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Reliable pareto solutions for multiple objective scheduling problems.
| Content Provider | CiteSeerX |
|---|---|
| Author | Malladi, Praveen Kumar Puvvula, Deepika Nagalla, Anisha |
| Abstract | Abstract — Many (may be most) real-world engineering optimization problems are implicitly or explicitly multiobjective, and approaches to find the best feasible solution to be implemented can be quite challenging for the decision-maker. A method is proposed to incorporate uncertainty in the problem formulation while keeping the formulation simple. This enables users with limited knowledge about multi-objective optimization to use this method to solve problems. It is not uncommon to find real-life engineering examples where the decision maker has multiple objectives but must select one feasible solution that can be implemented as the system design. This poses somewhat of a problem because, when dealing with multiple objectives, either you determine a single solution or identify a Pareto optimal set. However, the Pareto-optimal set is often large and cumbersome, making the post-Pareto analysis phase potentially difficult, especially as the number of objectives increase. Our research involves the post-Pareto analysis phase, and two methods are presented to filter the Pareto-optimal set to determine a subset of promising or desirable solutions. The first method is pruning using non-numerical objective function ranking preferences. The second approach involves pruning by using data clustering. The k-means algorithm is used to find clusters of similar solutions in the Pareto-optimal set. The clustered data allows the decision maker to have just k general solutions to choose from. These methods are explained with examples. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Reliable Pareto Solution Pareto-optimal Set Multiple Objective Scheduling Problem Feasible Solution Multiple Objective Post-pareto Analysis Phase Decision Maker Objective Increase Problem Formulation Second Approach Pareto Optimal Set Data Clustering System Design Multi-objective Optimization General Solution Non-numerical Objective Function Formulation Simple Clustered Data Single Solution Abstract Many First Method Limited Knowledge Desirable Solution K-means Algorithm Real-life Engineering Example Real-world Engineering Optimization Problem Similar Solution |
| Content Type | Text |
