seed Quality and yield. The meteorological conditions represent the deciding factor that affects the harvest scheduling and progress. Using chance-constrained programming, a mixed-integer probabilistically constrained model is proposed, with a view to minimising the risk of Crop Quality degradation under climate uncertainty with a safe confidence level. The chance-constrained optimisation problem is tackled and solved via an equivalent linear mixed-integer reformulation jointly with scenario-based approaches. Moreover, a new concept of -scenario pertinence is introduced in order to defy efficiently the probabilistically constrained problem complexity and time limitations. From the practical standpoint, this study is aimed at helping an agricultural cooperative in decision-making on Crop Quality risk management and harvest scheduling over a medium time horizon (10–15 time periods).
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A scenario-based approach for Quality risk management: case of annual Crops scheduling
2012Co-Authors: Valeria Borodin, Jean Bourtembourg, Faicel Hnaien, Nacima LabadieAbstract:This paper presents a stochastic optimization model that establishes the harvest scheduling of the entire farmers Crop at optimum maturity. Gathering the harvest represents an important stage for the agricultural cooperatives and individual farmers production that involves the risk control of Crop Quality and safety degradation. In this sense, meteorological conditions represent the determinative factor that affects the harvest scheduling during which Crops are gathered. Hereby, using chance constraints programming, we propose a mixed integer stochastic model with a view to minimizing the risk of Crop Quality degradation under the climate uncertainty with a safe confidence level.
Jean Bourtembourg - One of the best experts on this subject based on the ideXlab platform.
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A multi-step rolled forward chance-constrained model and a proactive dynamic approach for the wheat Crop Quality control problem
European Journal of Operational Research, 2015Co-Authors: Valeria Borodin, Jean Bourtembourg, Faicel Hnaien, Nacima LabadieAbstract:Handling weather uncertainty during the harvest season is an indispensable aspect of seed gathering activities. More precisely, the focus of this study refers to the multi-period wheat Quality control problem during the Crop harvest season under meteorological uncertainty. In order to alleviate the problem curse of dimensionality and to reflect faithfully exogenous uncertainties revealed progressively over time, we propose a multi-step joint chance-constrained model rolled forward step-by-step. This model is subsequently solved by a proactive dynamic approach, specially conceived for this purpose. Based on real-world derived instances, the obtained computational results exhibit proactive and accurate harvest scheduling solutions for the wheat Crop Quality control problem.
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A Quality risk management problem: case of annual Crop harvest scheduling
International Journal of Production Research, 2013Co-Authors: Valeria Borodin, Jean Bourtembourg, Faicel Hnaien, Nacima LabadieAbstract:This paper presents a stochastic optimisation model for the annual harvest scheduling problem of the farmers’ entire cereal Crop production at optimum maturity. Gathering the harvest represents an important stage for both agricultural cooperatives and individual farmers due to its high cost and considerable impact on seed Quality and yield. The meteorological conditions represent the deciding factor that affects the harvest scheduling and progress. Using chance-constrained programming, a mixed-integer probabilistically constrained model is proposed, with a view to minimising the risk of Crop Quality degradation under climate uncertainty with a safe confidence level. The chance-constrained optimisation problem is tackled and solved via an equivalent linear mixed-integer reformulation jointly with scenario-based approaches. Moreover, a new concept of -scenario pertinence is introduced in order to defy efficiently the probabilistically constrained problem complexity and time limitations. From the practical standpoint, this study is aimed at helping an agricultural cooperative in decision-making on Crop Quality risk management and harvest scheduling over a medium time horizon (10–15 time periods).
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A scenario-based approach for Quality risk management: case of annual Crops scheduling
2012Co-Authors: Valeria Borodin, Jean Bourtembourg, Faicel Hnaien, Nacima LabadieAbstract:This paper presents a stochastic optimization model that establishes the harvest scheduling of the entire farmers Crop at optimum maturity. Gathering the harvest represents an important stage for the agricultural cooperatives and individual farmers production that involves the risk control of Crop Quality and safety degradation. In this sense, meteorological conditions represent the determinative factor that affects the harvest scheduling during which Crops are gathered. Hereby, using chance constraints programming, we propose a mixed integer stochastic model with a view to minimizing the risk of Crop Quality degradation under the climate uncertainty with a safe confidence level.
