Method and system for optimizing an objective having discrete constraints

A computing apparatus for optimizing a retail promotion having discrete constraints using a dataset, the dataset comprising a plurality of aspects associated with the retail promotion including historical sales data, the apparatus comprising:

• a processor;
• and
• a memory storing instructions that, when executed by the processor, configure the apparatus to:
• receive the dataset, the retail promotion, and constraints, the constraints comprising a set of discrete constraints, wherein the set of discrete constraints comprising one or more of which products to put on promotion, promotion mechanics, product attributes and store attributes;
• receive a seed solution to an optimization model based on an objective, the seed solution based on the discrete constraints in view of the retail promotion, wherein the seed solution comprises a previous year's promotion schedule or a pre-developed future promotion schedule;
• iteratively perform an optimization iteration until a criteria is reached, the optimization iteration comprising:
• generate a constraint space that comprises a range or set of options for at least one of the discrete constraints and that defines a closeness to values provided in the seed solution, wherein the range or set of options are determined by a constraint satisfaction problem, and
• determine an optimized value of at least one variable of the optimized model based on the objective that is within the constraint space, the optimization model taking as input the dataset and the constraint space;
• output an optimized retail promotion based on the optimized value once the criteria is reached;
• and
• determine how many units for each of the products put on promotion should be purchased from vendors based on the optimized retail promotion.

A non-transitory computer-readable storage medium for optimizing a retail promotion having discrete constraints using a dataset, the dataset comprising a plurality of aspects associated with the retail promotion including historical sales data, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to:

• receive the dataset, the retail promotion, and constraints, the constraints comprising a set of discrete constraints, wherein the set of discrete constraints comprising one or more of which products to put on promotion, promotion mechanics, product attributes and store attributes;
• receive a seed solution to an optimization model based on an objective, the seed solution based on the discrete constraints in view of the retail promotion, wherein the seed solution comprises a previous year's promotion schedule or a pre-developed future promotion schedule;
• iteratively perform an optimization iteration until a criteria is reached, the optimization iteration comprising:
• generate a constraint space that comprises a range or set of options for at least one of the discrete constraints and that defines a closeness to values provided in the seed solution, wherein the range or set of options are determined by a constraint satisfaction problem, and
• determine an optimized value of at least one variable of the optimized model based on the objective that is within the constraint space, the optimization model taking as input the dataset and the constraint space;
• output an optimized retail promotion based on the optimized value once the criteria is reached;
• and,
• determining how many units for each of the products put on promotion should be purchased from vendors based on the optimized retail promotion.