Automated Multi-Objective Solution Selection Using Defensive Criteria
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Solution Overview
Problem
In multi-objective decision-making, users face challenges in selecting a subset of Pareto optimal solutions due to the complexity of manual comparison and the influence of physiological biases, which existing systems struggle to address effectively.
Innovation Solution
A computerized method computes outranking weights between solutions and selects a subset based on defensive or offensive criteria, utilizing k-domination relations, credibility indices, and fuzzy membership functions to assist users in selecting a preferred solution without requiring explicit preference definitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual selection of Pareto optimal solutions is performed, then user preference can be considered, but the process is complex and susceptible to physiological biases
Solution Approach 1:
The patent introduces an automated selection system that acts as an intermediary between the multi-objective optimization problem and the decision maker. This system computes outranking weights and applies defensive criteria to automatically select a subset of Pareto optimal solutions, eliminating the need for manual comparison while maintaining preference accuracy through mathematical rigor rather than human judgment.
Solution Approach 2:
The system enables self-service by allowing the automated selection process to operate independently without requiring explicit user preferences. The defensive criteria and outranking weight computations automatically identify and select the most robust solutions, making the system self-sufficient in the selection task while reducing cognitive load on the user.
2Measurement precision
If explicit user preferences are required for automated selection, then selection accuracy can be improved, but the system becomes less practical due to user difficulty in formulating preferences
Solution Approach 1:
The system performs self-service by automatically determining selection criteria without requiring user input about preferences. The defensive criteria and outranking weight computations are inherently built into the system, allowing it to autonomously select solutions based on robust mathematical principles rather than requiring users to articulate their preferences.
Solution Approach 2:
The patent transforms the selection problem from one requiring subjective preference parameters into one using objective computational parameters. By changing from user-defined preference weights to systematically computed outranking weights based on defensive criteria, the system maintains selection accuracy while eliminating the complexity of preference formulation.
3Loss of information
If all Pareto optimal solutions are presented to the user, then complete information is provided, but the quantity of solutions becomes overwhelming for effective decision-making
Solution Approach 1:
The patent extracts a representative subset of solutions from the complete Pareto front by applying defensive criteria and outranking weight computations. This extraction process identifies and removes redundant or dominated solutions, presenting only the most robust and distinctive options to the user, thereby maintaining information completeness while reducing the quantity to manageable levels.
Solution Approach 2:
Rather than presenting all possible solutions (excessive action), the system applies partial action by selectively filtering and presenting only a subset of solutions that meet the defensive criteria. This partial presentation is sufficient for effective decision-making without the overwhelming quantity of the complete Pareto front.
Data Source
AI summary
A method, apparatus and product for automated multi-objective solution selection. The method comprises obtaining a set of solutions to a multi-objective problem, wherein each solution is a non-dominated solution. The method further comprises computing outranking weights for each pair of solutions and selecting a subset of the set of solutions based upon a defensive criteria in with respect to the outranking weights, wherein the defensive criteria relates to a number of solutions that outrank the subset. The method further comprises outputting the subset of the set of solutions.