Multi-Step Optimization for Food Formulation Constraints
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Solution Overview
Problem
Single step, single product and single step, multiple product optimization methods are inadequate for complex manufacturing processes, particularly in food formulations, as they fail to effectively manage varying nutritional, sensory, physical, cost, and availability characteristics of commodity ingredients.
Innovation Solution
A multiple product, multiple step optimization method is introduced, where groups of subgroups with variables and constraints are defined, optimized to achieve specific objectives such as minimizing manufacturing costs, considering common and individual constraints across subgroups and groups, using computerized systems to solve large-scale, nonlinear blending problems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single step optimization methods are used for single or multiple product formulations, then the optimization process is simple and fast, but the method becomes inadequate for complex manufacturing processes with multiple steps and varying ingredient characteristics
Solution Approach 1:
The patent divides the complex manufacturing process into multiple discrete steps, with each step representing a specific processing stage. This segmentation allows the optimization method to handle complex multi-step manufacturing processes by treating each step as a manageable unit with its own variables and constraints, while still maintaining overall process optimization.
2Productivity
If commodity ingredients with varying nutritional, sensory, physical, cost, and availability characteristics are used, then economic value is increased, but the complexity of managing varying characteristics across multiple products and steps increases
Solution Approach 1:
The patent incorporates multiple varying parameters for each ingredient including nutritional content, sensory properties, physical characteristics, cost, and availability. By explicitly modeling these parameter variations across different ingredients and processing steps, the optimization method can manage the complexity of commodity ingredient variations while maximizing economic value through flexible formulation adjustments.
Solution Approach 2:
The optimization method is designed to handle multiple objective functions simultaneously, including cost minimization, nutritional requirement satisfaction, and sensory property optimization. This multi-functionality allows the same optimization framework to manage various ingredient characteristics across different products and processing steps, reducing overall system complexity.
3Manufacturing precision
If multiple product, multiple step manufacturing processes are optimized, then the comprehensiveness of optimization is improved, but the computational complexity and difficulty of solving the optimization problem increases
Solution Approach 1:
By segmenting the manufacturing process into discrete steps and identifying specific variables and constraints for each step, the patent reduces the computational complexity of optimizing entire multi-product, multi-step processes. Each segment can be optimized with appropriate detail while maintaining tractability.
Solution Approach 2:
The optimization method dynamically adjusts the level of detail and variables considered for each processing step based on its importance and impact on final product quality. This dynamic approach allows comprehensive optimization of critical steps while simplifying less critical steps, balancing comprehensiveness with computational feasibility.
Data Source
AI summary
Multiple product, multiple step optimization methods useful for manufacturing products. The optimization methods comprise defining a first group and a second group. The first and second groups each comprise at least two subgroups. One or more of the subgroups for each group comprises one or more variables and one or more constraints. The variables are optimized to obtain final values for the variables based on a defined objective subject to the constraints.