International audience

Support vector clustering (SVC) has been proposed in the literature as a data-driven approach to build uncertainty sets in robust optimization. Unfortunately, the resulting SVC-based uncertainty sets induces a large number of additional variables and constraints in the robust counterpart of mathematical formulations. We propose a two-phase method to approximate the resulting uncertainty sets and overcome these tractability issues. This method is controlled by a parameter defining a trade-off between the quality of the approximation and the complexity of the robust models formulated. We evaluate the approximation method on three distinct, well-known optimization problems. Experimental results show that the approximated uncertainty set leads to solutions that are comparable to those obtained with the classic SVC-based uncertainty set with a significant reduction of the computation time. History: Accepted by Andrea Lodi, Area Editor for Design and Analysis of Algorithms—Discrete. Funding: This work was supported by the German-French Academy for the Industry of the Future [Data-driven collaboration in Industrial Supply Chains project].

We study the problem of making forecasts about the future availability of bicycles in stations of a bike-sharing system (BSS). This is relevant in order to make recommendations guaranteeing that the probability that a user will be able to make a journey is sufficiently high. To do this we use probabilistic predictions obtained from a queuing theoretical time-inhomogeneous model of a BSS. The model is parametrized and successfully validated using historical data from the Vélib' BSS of the City of Paris.

We consider a well-known NP-hard deterministic inventory control problem: the One-Warehouse Multi-Retailer (OWMR) problem.We present a simple combinatorial algorithm to recombine the optimal solutions of the natural single-echelon inventory subproblems into a feasible solution of the OWMR problem.This approach yields a 3approximation.We then show how this algorithm can be improved to a 2-approximation by halving the demands at the warehouse and at the retailers in the subproblems.Both algorithms are purely combinatorial and can be implemented to run in linear time for traditional linear holding costs and quadratic time for more general holding cost structures.We finally show that our technique can be extended to the Joint Replenishment Problem (JRP) with backorders and to the OWMR problem with non-linear holding costs.