Abstract: A method, system and computer program product for generating a solution to an optimization problem. A received structured set of data is analyzed with the prescriptive domains to identify one or more prescriptive domains that match the received structure set of data in data structure and/or semantic terms. A user selection of one of the presented possible prescriptive intentions from the intention templates in the identified one or more prescriptive domains that match the received structure set of data in data structure and/or semantic terms is received. A prescriptive model is then generated from the prescriptive domain containing the selected prescriptive intention. The prescriptive model is translated into a technical prescriptive model using a set of mapping rules. Furthermore, the technical prescriptive model is translated into an optimization model. The optimization model is solved and an output defining a solution from the solved optimization model is presented.

Abstract: A method, system and computer program product for generating a solution to an optimization problem. A received structured set of data is analyzed with the prescriptive domains to identify one or more prescriptive domains that match the received structure set of data in data structure and/or semantic terms. A user selection of one of the presented possible prescriptive intentions from the intention templates in the identified one or more prescriptive domains that match the received structure set of data in data structure and/or semantic terms is received. A prescriptive model is then generated from the prescriptive domain containing the selected prescriptive intention. The prescriptive model is translated into a technical prescriptive model using a set of mapping rules. Furthermore, the technical prescriptive model is translated into an optimization model. The optimization model is solved and an output defining a solution from the solved optimization model is presented.

Abstract: A method, system and computer program product for generating a solution to an optimization problem. A received structured set of data is analyzed with the prescriptive domains to identify one or more prescriptive domains that match the received structure set of data in data structure and/or semantic terms. A user selection of one of the presented possible prescriptive intentions from the intention templates in the identified one or more prescriptive domains that match the received structure set of data in data structure and/or semantic terms is received. A prescriptive model is then generated from the prescriptive domain containing the selected prescriptive intention. The prescriptive model is translated into a technical prescriptive model using a set of mapping rules. Furthermore, the technical prescriptive model is translated into an optimization model. The optimization model is solved and an output defining a solution from the solved optimization model is presented.

Abstract: A method, system and computer program product for generating a solution to an optimization problem. A received structured set of data is analyzed with the prescriptive domains to identify one or more prescriptive domains that match the received structure set of data in data structure and/or semantic terms. A user selection of one of the presented possible prescriptive intentions from the intention templates in the identified one or more prescriptive domains that match the received structure set of data in data structure and/or semantic terms is received. A prescriptive model is then generated from the prescriptive domain containing the selected prescriptive intention. The prescriptive model is translated into a technical prescriptive model using a set of mapping rules. Furthermore, the technical prescriptive model is translated into an optimization model. The optimization model is solved and an output defining a solution from the solved optimization model is presented.

Abstract: A method, system and computer program product for generating a solution to an optimization problem. A received structured set of data is analyzed with the prescriptive domains to identify one or more prescriptive domains that match the received structure set of data in data structure and/or semantic terms. A user selection of one of the presented possible prescriptive intentions from the intention templates in the identified one or more prescriptive domains that match the received structure set of data in data structure and/or semantic terms is received. A prescriptive model is then generated from the prescriptive domain containing the selected prescriptive intention. The prescriptive model is translated into a technical prescriptive model using a set of mapping rules. Furthermore, the technical prescriptive model is translated into an optimization model. The optimization model is solved and an output defining a solution from the solved optimization model is presented.

Abstract: A method, apparatus and computer program product for solving an optimization model by automatically creating alternative formulations, and solving those with parallel solution approaches communicating with each other. The method: automatically generates alternative formulations for a given optimization model; executes parallel communicating solution approaches in a parallel computing infrastructure in order to solve a given set of alternative model formulations; utilizes a mechanism to automatically detect the model structure and a mechanism to automatically detect the appropriate solution approach(es) for a given model structure, and to launch multiple parallel solution approaches at existing optimization solvers. The system and methods enable communication between parallel solution approaches in order to improve performance. The system communicates information between the parallel solution approaches during a solve process, in order to improve performance.

Abstract: A method, system, and computer program product are disclosed for guiding users in optimization-based planning under uncertainty. In one embodiment, the invention provides a method comprising identifying one or more characterizations of a specified uncertainty in a defined process; generating a set of plans based on the uncertainty characterization; and finding a new plan based on the existing set of plans, including identifying an added constraint, and finding a new plan that satisfies this added constraint. The new plan is analyzed to determine whether the new plan satisfies defined criteria; and when the new plan satisfies the defined criteria, the new plan is added to the set of plans. One of the plans is identified as a recommended plan for the defined process. In an embodiment, the recommended plan is identified based on a trade-off analysis of the plans using at least two defined aspects of the plans.