Raw Material And Semi-Finished Selection With ALMM Optimization
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Solution Overview
Problem
Existing methods struggle to efficiently optimize the selection of raw materials and semi-finished products, particularly in NP-hard discrete optimization problems, and fail to dynamically adapt to changes in production processes, leading to inefficiencies and waste minimization challenges.
Innovation Solution
The ALMM-Optim algorithm, which uses an algebraic-logical meta-model to create formal records of constraints and update a knowledge base, supports the creation of new models, and implements decision optimization models to solve NP-hard problems by dynamically controlling decision sequences in production processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing optimization methods are used for selecting raw materials and semi-finished products, then the selection process can be completed, but the optimization efficiency is insufficient and waste cannot be effectively minimized
Solution Approach 1:
The patent transforms the raw material selection problem by changing parameters to a standardized algebraic-logical format, defining discrete optimization problems with specific parameters (U, S, s0, f, SN, SG). This parameter transformation enables systematic optimization and reduces waste through algorithmic solution of discrete optimization problems.
Solution Approach 2:
The patent replaces traditional mechanical trial-and-error selection methods with computational algorithms (ALMM-Optim). The system substitutes physical experimentation and manual optimization with mathematical programming and constraint logic programming, achieving更高效 optimization of raw material selection.
2Adaptability or versatility
If traditional solvers are used, then basic optimization can be performed, but they fail to dynamically adapt to changes in production processes
Solution Approach 1:
The patent implements dynamic adaptability by designing the ALMM-Optim system to continuously update its knowledge base with new problem models, algorithms, and properties. The system dynamically adjusts to changing production conditions through ongoing learning and model updates, maintaining high productivity while adapting to process changes.
Solution Approach 2:
The system incorporates feedback mechanisms where optimization results and process changes are fed back into the knowledge base. This feedback loop enables the system to learn from past optimizations and adapt to new production conditions, improving both adaptability and sustained productivity.
3Device complexity
If discrete optimization problems are solved without a unified formal framework, then individual problems can be addressed, but NP-hard problems cannot be efficiently solved
Solution Approach 1:
The patent creates a universal algebraic-logical meta-model framework that can handle multiple types of discrete optimization problems through a unified structure. The DOP sextuple (U, S, s0, f, SN, SG) provides a multi-functional template that adapts to various NP-hard problems while maintaining solution accuracy through standardized solution methods.
Data Source
AI summary
A method of using algorithms for optimizing tasks of selecting raw materials and semi-finished products is to optimize discrete problems. The method is distinguished from other solvers, among other things, by the fact that ALMM-Optim updates on an ongoing basis and increases the knowledge base, which is used to: store models of problems and their components, represented in ALMM technology; support the creation of models of new problems, by using the components saved (stored) in the database; store specifications of discrete optimization methods and algorithms, represented in ALMM technology; store definitions of general properties of discrete optimization problems and information about the properties that are satisfied by individual problems. The ALMM-type decision optimization technology used is an abstract structure of logical relationships, and physical implementation requires the development and implementation of dedicated optimization models and the development of real applications that can solve NP-Hard problems based on them.