Abstract: A process for optimizing a portfolio of products produced from a crop includes the use of an objective function to determine optimized quantities of the products in the portfolio. The objective function, for example, includes quantity terms for the products. The objective function may also include additional terms such as an energy term and/or a storage term. The energy term, for example, relates to an amount of energy required to produce the products. The storage term, for example, relates to the cost of storing products. The crop, for example, may be sugarcane, and the products, for example, may be sugar, molasses, bagasse, biofuel, electricity, and/or carbon credits.

Abstract: A product recovery prediction model that models recovery of a product from a crop is generated by inputting training product recovery data by date, age, and variety. A first model that models season dependent effects on product recovery, and/or a second model that models age dependent effects on product recovery, and/or a third model that models other effects such as, for example, weather dependent effects on product recovery are generated. The first, second, and/or third models are combined, and the product recovery prediction model is generated based on the combined first, second, and/or third models and on the training product recovery data. The crop may be sugarcane, and the product may be sugar. The product recovery prediction model may be used to predict recovery of the product to use for harvesting or any economical decisions.

Abstract: A process for optimizing a portfolio of products produced from a crop includes the use of an objective function to determine optimized quantities of the products in the portfolio. The objective function, for example, includes quantity terms for the products. The objective function may also include additional terms such as an energy term and/or a storage term. The energy term, for example, relates to an amount of energy required to produce the products. The storage term, for example, relates to the cost of storing products. The crop, for example, may be sugarcane, and the products, for example, may be sugar, molasses, bagasse, biofuel, electricity, and/or carbon credits.

Abstract: Raw rolls and/or sheets of flat sheet stock are charted by a processing system that generates charting patterns using constraint logic programming, that matches the charting patterns and available raw rolls and/or sheets of flat sheet stock, and that selects the optimal patterns from those matching charting patterns and available raw rolls and/or sheets using mixed integer linear programming.

Abstract: A harvesting and/or planting schedule is generated based on a product recovery model and a crop yield model. The product recovery model models recovery of a product, such as sugar, from a crop, such as sugarcane. The crop yield model models yield of the crop from land. First, second, third, and fourth data are used to generate the harvesting and/or planting schedule. The first input data is pertinent to predicting the recovery of the product by use of the product recovery model. The second input data is pertinent to predicting the yield of the crop by use of the crop yield model. The third input data relates to capacity of a crop processing plant to process the crop to produce the product. The fourth input data relates to harvesting and/or planting practices for the crop.

Abstract: A combination of yield prediction models is usable to predict the yield of a crop, such as sugarcane, from land. The model combination includes at least first and/or second models. The first model may be a structured or unstructured model that models season dependent effects on yield. If structured, the first model may be a linear, non-linear, or polynomial representation. The second model may be a structured or unstructured model that models age dependent effects on yield. If structured, the second model may be a linear, non-linear, or polynomial representation. Additional models that model weather and/or soil dependent effects on yield may also be used in the model combination.

Abstract: Periodic communication of data packets between modules in time frames having a plurality of frame rates including a base frame rate through a bus is schedule by determining a first load schedule for data packets of base frame and half base frame rates using constraint logic programming techniques, by determining a second load schedule for data packets of other frame rates using mixed integer linear programming techniques, and by scheduling produce and consume loads for each of the modules based on the first and second load schedules.

Abstract: Cutting and skiving patterns to be used in the production of product rolls from a master roll are selected so as to optimize trim efficiency. The product rolls have widths ordered by customers. In selecting these cutting and skiving patterns, cutting patterns that can be used to trim master rolls of the same or different dimensions in order to fill customer orders for the product rolls are generated, skiving patterns for skiving auxiliary rolls that result from trimming the master roll in order to produce the product rolls according to the customer orders are generated, and those of the generated skiving and cutting patterns that optimize trim efficiency for the production of the product rolls are selected.

Abstract: A product recovery prediction model that models recovery of a product from a crop is generated by inputting training product recovery data by date, age, and variety. A first model that models season dependent effects on product recovery, and/or a second model that models age dependent effects on product recovery, and/or a third model that models other effects such as, for example, weather dependent effects on product recovery are generated. The first, second, and/or third models are combined, and the product recovery prediction model is generated based on the combined first, second, and/or third models and on the training product recovery data. The crop may be sugarcane, and the product may be sugar. The product recovery prediction model may be used to predict recovery of the product to use for harvesting or any economical decisions.