Zobrazeno 1 - 10
of 78
pro vyhledávání: '"Marc Goerigk"'
Publikováno v:
EURO Journal on Computational Optimization, Vol 4, Iss 3, Pp 241-270 (2016)
We consider a network flow problem, where the outgoing flow is reduced by a certain percentage in each node. Given a maximum amount of flow that can leave the source node, the aim is to find a solution that maximizes the amount of flow which arrives
Externí odkaz:
https://doaj.org/article/5b3cb303da874f5f8f615a22b27ac479
Autor:
Marc Goerigk, HorstW. Hamacher
Publikováno v:
EURO Journal on Computational Optimization, Vol 4, Iss 3, Pp 215-217 (2016)
Externí odkaz:
https://doaj.org/article/822a77815df94f6a8a713db53fc25c35
Autor:
Marc Goerigk, Mohammad Khosravi
Publikováno v:
European Journal of Operational Research. 310:529-551
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d315b1dff9f2da2dd4303086308b3d19
https://doi.org/10.1016/j.ejor.2023.03.019
https://doi.org/10.1016/j.ejor.2023.03.019
Autor:
Marc Goerigk, Michael Hartisch
Publikováno v:
Discrete Applied Mathematics. 328:40-59
For decision making under uncertainty, min-max regret has been established as a popular methodology to find robust solutions. In this approach, we compare the performance of our solution against the best possible performance had we known the true sce
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ce52ebf36fea6c46d5712ee47d0a0f3a
https://doi.org/10.1016/j.dam.2022.11.013
https://doi.org/10.1016/j.dam.2022.11.013
Autor:
Trivikram Dokka, Marc Goerigk
Publikováno v:
Computers & Operations Research. 154:106213
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e683f1a8aed70f7129d00e70214f5f2a
https://doi.org/10.1016/j.cor.2023.106213
https://doi.org/10.1016/j.cor.2023.106213
Publikováno v:
Journal of Combinatorial Optimization. 43:497-527
In this paper a class of robust two-stage combinatorial optimization problems is discussed. It is assumed that the uncertain second-stage costs are specified in the form of a convex uncertainty set, in particular polyhedral or ellipsoidal ones. It is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8a154de4d6f0ef300688975260eddbac
https://doi.org/10.1007/s10878-021-00776-4
https://doi.org/10.1007/s10878-021-00776-4
Autor:
André B. Chassein, Marc Goerigk
Publikováno v:
Discrete Applied Mathematics. 296:141-163
We study robust solutions for combinatorial optimization problems with budgeted uncertainty sets in the min–max–min setting, where the decision maker is allowed to choose a set of k different solutions from which one can be chosen after the uncer
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9d31111fbf7300283ef6c0e373dd7cbe
https://doi.org/10.1016/j.dam.2020.05.017
https://doi.org/10.1016/j.dam.2020.05.017
Autor:
Marc Goerigk, Michael Hartisch
With dramatic improvements in optimization software, the solution of large-scale problems that seemed intractable decades ago are now a routine task. This puts even more real-world applications into the reach of optimizers. At the same time, solving
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4f37c15528f4ae481c2fe1b7a0f9e308
http://arxiv.org/abs/2208.12570
http://arxiv.org/abs/2208.12570
Autor:
Marc Goerigk, Jannis Kurtz
Publikováno v:
Computers & Operations Research. 151:106087
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0256a40520c48e0459889e60b4ccc20e
https://doi.org/10.1016/j.cor.2022.106087
https://doi.org/10.1016/j.cor.2022.106087
Autor:
Matthew Bold, Marc Goerigk
We investigate the recoverable robust single machine scheduling problem under interval uncertainty. In this setting, jobs have first-stage processing times p and second-stage processing times q and we aim to find a first-stage and second-stage schedu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2bda9d9df2b6207ef0b05c3250e7de94
https://doi.org/10.1016/j.dam.2022.02.005
https://doi.org/10.1016/j.dam.2022.02.005