Robustness analysis methodology for multi-objective combinatorial optimization problems and application to project selection |
| |
Institution: | 1. Fraunhofer Research Institution for Casting, Composite and Processing Technology IGCV Provinostr. 52, Building B 1, 86153 Augsburg, Germany;2. Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA |
| |
Abstract: | Multi-objective combinatorial optimization (MOCO) problems, apart from being notoriously difficult and complex to solve in reasonable computational time, they also exhibit high levels of instability in their results in case of uncertainty, which often deviate far from optimality. In this work we propose an integrated methodology to measure and analyze the robustness of MOCO problems, and more specifically multi-objective integer programming ones, given the imperfect knowledge of their parameters. We propose measures to assess the robustness of each specific Pareto optimal solution (POS), as well as the robustness of the entire Pareto set (PS) as a whole. The approach builds upon a synergy of Monte Carlo simulation and multi-objective optimization, using the augmented ε-constraint method to generate the exact PS for the MOCO problems under examination. The usability of the proposed framework is justified through the identification of the most robust areas of the Pareto front, and the characterization of every POS with a robustness index. This index indicates a degree of certainty that a specific POS sustains its efficiency. The proposed methodology communicates in an illustrative way the robustness information to managers/decision makers and provides them with an additional supplement/tool to guide and support their final decision. Numerical examples focusing on a multi-objective knapsack problem and an application to academic capital budgeting problem for project selection, are provided to verify the efficacy and added value of the methodology. |
| |
Keywords: | Robustness analysis Multi-criteria Combinatorial optimization Multi-objective programming Monte Carlo simulation Visualization |
本文献已被 ScienceDirect 等数据库收录! |
|