Prepack Configuration Planning for Retail Demand Allocation

The patent segments the complex nonlinear optimization problem into smaller, more manageable components by introducing a genetic heuristic algorithm that breaks down the prepack configuration into discrete genetic operations (selection, crossover, mutation). This segmentation allows the system to handle the complexity of multiple factors and constraints through modular genetic operators rather than attempting to solve the entire nonlinear optimization problem at once, thereby reducing solution time while maintaining accuracy.

The patent applies dynamics by implementing a genetic algorithm that evolves prepack configurations through iterative genetic operations. Instead of using static traditional optimization methods, the system dynamically adapts the prepack configuration through selection, crossover, and mutation operations across generations. This dynamic approach allows the system to converge to optimal solutions with fewer iterations by continuously improving the configuration based on fitness evaluation, thereby reducing the excessive iteration count required by traditional nonlinear optimization methods.

The patent substitutes traditional mechanical optimization methods with a biological-inspired genetic heuristic algorithm. Instead of relying on conventional nonlinear optimization techniques that require extensive computational resources, the system employs genetic operations (selection, crossover, mutation) that mimic natural evolution. This substitution replaces the computationally intensive mechanical optimization process with a more efficient biological metaphor approach, significantly improving computational efficiency while handling the nonlinear optimization complexity of prepack configuration.