Crop harvest scheduling on a rolling horizon basis : a farm-dedicated operational application

International audienceCrop harvest organization is the pivotal stage in the cereal production circuit due to its high cost and impact on the returns earned. Considering stochastic and dynamic aspects of nature of this field, we propose a rolling multistep chance-constrained model for harvest scheduling problem that minimize the crop quality degradation under weather uncertainty. To solve this, a dynamic approach is also developed. Obtained results on data from a case study encountered at a French cooperative, exhibit accurate solutions devoted to help efficiently managing the harvesting activity at a farm level

A discrete event simulation model for harvest operations under stochastic conditions

International audienceThis study presents an application of stochastic discrete-event simulation modelling for harvesting, transportation and storage activities of one grain and oilseed agricultural cooperative. Gathering the harvest represents an important stage for both agricultural cooperatives and individual farmers, that requires an efficient management in order to ensure a high production quality and yield. For the purpose to take into account the intricacy and the dynamic behaviour of the studied system, the model proposed here, considers various inherent heterogeneous parameters, such as: daily meteorological uncertainty, loss queuing networks, farmers contractual delivery policies, etc. This paper applies discrete and stochastic simulation techniques in order to analyse and evaluate the performance of the cooperative supply chain system. Moreover, it enables to investigate alternative configurations and strategies of its operations for an eventual supply chain redesign

Predictive modelling with panel data and multivariate adaptive regression splines: case of farmers crop delivery for a harvest season ahead

International audienceThis paper investigates a harvest-season level unbalanced panel data (PD) of farmers crop delivery for monitoring the gathering activity and for aiding to support reception and storage decisions making of an agricultural cooperative. To achieve these purposes, the fitting and the prediction of the daily farmers crop delivery quantities were realised based-on the total expected quantity of the whole harvest season, the daily volume of precipitation and the amount of sunshine. In order to capture and extrapolate data patterns, both the PD regression and the multivariate adaptive regression approaches were implemented and tested for a real life agricultural cooperative case study. The obtained results exhibit an accurate predictive modelling of the farmers crop delivery behaviour for harvest seasons ahead

A scenario-based approach for quality risk management: case of annual crops scheduling

International audienceThis 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