Abstract: Some examples describes herein relate to handling bulk requests for resources. In one example, a system can determine a bulk request parameter-value associated with a bulk request. The system can then predict a baseline benefit value, which can be a benefit value when the bulk request parameter-value is used as a lower boundary for a unit parameter-value. The system can also determine a lower boundary constraint on the unit parameter-value independently of the bulk request parameter-value. The system can then execute an iterative process using the baseline benefit value and the lower boundary constraint. Based on a result of the iterative process, the system can determine whether and how much the bulk request parameter-value should be adjusted. The system may adjust the bulk request parameter-value accordingly or output a recommendation to do so.

Abstract: Exemplary embodiments relate to the problem of allocating a finite number of units of a resource among requestors willing to offer different amounts of value for the resource. When different classes of requestors are permitted to cancel the request or fail to show up to collect the unit of the resource with different probabilities (collectively referred to as “wash”), the problem becomes difficult to solve efficiently. According to the procedures described herein, the capacity is artificially inflated to offset the impact of wash, and then protection levels are computed using the inflated capacity as if there was no wash. The capacity is then artificially inflated again based on the new protection levels, and the process is repeated until, e.g., the results converge. Using this procedure, overallocation limits and protection levels can be computed in real-time, and accordingly the resource can be allocated efficiently as new requests are received.

Abstract: Exemplary embodiments relate to the problem of allocating a finite number of units of a resource among requestors willing to offer different amounts of value for the resource. When different classes of requestors are permitted to cancel the request or fail to show up to collect the unit of the resource with different probabilities (collectively referred to as “wash”), the problem becomes difficult to solve efficiently. According to the procedures described herein, the capacity is artificially inflated to offset the impact of wash, and then protection levels are computed using the inflated capacity as if there was no wash. The capacity is then artificially inflated again based on the new protection levels, and the process is repeated until, e.g., the results converge. Using this procedure, overallocation limits and protection levels can be computed in real-time, and accordingly the resource can be allocated efficiently as new requests are received.

Abstract: In one example, a system can receive configuration data indicating how resources can be combined, identify availability data indicating the total number of various resources that are available, and determine maximum-capacity data using the availability data and the configuration data. The system can also receive distribution data having probability distributions for jobs to be implemented using the resources, determine capacity valuations using the distribution data and the availability data, and determine a configuration of resources using the capacity valuations and the maximum-capacity data. Thereafter, the system can receive a job and determine that a valuation for the job exceeds a predefined threshold associated with using the configuration of resources. In response to determining that the valuation exceeds the predefined threshold, the system can assign the resources to the job in the configuration.

Abstract: Exemplary embodiments are generally directed to methods, mediums, and systems for accounting for extensions or reductions of the period for which a resource (e.g., computer processor time, scientific apparatus, storage units, devices, etc.) is allocated. According to exemplary embodiments, allocation-based aggregated effects of extension and relinquishment are modeled. The modeled effects are used to offset allocation forecasts based on historical data. As a result, the dimensionality of the problem of incorporating in-house data is greatly reduced as compared to other techniques, and allocation forecasts can be made more accurately and efficiently.

Abstract: Exemplary embodiments are generally directed to methods, mediums, and systems for accounting for extensions or reductions of the period for which a resource (e.g., computer processor time, scientific apparatus, storage units, devices, etc.) is allocated. According to exemplary embodiments, allocation-based aggregated effects of extension and relinquishment are modeled. The modeled effects are used to offset allocation forecasts based on historical data. As a result, the dimensionality of the problem of incorporating in-house data is greatly reduced as compared to other techniques, and allocation forecasts can be made more accurately and efficiently.

Abstract: Computer-implemented systems and methods generate a near-optimum product markdown plan for a plurality of uniform pricing levels having a required inventory sell-through target over all of the plurality of uniform pricing levels. A plurality of feasible markdown schedules are generated for the uniform pricing level, where each of the plurality of feasible markdown schedules meets all individual constraints for the uniform pricing level. All dominated feasible markdown schedules are removed for the uniform pricing level to generate one or more candidate markdown schedules for the uniform pricing level. A near-optimum product markdown plan is generated, where generating the near-optimum product markdown plans includes executing a limited exact algorithm solver for a plurality of iterations, and executing a dynamic programming solver if no product markdown plan generated by the limited exact algorithm solver is within the threshold percentage of the revenue upper bound.

