A mean-risk mixed integer nonlinear program for transportation network protection
•Develop a mean-risk model for transportation network protection.•Reformulate the recourse function to overcome non-convexity and non-separability.•Apply Generalized Benders Decomposition method to the convex reformulation.•Demonstrate the model and solution method on a large-scale benchmark network...
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| Veröffentlicht in: | European journal of operational research Jg. 265; H. 1; S. 277 - 289 |
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| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier B.V
16.02.2018
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| Schlagworte: | |
| ISSN: | 0377-2217, 1872-6860 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | •Develop a mean-risk model for transportation network protection.•Reformulate the recourse function to overcome non-convexity and non-separability.•Apply Generalized Benders Decomposition method to the convex reformulation.•Demonstrate the model and solution method on a large-scale benchmark network.
This paper focuses on transportation network protection to hedge against extreme events such as earthquakes. Traditional two-stage stochastic programming has been widely adopted to obtain solutions under a risk-neutral preference through the use of expectations in the recourse function. In reality, decision makers hold different risk preferences. We develop a mean-risk two-stage stochastic programming model that allows for greater flexibility in handling risk preferences when allocating limited resources. In particular, the first stage minimizes the retrofitting cost by making strategic retrofit decisions whereas the second stage minimizes the travel cost. The conditional value-at-risk (CVaR) is included as the risk measure for the total system cost. The two-stage model is equivalent to a nonconvex mixed integer nonlinear program (MINLP). To solve this model using the Generalized Benders Decomposition (GBD) method, we derive a convex reformulation of the second-stage problem to overcome algorithmic challenges embedded in the non-convexity, nonlinearity, and non-separability of first- and second-stage variables. The model is used for developing retrofit strategies for networked highway bridges, which is one of the research areas that can significantly benefit from mean-risk models. We first justify the model using a hypothetical nine-node network. Then we evaluate our decomposition algorithm by applying the model to the Sioux Falls network, which is a large-scale benchmark network in the transportation research community. The effects of the chosen risk measure and critical parameters on optimal solutions are empirically explored. |
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| ISSN: | 0377-2217 1872-6860 |
| DOI: | 10.1016/j.ejor.2017.07.025 |