Abstract: The present invention makes it possible to supply transportation capacity suitable for movement demand in numerous circumstances.

Abstract: A system calculates, by executing an optimum parameter model, an optimum parameter group used to generate an allocation plan based on input data including all or part of allocation information of human resources in an organization. The optimum parameter model is a machine learning model to which the input data is input and from which an optimum parameter group is output. For each time point, the system generates an allocation plan based on the input data corresponding to the time point and based on a given parameter group, and calculates an optimum parameter group based on the allocation plan, correct data that obeys manual correction of the allocation plan, and the given parameter group. The system performs learning of the optimum parameter model based on a data set (including the input data or a characteristic amount of the input data, and the optimum parameter group) for each time point.

Abstract: The present invention makes it possible to supply transportation capacity suitable for movement demand in numerous circumstances.

Abstract: Supply and demand of resources are controlled while satisfying both a resource providing condition and a resource use condition. A resource mediation server 1 predicts an arrival clock time in a case of no occurrence of a railway car accident P3, an arrival clock time after recovery from the railway car accident P3, and an arrival clock time in a case of using a special bus on the basis of a damage scale due to the railway car accident P3, delivers prediction information to each passenger, delivers a special bus service plan to a bus company, provides a condition for using the special bus to the passenger and the bus company on the basis of the prediction information about the arrival clock time, and conducts mediation of the number of special buses and a fare of each special bus between the passenger and the bus company.

Abstract: A schedule arbitration server is configured to obtain a first scheduling constraint and first schedule information from a first server, and obtain a second scheduling constraint and second schedule information from a second server; detect a discrepancy between the first schedule information and the second schedule information, based on the first scheduling constraint, the first schedule information, the second scheduling constraint, and the second schedule information, and on overall optimization constraint information; recreate a schedule when a discrepancy is detected, by creating third and fourth schedule information through modification of the first schedule information and the second schedule information; calculate a first credit for the first server and a second credit for the second server, based on degrees of modification; and send the third schedule information and the first credit to the first server, and send the fourth schedule information and the second credit to the second server.

Abstract: A plan coordination system includes a processing device configured to perform: processing that generates public data from definition data of plans, receives definitions by a user for information coordinated between plans among process procedures indicated by the public data, applies the definitions by the user to the definition data of the plan, and generates a plan coordination model; processing that notifies the data to a management device of another plan as a coordination destination in the coordination relation at a change event on the definition data, changes the plan coordination model based on a generation result of the other plan reflecting the change event, and notifies the changed data to the management device of the other plan having the coordination relation with the plan changed along with this change; and processing that outputs a generation result of each plan when planning is completed for all plans.