M
Michael Poss
Researcher at University of Montpellier
Publications - 90
Citations - 1324
Michael Poss is an academic researcher from University of Montpellier. The author has contributed to research in topics: Robust optimization & Shortest path problem. The author has an hindex of 17, co-authored 90 publications receiving 1079 citations. Previous affiliations of Michael Poss include University of Kiel & University of Coimbra.
Papers
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The robust vehicle routing problem with time windows
Agostinho Agra,Marielle Christiansen,Rosa Figueiredo,Lars Magnus Hvattum,Michael Poss,Cristina Requejo +5 more
TL;DR: This paper addresses the robust vehicle routing problem with time windows by proposing two new formulations for the robust problem, each based on a different robust approach, and develops a new cutting plane technique for robust combinatorial optimization problems with complicated constraints.
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Benders Decomposition for the Hop-Constrained Survivable Network Design Problem
TL;DR: A thorough computational study of various branch-and-cut algorithms on a large set of instances including the real-based instances from SNDlib, able to solve the instances significantly faster than CPLEX 12 on the extended formulation.
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An improved Benders decomposition applied to a multi-layer network design problem
Bernard Fortz,Michael Poss +1 more
TL;DR: A branch-and-cut algorithm is used to improve the separation procedure of Gabrel et al. and Knippel et al for capacitated network design and details experiments on bi-layer networks.
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Affine recourse for the robust network design problem: Between static and dynamic routing
TL;DR: It is shown that affine routing can be seen as a generalization of the widely used static routing while still being tractable and providing cheaper solutions and that for these instances the optimal solutions based on affine routings tend to be as cheap as optimal network designs for dynamic routings.
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Robust combinatorial optimization with variable budgeted uncertainty
Michael Poss,Michael Poss +1 more
TL;DR: A new model for robust combinatorial optimization where the uncertain parameters belong to the image of multifunctions of the problem variables, an extension of the budgeted uncertainty introduced by Bertsimas and Sim, is introduced and a mixed-integer programming reformulation for the problem is proposed.