Faicel Hnaien - One of the best experts on this subject based on the ideXlab platform.
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A multi-step rolled forward chance-constrained model and a proactive dynamic approach for the wheat Crop Quality control problem
European Journal of Operational Research, 2015Co-Authors: Valeria Borodin, Jean Bourtembourg, Faicel Hnaien, Nacima LabadieAbstract:Handling weather uncertainty during the harvest season is an indispensable aspect of seed gathering activities. More precisely, the focus of this study refers to the multi-period wheat Quality control problem during the Crop harvest season under meteorological uncertainty. In order to alleviate the problem curse of dimensionality and to reflect faithfully exogenous uncertainties revealed progressively over time, we propose a multi-step joint chance-constrained model rolled forward step-by-step. This model is subsequently solved by a proactive dynamic approach, specially conceived for this purpose. Based on real-world derived instances, the obtained computational results exhibit proactive and accurate harvest scheduling solutions for the wheat Crop Quality control problem.
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A Quality risk management problem: case of annual Crop harvest scheduling
International Journal of Production Research, 2013Co-Authors: Valeria Borodin, Jean Bourtembourg, Faicel Hnaien, Nacima LabadieAbstract:This paper presents a stochastic optimisation model for the annual harvest scheduling problem of the farmers’ entire cereal Crop production at optimum maturity. Gathering the harvest represents an important stage for both agricultural cooperatives and individual farmers due to its high cost and considerable impact on seed Quality and yield. The meteorological conditions represent the deciding factor that affects the harvest scheduling and progress. Using chance-constrained programming, a mixed-integer probabilistically constrained model is proposed, with a view to minimising the risk of Crop Quality degradation under climate uncertainty with a safe confidence level. The chance-constrained optimisation problem is tackled and solved via an equivalent linear mixed-integer reformulation jointly with scenario-based approaches. Moreover, a new concept of -scenario pertinence is introduced in order to defy efficiently the probabilistically constrained problem complexity and time limitations. From the practical standpoint, this study is aimed at helping an agricultural cooperative in decision-making on Crop Quality risk management and harvest scheduling over a medium time horizon (10–15 time periods).
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A scenario-based approach for Quality risk management: case of annual Crops scheduling
2012Co-Authors: Valeria Borodin, Jean Bourtembourg, Faicel Hnaien, Nacima LabadieAbstract:This paper presents a stochastic optimization model that establishes the harvest scheduling of the entire farmers Crop at optimum maturity. Gathering the harvest represents an important stage for the agricultural cooperatives and individual farmers production that involves the risk control of Crop Quality and safety degradation. In this sense, meteorological conditions represent the determinative factor that affects the harvest scheduling during which Crops are gathered. Hereby, using chance constraints programming, we propose a mixed integer stochastic model with a view to minimizing the risk of Crop Quality degradation under the climate uncertainty with a safe confidence level.
Du Tai - One of the best experts on this subject based on the ideXlab platform.
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Water-Saving and Crop Quality Improvement of Alternate Partial Root-Zone Irrigation and Application of Isotope Technology in the Research of Crop Water Use
Plant Physiology, 2011Co-Authors: Du TaiAbstract:Alternate partial root-zone irrigation (APRI) is a new irrigation technique and requires that approximately half of the root system is exposed to drying soil while the remaining half is irrigated as in full irrigation. This irrigation method can reduce plant water consumption and is based on theory of root-to-shoot long distance signaling and regulation. To understand its mechanism and other effects on the Crop Quality, it is important to investigate the water movements and hydraulic connections between the dry and wet root-zones and from soil to the roots, xylem, leaves and fruits. Stable hydrogen isotopic tracing and carbon isotopic implicating technology provides a promising tool for such research. In this review, the effects of APRI on water-saving and fruit Quality improving; application of stable hydrogen isotopic tracing in water transformation and carbon isotopic implicating on water use efficiency are discussed; and some important scientific issues which should be studied in the future are also commented. It is expected that APRI should have the potential to improve Crop water use efficiency, maintain yield and enhance product Quality so that agricultural resources are most effectively used.