Abstract: Computer-implemented systems and methods for identifying markdown prices for items. As an example, a system and method can include identifying for each item an optimal markdown plan containing a markdown price for the item. Also, the method and system can be configured to identify for each item an optimal delay plan. For each item, a delay cost and a markdown spend are calculated, and a comparison is performed of the item's delay cost with respect to the item's markdown spend. The comparison is used to determine whether to mark down an item based upon the item's determined markdown price.

Abstract: Computer-implemented systems and methods generate a near-optimum product markdown plan for a plurality of uniform pricing levels having a required inventory sell-through target over all of the plurality of uniform pricing levels. A plurality of feasible markdown schedules are generated for the uniform pricing level, where each of the plurality of feasible markdown schedules meets all individual constraints for the uniform pricing level. All dominated feasible markdown schedules are removed for the uniform pricing level to generate one or more candidate markdown schedules for the uniform pricing level. A near-optimum product markdown plan is generated, where generating the near-optimum product markdown plans includes executing a limited exact algorithm solver for a plurality of iterations, and executing a dynamic programming solver if no product markdown plan generated by the limited exact algorithm solver is within the threshold percentage of the revenue upper bound.

Abstract: The present invention discloses a control technology for brushless DC motor, in which firstly it is to build or import a motor running parameters' database, then detect the signals always running and the signals closely relevant to the rotor's rotating state such as the voltage and the current, and process these signals and figure out the signals about the rotor's position. Wherein these detected signals should be filtered before being used, and the best filter is the low-pass filter to cut out the high frequency components, and the cut-off frequency of the low-pass filter are determined by the motor running voltage U and PWM signal width when the motor is running at no load, finally determine if the motor is working properly by means of the motor running parameters stored in the database in advance, that is to determine if the detected value is identical with the one predicted from the database.

Abstract: The present invention discloses a control technology for brushless DC motor, in which firstly it is to build or import a motor running parameters' database, then detect the signals always running and the signals closely relevant to the rotor's rotating state such as the voltage and the current, and process these signals and figure out the signals about the rotor's position. Wherein these detected signals should be filtered before being used, and the best filter is the low-pass filter to cut out the high frequency components, and the cut-off frequency of the low-pass filter are determined by the motor running voltage U and PWM signal width when the motor is running at no load, finally determine if the motor is working properly by means of the motor running parameters stored in the database in advance, that is to determine if the detected value is identical with the one predicted from the database.

Abstract: A container inspection equipment with cobalt-60 ?-ray source and cesium iodide or cadmium tungstate detector includes a cobalt-60 ?-ray source, a cask, a front collimator, a rear collimator, a cesium iodide or cadmium tungstate detector, signal and image processing systems, container trailer system, and automatic control system. The cask of the cobalt-60 ?-ray source, the beam shutter, and the front collimator are fixed on the same chassis to form an integration and placed in the source room. The rear collimator, cesium iodide or cadmium tungstate array detector and the radiation catcher are fixed on the same chassis to form an integration and placed in the detector room. A container inspection tunnel is formed between the source room and the detector room. The equipment is mainly used for inspecting smuggling goods and contrabands etc., in large containers, container trucks, train carriage and air containers.

Abstract: A container inspection equipment with cobalt-60 &ggr;-ray source and cesium iodide or cadmium tungstate detector includes a cobalt-60 &ggr;-ray source, a cask, a front collimator, a rear collimator, a cesium iodide or cadmium tungstate detector, signal and image processing systems, container trailer system, and automatic control system. The cask of the cobalt-60 &ggr;-ray source, the beam shutter, and the front collimator are fixed on the same chassis to form an integration and placed in the source room. The rear collimator, cesium iodide or cadmium tungstate array detector and the radiation catcher are fixed on the same chassis to form an integration and placed in the detector room. A container inspection tunnel is formed between the source room and the detector room. The equipment is mainly used for inspecting smuggling goods and contrabands etc., in large containers, container trucks, train carriage and air containers.