Prepack Configuration Planning for Demand-Coverage Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Determining optimal prepack solutions for distributing items in a supply chain network is challenging due to the complexity of factors and constraints, including demand forecasting, inventory management, and package configuration, which traditional methods struggle to efficiently address.
Innovation Solution
A genetic heuristic and mixed integer programming (MIP) model is employed to minimize penalties associated with over-cover and under-cover of demand, with overstock being penalized more heavily than understock, and the method iteratively refines prepack configurations using a genetic algorithm to reduce complexity and solve the problem efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional optimization methods are used to determine prepack solutions, then the problem can be solved with conventional approaches, but the complexity of multiple factors and constraints makes it inefficient and time-consuming
Solution Approach 1:
The patent segments the complex prepack optimization problem into distinct components: demand forecasting, inventory management, and package configuration. By breaking down the nonlinear optimization problem into separate manageable segments, the system can process each aspect independently while maintaining overall optimization, thereby reducing computational complexity and improving solution efficiency.
Solution Approach 2:
The patent introduces an intermediary optimization layer that mediates between demand forecasts and package configurations. This intermediary component transforms the complex nonlinear optimization problem into a series of simpler sub-problems that can be solved more efficiently, acting as a bridge that simplifies the overall computational burden while maintaining solution accuracy.
2Measurement precision
If detailed demand forecasting and inventory management are incorporated into prepack planning, then distribution accuracy improves, but the computational complexity and time required to solve the problem increases
Solution Approach 1:
The patent applies preliminary action by performing demand forecasting and inventory assessment before the actual prepack configuration optimization. By pre-processing and preparing demand data and inventory status in advance, the system reduces the computational burden during the optimization phase, thereby maintaining high accuracy in demand forecasting while reducing the time required to solve the overall prepack problem.
3Reliability
If multiple constraints and factors are considered in prepack optimization, then the quality and accuracy of distribution plans improve, but the complexity of the optimization problem increases making it harder to solve efficiently
Solution Approach 1:
The patent applies local quality by optimizing package configurations for specific distribution scenarios and constraints rather than attempting a single global optimization solution. By tailoring the optimization approach to local conditions and constraints, the system maintains high quality distribution plans while managing computational complexity through localized optimization strategies.
Data Source
AI summary
A system and method are disclosed including a prepack planner that determines an initial prepack configuration of a package including two or more items, which include one or more attributes and two or more attribute values. The prepack planner also evaluates the initial prepack configuration by solving a mixed integer problem model and selects the initial prepack configuration to be the parent prepack configuration. The prepack planner further generates one or more child prepack configurations by attributing the number of the two or more attribute values to the one or more child prepack configurations and mutates the one or more child prepack configurations by changing the two or more attribute values with two of the two or more attributes and compares the initial prepack configuration with a stop criteria to determine whether a stop criteria has been reached.