Integer Quadratic Programming for Resource Reallocation Scheduling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Determining resource reallocation schemes in complex environments, such as retail store chains, is challenging due to the large number of variables and the difficulty in implementing theoretical models efficiently on computer systems for timely processing.
Innovation Solution
A computer system comprising a database with historical data analysis and reallocation scheduling modules, which determine average and deviation values for stock rates and replenishment durations, and use integer quadratic programming to optimize resource reallocation schemes by constructing an objective function with decision variables that include current stock, rate of decrease, and transport costs, while adhering to linear constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If theoretical models are used to solve resource reallocation problems, then solution accuracy is improved, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent transforms the objective function from a complex nonlinear form to a quadratic form by changing the mathematical parameters and representation. This allows the use of efficient quadratic programming algorithms while maintaining solution accuracy for resource reallocation problems in retail store chains
Solution Approach 2:
The patent replaces complex iterative optimization methods with integer quadratic programming, which can be solved more efficiently using standard computational algorithms. This substitution reduces processing time while maintaining the ability to handle large-scale problems with tens of thousands of variables
2Reliability
If comprehensive data analysis is performed to capture all variables, then solution completeness is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary data processing to compute average values and deviation values for stock decrease rates and replenishment durations before the main optimization. This pre-computation captures essential variability information while reducing the computational burden during the actual optimization process
Solution Approach 2:
The patent transforms detailed historical data into summarized statistical parameters (averages and deviations) that capture the essential behavior patterns. This parameter transformation maintains solution completeness while significantly reducing the amount of data that needs to be processed during optimization
3Measurement precision
If nonlinear functions are used to model benefit increments, then modeling accuracy is improved, but computational efficiency decreases
Solution Approach 1:
The patent transforms nonlinear benefit functions into quadratic forms by changing the mathematical representation. This allows accurate modeling of diminishing returns and benefit increments while enabling efficient solution using quadratic programming algorithms
Solution Approach 2:
The patent substitutes complex nonlinear optimization with quadratic programming, which has well-established efficient algorithms. This substitution maintains the ability to model complex benefit structures while dramatically improving computational efficiency for large-scale problems
Data Source
AI summary
The present disclosure relates to a computer system comprising a database including, a reallocation scheduling module, the reallocation scheduling module configured to obtain an objective function including one or more integer-valued decision variables and automatically determine a proposed reallocation scheme for each of one or more items between a plurality of locations based on the objective function, wherein determining the reallocation scheme includes finding values of one or more decision variables that optimize the objective function while obeying the one or more constraints, wherein determining a proposed reallocation scheme includes solving an integer quadratic programming problem.