International Business Machines Corporation

Stephane Michel

Alain Chabrier

Xavier Ceugniet

Susara A Van Den Heever

Roman Vaculin

Lam Nguyen

Yuan Yuan Jia

Surya Shravan Kumar Sajja

Kanthi Sarpatwar

Viu-Long Kong

Amelie Hocquette

A computer implemented method using an artificial intelligence (A.I.) module to explain large scale scheduling solutions includes receiving an original instance of a resource constrained scheduling problem. The instance includes a set of tasks and a variety of resource requirements and a variety of constraints. An optimizer process determines a schedule for the set of tasks while minimizing a makespan of the schedule. A minimal set of resource links is generated based on resource dependencies between tasks. The resource links are added to the original instance of scheduling problem, as precedence constraints. All the resource constraints are removed from the original instance of the resource constrained scheduling problem. A set of critical tasks is computed using a non-resource constrained critical path. Schedules are provided with an explanation of an optimized order of the set of tasks based on the use of the non-resource constrained critical path.

Optimizer agnostic explanation system for large scale schedules

A method, system and computer program product for generating an optimization model. A prescriptive model including a structured set of data and instance(s) of templates of a technical and/or business prescriptive domain is received, where the instances of the templates define goals and/or constraints for the structured set of data. The business prescriptive domain extends the technical prescriptive domain defining a generic optimization problem and the business prescriptive domain defines a more specific business problem. The prescriptive model is translated into a technical prescriptive model using a set of mapping rules to translate the instance(s) of the templates of the business prescriptive domain into instance(s) of the templates of the technical prescriptive domain from which the business prescriptive domain extends. Furthermore, the technical prescriptive model is translated into an optimization model, where the optimization model is solved. An output defining a solution from the solved optimization model is then presented.

Generation of an optimization model

A method, system and computer program product for operating an expert system. Technical prescriptive domains and business prescriptive domains containing one or more templates are accessed, where each template defines an intention, goal or constraint. Furthermore, each technical prescriptive domain defines a generic optimization problem and each business prescriptive domain extends a technical prescriptive domain and defines a more specific business problem. A structure of data is received and analyzed with the technical prescriptive domains and business prescriptive domains to identify technical and/or business prescriptive domains that match the received structure set of data in data structure and/or semantic terms. A prescriptive model from the technical or business prescriptive domain containing a selected prescriptive intention from the intention templates in the identified technical and/or business prescriptive domains that match the received structure set of data in data structure and/or semantic terms is then generated.

Expert system

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.

Generating a solution for an optimization problem

Method and system are provided for automated partitioning of transportation routing problems. The method includes: determining a threshold number of shipments per partition; selecting a geographic center; mapping delivery and/or pickup sites at geographic locations; scanning radially around the geographic center to determine the sparsest or densest region of sites and selecting a starting point in this region; and progressing from the starting point radially around the geographic center aggregating sites into partitions with a maximum of the threshold number of shipments in a partition. The method may include: solving each partitioned instance of a problem to generate one or more optimized routes; and creating a union of all the instances solutions.

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Roquefort les Pins, France

Stephane Michel