Abstract: The disclosure concerns a computer-implemented method of optimizing the cash level of a cash device (20), wherein the cash level indicates an amount of currency in at least one cash box of the cash device (20), wherein the method comprises the following steps: iteratively optimizing, in an automated manner, a calibration (26) of the cash device (20), wherein the calibration (26) comprises a plurality of parameters; and generating a plan (29) based at least in part on the calibration (26), wherein the plan (29) indicates when and/or how the cash device (20) is to be serviced.

Abstract: The present invention concerns a cryptocurrency-cash gateway, comprising a wallet interface (403) for communicating over a network with at least one crypto wallet (102) and an EFT gateway interface (404) for communicating over a network with at least one electronic funds transfer, EFT, gateway or financial institution (407), wherein the cryptocurrency-cash gateway is operable to perform a cryptocurrency-to-cash transaction for allowing a user to withdraw cash at an automatic transaction machine, ATM (110), or a point-of-sale, POS, system (110B) by exchanging a corresponding amount of a cryptocurrency from the crypto wallet(s); or conversely allowing the user to deposit cash at an ATM or POS system and exchanging it for a corresponding amount of a cryptocurrency to be deposited in at least one crypto wallet of the user.

Abstract: Generating an optimized pricing plan includes accessing a hierarchy comprising a sequence of levels, where each level includes an objective function and a set of constraints associated with the objective function. A mathematical programming model representing a pricing plan problem is determined for an item group comprising items. The following is repeated for each level of the sequence of levels: selecting a level comprising an objective function and a set of constraints associated with the objective function, determining an optimized boundary for the objective function, and adding a constraint generated from the optimized boundary to the set of constraints of a next level. An objective function of a last level of the sequence is optimized subject to the set of constraints associated with the objective function to yield an optimized result. An optimized pricing plan is generated in accordance with the optimized result.

Abstract: Generating an optimized pricing plan includes accessing a hierarchy comprising a sequence of levels, where each level includes an objective function and a set of constraints associated with the objective function. A mathematical programming model representing a pricing plan problem is determined for an item group comprising items. The following is repeated for each level of the sequence of levels: selecting a level comprising an objective function and a set of constraints associated with the objective function, determining an optimized boundary for the objective function, and adding a constraint generated from the optimized boundary to the set of constraints of a next level. An objective function of a last level of the sequence is optimized subject to the set of constraints associated with the objective function to yield an optimized result. An optimized pricing plan is generated in accordance with the optimized result.

Abstract: In one aspect, a computer-implemented method is provided for aggregating and scheduling product batches in a manufacturing environment. Using a batch aggregation engine implementing a mathematical programming strategy, one or more product demands are allocated to one or more product batches having suggested sizes and suggested starting times. The mathematical programming strategy includes evaluating a number of time-based penalties relative to one another in allocating the demands to the batches, the time-based penalties being based on relationships between suggested starting times for batches and times of demands being considered for allocation to batches. The suggested sizes, the suggested starting times, and feedback relating to the suggested sizes and suggested starting times are communicated from the batch aggregation engine to a scheduling engine to assist the scheduling engine in scheduling starting times for the batches.

Abstract: In one aspect, a computer-implemented method is provided for aggregating and scheduling product batches in a manufacturing environment. Using a batch aggregation engine implementing a mathematical programming strategy, one or more product demands are allocated to one or more product batches having suggested sizes and suggested starting times. The mathematical programming strategy includes evaluating a number of time-based penalties relative to one another in allocating the demands to the batches, the time-based penalties being based on relationships between suggested starting times for batches and times of demands being considered for allocation to batches. The suggested sizes, the suggested starting times, and feedback relating to the suggested sizes and suggested starting times are communicated from the batch aggregation engine to a scheduling engine to assist the scheduling engine in scheduling starting times for the batches.

Abstract: In one aspect, a computer-implemented method is provided for aggregating and scheduling product batches in a manufacturing environment. Using a batch aggregation engine implementing a mathematical programming strategy, one or more product demands are allocated to one or more product batches having suggested sizes and suggested starting times. The mathematical programming strategy includes evaluating a number of time-based penalties relative to one another in allocating the demands to the batches, the time-based penalties being based on relationships between suggested starting times for batches and times of demands being considered for allocation to batches. The suggested sizes, the suggested starting times, and feedback relating to the suggested sizes and suggested starting times are communicated from the batch aggregation engine to a scheduling engine to assist the scheduling engine in scheduling starting times for the batches.

Abstract: In one embodiment, a method is provided for collaboratively solving an optimization problem using at least first optimization software and second optimization software each having at least partial information concerning the optimization problem.

Abstract: In one embodiment, a method is provided for collaboratively solving an optimization problem using at least first optimization software and second optimization software each having at least partial information concerning the optimization problem.

Abstract: Generating an optimized pricing plan includes accessing a hierarchy comprising a sequence of levels, where each level includes an objective function and a set of constraints associated with the objective function. A mathematical programming model representing a pricing plan problem is determined for an item group comprising items. The following is repeated for each level of the sequence of levels: selecting a level comprising an objective function and a set of constraints associated with the objective function, determining an optimized boundary for the objective function, and adding a constraint generated from the optimized boundary to the set of constraints of a next level. An objective function of a last level of the sequence is optimized subject to the set of constraints associated with the objective function to yield an optimized result. An optimized pricing plan is generated in accordance with the optimized result.

Abstract: A computer-based system (10) for aggregating and scheduling product batches (50) includes a batch aggregation engine (20) that allocates one or more product demands (40) to one or more product batches (50) having suggested sizes and suggested starting times. The batch aggregation engine (20) communicates to a scheduling engine (30) the suggested batch sizes, the suggested starting times, and feedback (26) relating to the suggested sizes and suggested starting times to assist the scheduling engine (30) in scheduling a start time for each product batch (50). The system (10) may also include a scheduling engine (30) coupled to the batch aggregation engine (20) that receives the suggested sizes, suggested starting times, and feedback (26) from the batch aggregation engine (20). The scheduling engine (30) schedules a starting time for one or more of the batches (50) according to the suggested sizes, suggested starting times, and feedback (26) received from the batch aggregation engine (20).

Abstract: A method and system for solving constrained optimization problems. An initial abstract solution represents a prioritized set of decisions. The abstract solution is used as the basis for building a concrete solution. The concrete solution is analyzed to determine one or more local moves that represent a re-prioritization of the abstract solution. After a local moves is made, the process begins again with a new abstract solution, that is closer to an optimal solution. This process continues interactively until an optimal solution is reached or approached. The prioritized set of decisions can be implemented as a priority vector or a priority graph.