Bounds on risk-averse mixed-integer multi-stage stochastic programming problems with mean-CVaR
buir.contributor.author | Mahmutoğulları, Ali İrfan | |
buir.contributor.author | Çavuş, Özlem | |
buir.contributor.author | Aktürk, M. Selim | |
dc.citation.epage | 608 | en_US |
dc.citation.issueNumber | 2 | en_US |
dc.citation.spage | 595 | en_US |
dc.citation.volumeNumber | 266 | en_US |
dc.contributor.author | Mahmutoğulları, Ali İrfan | en_US |
dc.contributor.author | Çavuş, Özlem | en_US |
dc.contributor.author | Aktürk, M. Selim | en_US |
dc.date.accessioned | 2019-02-21T16:01:32Z | |
dc.date.available | 2019-02-21T16:01:32Z | |
dc.date.issued | 2018 | en_US |
dc.department | Department of Industrial Engineering | en_US |
dc.description.abstract | Risk-averse mixed-integer multi-stage stochastic programming forms a class of extremely challenging problems since the problem size grows exponentially with the number of stages, the problem is non-convex due to integrality restrictions, and the objective function is nonlinear in general. We propose a scenario tree decomposition approach, namely group subproblem approach, to obtain bounds for such problems with an objective of dynamic mean conditional value-at-risk (mean-CVaR). Our approach does not require any special problem structure such as convexity and linearity, therefore it can be applied to a wide range of problems. We obtain lower bounds by using different convolution of mean-CVaR risk measures and different scenario partition strategies. The upper bounds are obtained through the use of optimal solutions of group subproblems. Using these lower and upper bounds, we propose a solution algorithm for risk-averse mixed-integer multi-stage stochastic problems with mean-CVaR risk measures. We test the performance of the proposed algorithm on a multi-stage stochastic lot sizing problem and compare different choices of lower bounds and partition strategies. Comparison of the proposed algorithm to a commercial solver revealed that, on the average, the proposed algorithm yields 1.13% stronger bounds. The commercial solver requires additional running time more than a factor of five, on the average, to reach the same optimality gap obtained by the proposed algorithm. | |
dc.description.provenance | Made available in DSpace on 2019-02-21T16:01:32Z (GMT). No. of bitstreams: 1 Bilkent-research-paper.pdf: 222869 bytes, checksum: 842af2b9bd649e7f548593affdbafbb3 (MD5) Previous issue date: 2018 | en |
dc.embargo.release | 2020-04-16 | en_US |
dc.identifier.doi | 10.1016/j.ejor.2017.10.038 | |
dc.identifier.issn | 0377-2217 | |
dc.identifier.uri | http://hdl.handle.net/11693/49868 | |
dc.language.iso | English | |
dc.publisher | Elsevier B.V. | |
dc.relation.isversionof | https://doi.org/10.1016/j.ejor.2017.10.038 | |
dc.source.title | European Journal of Operational Research | en_US |
dc.subject | Bounding | en_US |
dc.subject | CVaR | en_US |
dc.subject | Dynamic measures of risk | en_US |
dc.subject | Mixed-integer multi-stage stochastic programming | en_US |
dc.subject | Stochastic programming | en_US |
dc.title | Bounds on risk-averse mixed-integer multi-stage stochastic programming problems with mean-CVaR | en_US |
dc.type | Article | en_US |