Abstract: Embodiments for improving travel mobility as a service (MaaS) by one or more processors. A selected mode of transportation may be matched with a selected route to generate a travel package according to a multi-objective model based on a route profile for a plurality of routes, a user profile of the one or more users, an environmental profile, and a collaboration of monitored data relating to preferences of a mode of transportation and routes of the one or more users, wherein the travel package includes at least the matching selected mode of transportation, the selected route, and one or more travel suggestions.

Abstract: Embodiments for targeted learning and recruitment by a processor. Attributes obtained from a user profile are used to identify a user as a potential candidate for performing an activity related to either the recruiting or the learning and providing a customized learning experience with targeted course suggestions for the user.

Abstract: An apparatus, method and computer program product for scheduling on/off control of equipment. The method implements an iterative approach that uses one objective and sets a tolerance value on the other objectives. Initially, the method computes the value of the different objective functions given a current feasible solution. The different objective functions are iteratively evaluated with respect to the current solution and a margin is set for every objective at each iteration. The margin is a deviation measure indicating the acceptable range by which each objective can be worsened. The method iteratively optimizes each objective function with respect to one of the objectives, following an order, while enforcing a maximum deviation on the other objective functions by setting them as constraints. The allowable deviation margin is then decreased for all the objective functions. A final output schedule is provided from which signals may be generated to automatically turn on/off the equipment.

Abstract: Embodiments for improving travel mobility as a service (MaaS) by one or more processors. A selected mode of transportation may be matched with a selected route to generate a travel package according to a multi-objective model based on a route profile for a plurality of routes, a user profile of the one or more users, an environmental profile, and a collaboration of monitored data relating to preferences of a mode of transportation and routes of the one or more users, wherein the travel package includes at least the matching selected mode of transportation, the selected route, and one or more travel suggestions.

Abstract: Embodiments for targeted learning and recruitment by a processor. Attributes obtained from a user profile are used to identify a user as a potential candidate for performing an activity related to either the recruiting or the learning and providing a customized learning experience with targeted course suggestions for the user.

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: An apparatus, method and computer program product for scheduling on/off control of equipment. The method implements an iterative approach that uses one objective and sets a tolerance value on the other objectives. Initially, the method computes the value of the different objective functions given a current feasible solution. The different objective functions are iteratively evaluated with respect to the current solution and a margin is set for every objective at each iteration. The margin is a deviation measure indicating the acceptable range by which each objective can be worsened. The method iteratively optimizes each objective function with respect to one of the objectives, following an order, while enforcing a maximum deviation on the other objective functions by setting them as constraints. The allowable deviation margin is then decreased for all the objective functions. A final output schedule is provided from which signals may be generated to automatically turn on/off the equipment.