Mitsubishi Electric Research Laboratories, Inc.

David Bergman

Arvind Raghunathan

Thiago Serra

Hiroyuki Hashimoto

Nikolaos V Sahinidis

Systems and methods for scheduling early riders (ERs) and late riders (LRs) to vehicles in a multi-modal transportation network (MTN). Stored instructions, when executed, cause a processor to perform acts of forecasting a finite set of scenarios, each scenario having a possible set of forecast LRs (FLRs) itinerary requests. Iteratively, generate ER groups and FLR groups for each scenario, based on a desired time of arrival at a destination. Assign a commuter vehicle (CV) for each ER and FLR group in each scenario. Iteratively, determine for each ER and FLR group a start time and an arrival time at the destination in the corresponding CV, for which, the ER and FLR group are assigned. The iterations continue until a joint schedule for the ERs and the FLRs form each scenario that minimizes an objective function. Formulate assignment information, and transmit the assignment information to the ERs and the assigned CVs.

System and method for scheduling multiple modes of transport with incomplete information

A power generation planning system for controlling on/off sequence of generators according to operational parameters includes an interface to receive the operational parameters including a power demand, state-data of the generators and operational histories of the generators from a power control system, a memory to store an objective function, a mixed-integer programming solver, generator parameters of each the generators and planning modules including a state-space representation module, a variable assignment module, a network flow module and a tight constraint module, a processor to perform the planning modules based on the operational parameters received by the interface. The processor is configured to construct decision diagrams for each of the generators by using the state-space representation module, generate arc-variables representing state-transformations of the generators by assigning binary variables to arcs of the decision, generate network flow constraints to represent feasible operations of each of the generators, generate tight constraints of each of the generators by using the tight constraint module and formulate a mixed-integer problem, solve the mixed-integer problem, by using the mixed-integer programming solver, based on the states-data of the generators, the network flow constraints and the tight constraints, and transmit on/off sequence data of each of the generators obtained from the solved mixed-integer problem to a power control system via the interface.

System and method for scheduling electric generators using decision diagrams

Methods and systems for servicing of machines by workers within a period of time. Acquiring for each service an available servicing time, a time duration for servicing, a location of the machine, and a number of workers having appropriate qualifications to be concurrently present for a service. Acquiring for each worker a worker availability, qualifications and location. Determining a cost function representing a service schedule for each worker, wherein an optimization of the cost function is subject to constraints. The constraints include a number of workers with qualifications concurrently present for a service, each worker starts and ends the period of time at the same location and travels independently from other workers. The cost function includes maximizing a number of services to be performed; minimizing a number of workers required to perform servicing for each service; or minimizing a total travel time for each worker to the location.

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Wethersfield, CT, United States of America

David Bergman