Abstract: Various embodiments of the present disclosure provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for determining optimized solutions to input problems in a containerized, cloud-based (e.g., serverless) manner. In one embodiment, an example method is provided. The method comprises: receiving a problem type of an input problem originating from a client computing entity; mapping the problem type to one or more selected solver types; generating one or more container instances of one or more compute containers, each compute container corresponding to a selected solver type; generating a problem output using the one or more container instances; and providing the problem output comprising a solution to the input problem to the client computing entity.

Abstract: Various embodiments of the present disclosure provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for determining optimized solutions to input problems in a containerized, cloud-based (e.g., serverless) manner using multiple solver types. In one embodiment, an example method involves receiving a problem type of an input problem originating from a client computing entity, mapping the problem type to one or more selected solver types, generating one or more container instances of one or more compute containers with each compute container corresponding to a selected solver type, generating a problem output using the one or more container instances, and providing the problem output to the client computing entity, where the problem output includes an optimized solution to the input problem that can be used to perform one or more prediction-based actions.

Abstract: Various embodiments of the present disclosure provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for determining optimized solutions to input problems in a containerized, cloud-based (e.g., serverless) manner. In one embodiment, an example method is provided. The method comprises: receiving a problem type of an input problem originating from a client computing entity; mapping the problem type to one or more selected solver types; generating one or more container instances of one or more compute containers, each compute container corresponding to a selected solver type; generating a problem output using the one or more container instances; and providing the problem output comprising a solution to the input problem to the client computing entity.

Abstract: Various embodiments of the present disclosure provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for determining optimized solutions to input problems in a containerized, cloud-based (e.g., serverless) manner. In one embodiment, an example method is provided. The method comprises: receiving a problem type of an input problem originating from a client computing entity; mapping the problem type to one or more selected solver types; generating one or more container instances of one or more compute containers, each compute container corresponding to a selected solver type; generating a problem output using the one or more container instances; and providing the problem output comprising a solution to the input problem to the client computing entity.

Abstract: Various embodiments of the present disclosure provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for determining optimized solutions to input problems in a containerized, cloud-based (e.g., serverless) manner using multiple solver types. In one embodiment, an example method involves receiving a problem type of an input problem originating from a client computing entity, mapping the problem type to one or more selected solver types, generating one or more container instances of one or more compute containers with each compute container corresponding to a selected solver type, generating a problem output using the one or more container instances, and providing the problem output to the client computing entity, where the problem output includes an optimized solution to the input problem that can be used to perform one or more prediction-based actions.

Abstract: An apparatus and method for directing a computer system to transform a plurality of data in a planning data structure into a scheduling data structure. Each entry of the scheduling data structure has at least a start date, quantity, and duration.