This paper presents an application of the simulation of discrete events (SED) using ARENATM in the management of large-scale breeding farms. The main objective of the simulation model is to find a policy of replacemen...This paper presents an application of the simulation of discrete events (SED) using ARENATM in the management of large-scale breeding farms. The main objective of the simulation model is to find a policy of replacement, to ensure the best economic performance of a farm. The only variant analyzed of replacement policy was the number of cycles set in permanency for a sow in the herd. Considered incomes come from the sale of piglets and unproductive sows, and costs are due to the feeding of animals, replacement sows purchases, and the operation expenses of the farm. For this analysis, the production process was divided into three major stages called: mating, pregnancy or gestation and lactation. The sow’s movement from one stage to other was modeled by cycle-dependent transition probabilities. Considering the daily utility, as response variable, the model shows the best number of cycles to maintain the sows.展开更多
基金Instituto Tecnologico y de Estudios Superiores de Monterrey(Scholarship)Universidad Autonoma del Estado de Hidalgo,Secretaria de Educacion Publica(Complement scholarship)University of Lleida(Scholarship).
文摘This paper presents an application of the simulation of discrete events (SED) using ARENATM in the management of large-scale breeding farms. The main objective of the simulation model is to find a policy of replacement, to ensure the best economic performance of a farm. The only variant analyzed of replacement policy was the number of cycles set in permanency for a sow in the herd. Considered incomes come from the sale of piglets and unproductive sows, and costs are due to the feeding of animals, replacement sows purchases, and the operation expenses of the farm. For this analysis, the production process was divided into three major stages called: mating, pregnancy or gestation and lactation. The sow’s movement from one stage to other was modeled by cycle-dependent transition probabilities. Considering the daily utility, as response variable, the model shows the best number of cycles to maintain the sows.
