Some matheuristic algorithms for multistage stochastic optimization models with endogenous uncertainty and risk management
| Autor: | Laureano F. Escudero, Aitziber Unzueta, M. Araceli Garín, Juan F. Monge |
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| Rok vydání: | 2020 |
| Předmět: |
050210 logistics & transportation
021103 operations research Information Systems and Management General Computer Science Computer science Risk aversion 05 social sciences 0211 other engineering and technologies Stochastic dominance 02 engineering and technology Management Science and Operations Research Solver Regularization (mathematics) Industrial and Manufacturing Engineering Stochastic programming Risk neutral Modeling and Simulation Norm (mathematics) 0502 economics and business Piecewise Resource allocation Stochastic optimization Hedge (finance) Algorithm |
| Zdroj: | European Journal of Operational Research. 285:988-1001 |
| ISSN: | 0377-2217 |
| DOI: | 10.1016/j.ejor.2020.02.046 |
| Popis: | Two matheuristic decomposition algorithms are introduced. The first one is a Progressive Hedging type so-named Regularized scenario Cluster Progressive Algorithm. The second one is a Frank-Wolfe PH type so-named Regularized scenario Cluster Simplicial Decomposition Progressive Algorithm. An extension of endogenous Type III uncertainty is considered for representing the decision-dependent scenario probability and outlook. Its performance is tested in the time-consistent Expected Conditional Stochastic Dominance risk averse environment. As a result of the modeling, the typical risk neutral multistage mixed 0–1 linear stochastic problem under uncertainty is replaced with an enlarged model that is equivalent to the required mixed 0–1 bilinear model. Based on the special features of the problem, it is unrealistic to seek the optimal solution for large-scale instances. Feasible solutions and lower bounds on the solution value of the original model are provided. In total, 48 strategies are considered, each one consists of a combination of a regularization norm, a calibration type for the PH pseudo-gradient computation, and a set of value intervals of the influential variables on a representative endogenous uncertainty-based piecewise function in the scenarios. Computational results are reported for a large-scale extension of a well-known real-life pilot case for preparedness resource allocation planning aiming to natural disaster relief. The matheuristics outperform the plain use of a state-of-the-art solver. |
| Databáze: | OpenAIRE |
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