Pruned Pareto-optimal sets for the system redundancy allocation problem based on multiple prioritized objectives |
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Authors: | Sadan Kulturel-Konak David W Coit Fatema Baheranwala |
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Institution: | (1) Management Information Systems, Penn State Berks, Tulpehocken Road, P.O. Box 7009, Reading, PA 19610, USA;(2) Department of Industrial & Systems Engineering, Rutgers University, Piscataway, NJ 08844, USA |
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Abstract: | In this paper, a new methodology is presented to solve different versions of multi-objective system redundancy allocation
problems with prioritized objectives. Multi-objective problems are often solved by modifying them into equivalent single objective
problems using pre-defined weights or utility functions. Then, a multi-objective problem is solved similar to a single objective
problem returning a single solution. These methods can be problematic because assigning appropriate numerical values (i.e.,
weights) to an objective function can be challenging for many practitioners. On the other hand, methods such as genetic algorithms
and tabu search often yield numerous non-dominated Pareto optimal solutions, which makes the selection of one single best
solution very difficult. In this research, a tabu search meta-heuristic approach is used to initially find the entire Pareto-optimal
front, and then, Monte-Carlo simulation provides a decision maker with a pruned and prioritized set of Pareto-optimal solutions
based on user-defined objective function preferences. The purpose of this study is to create a bridge between Pareto optimality
and single solution approaches. |
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Keywords: | Multi-objective combinatorial optimization Pruned Pareto-optimal front Decision making Uncertainty Tabu search Redundancy allocation problem |
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