Systems and methods for markdown optimization when inventory pooling level is above pricing level
First Claim
1. A computer-implemented method for selecting a product markdown plan, comprising:
- receiving, by one or more data processors, candidate data corresponding to a plurality of candidate markdown sets that detail schedules for marking down prices of one or more products;
receiving, by the one or more data processors, constraints data, wherein the constraints data includes individual constraints that apply to a single candidate markdown set, and wherein the constraints data includes a linking constraint that applies to the plurality of candidate markdown sets;
generating, by the one or more data processors, candidate markdown schedules for each of the candidate markdown sets, wherein each candidate markdown schedule conforms to applicable individual constraints;
generating, by the one or more data processors, one or more class sets, wherein each class set includes a candidate markdown schedule, and wherein each class set conforms to the linking constraint;
evaluating, by the one or more data processors, the one or more class sets, wherein the evaluations are iterative, and wherein each evaluation iteration includes computing an expected revenue value for a class set;
computing, by the one or more data processors, a revenue upper bound using the candidate markdown schedules and the linking constraint;
terminating, by the one or more data processors, the evaluation when a threshold number of iterations is reached, wherein the terminating includes determining a class set with a highest expected revenue value within a threshold of the revenue upper bound; and
selecting, by the one or more data processors, a product markdown plan, wherein the product markdown plan includes the class set with the highest expected revenue value.
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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.
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Citations
18 Claims
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1. A computer-implemented method for selecting a product markdown plan, comprising:
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receiving, by one or more data processors, candidate data corresponding to a plurality of candidate markdown sets that detail schedules for marking down prices of one or more products; receiving, by the one or more data processors, constraints data, wherein the constraints data includes individual constraints that apply to a single candidate markdown set, and wherein the constraints data includes a linking constraint that applies to the plurality of candidate markdown sets; generating, by the one or more data processors, candidate markdown schedules for each of the candidate markdown sets, wherein each candidate markdown schedule conforms to applicable individual constraints; generating, by the one or more data processors, one or more class sets, wherein each class set includes a candidate markdown schedule, and wherein each class set conforms to the linking constraint; evaluating, by the one or more data processors, the one or more class sets, wherein the evaluations are iterative, and wherein each evaluation iteration includes computing an expected revenue value for a class set; computing, by the one or more data processors, a revenue upper bound using the candidate markdown schedules and the linking constraint; terminating, by the one or more data processors, the evaluation when a threshold number of iterations is reached, wherein the terminating includes determining a class set with a highest expected revenue value within a threshold of the revenue upper bound; and selecting, by the one or more data processors, a product markdown plan, wherein the product markdown plan includes the class set with the highest expected revenue value. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16)
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17. A system for selecting a product markdown plan, comprising:
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one or more processors; one or more computer-readable storage mediums containing instructions configured to cause the one or more processors to perform operations including; receiving candidate data corresponding to a plurality of candidate markdown sets that detail schedules for marking down prices of one or more products; receiving constraints data, wherein the constraints data includes individual constraints that apply to a single candidate markdown set, and wherein the constraints data includes a linking constraint that applies to the plurality of candidate markdown sets; generating candidate markdown schedules for each of the candidate markdown sets, wherein each candidate markdown schedule conforms to applicable individual constraints; generating one or more class sets wherein each class set includes a candidate markdown schedule, and wherein each class set conforms to the linking constraint; evaluating the one or more class sets, wherein the evaluations are iterative, and wherein each evaluation iteration includes computing an expected revenue value for a class set; computing a revenue upper bound using the candidate markdown schedules and the linking constraint; terminating the evaluation when a threshold number of iterations is reached, wherein the terminating includes determining a class set with a highest expected revenue value within a threshold of the revenue upper bound; and selecting a product markdown plan, wherein the product markdown plan includes the class set with the highest expected revenue value.
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18. A computer-program product for selecting a product markdown plan, tangibly embodied in a machine-readable storage medium, including instructions configured to cause a data processing apparatus to:
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receive candidate data corresponding to a plurality of candidate markdown sets that detail schedules for marking down prices of one or more products; receive constraints data, wherein the constraints data includes individual constraints that apply to a single candidate markdown set, and wherein the constraints data includes a linking constraint that applies to the plurality of candidate markdown sets; generate candidate markdown schedules for each of the candidate markdown sets, wherein each candidate markdown schedule conforms to applicable individual constraints; generate one or more class sets wherein each class set includes a candidate markdown schedule, and wherein each class set conforms to the linking constraint; evaluate the one or more class sets, wherein the evaluations are iterative, and wherein each evaluation iteration includes computing an expected revenue value for a class set; compute a revenue upper bound using the candidate markdown schedules and the linking constraint; terminate the evaluation when a threshold number of iterations is reached, wherein the terminating includes determining a class set with a highest expected revenue value within a threshold of the revenue upper bound; and select a product markdown plan, wherein the product markdown plan includes the class set with the highest expected revenue value.
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Specification