Cost-based filtering for stochastic inventory control

Abstract. An interesting class of production/inventory control problems considers a single product and a single stocking location, given a stochastic demand with a known non-stationary probability distribution. Under a widely-used control policy for this type of inventory system, the objective is to find the optimal number of replenishments, their timings and their respective order-up-to-levels that meet customer demands to a required service level. We extend a known CP approach for this problem using a cost-based filtering method. Our algorithm can solve to optimality instances of realistic size much more efficiently than previous approaches, often with no search effort at all.

Constraint Programming Models for Graceful Graphs

Abstract. The problem of finding a graceful labelling of a graph, or proving that the graph is not graceful, has previously been modelled as a CSP. A new and much faster CSP model of the problem is presented, with several new results for graphs whose gracefulness was previously unknown. Several classes of graph that are conjectured to be graceful only for small instnces are investigated: after a certain size, it appears that for some of these classes the search to prove that there is no graceful labelling is essentially the same for each successive instance. The possibility of constructing a proof of the conjecture based on the search is discussed.

Network planning under uncertainty with an application to hydropower generation

The original publication is available at www.springerlink.comWe present. a general modeling framework for the robust opti- mization of linear network problems with uncertainty in the val- ues of the right-hand side. In contrast to traditional approadies in mathematical programming, we use scenarios to characterize the uncertainty. Solutions are obtained for each scenario and these individual scenarios are aggregated to yield a nonanticipative or implementable policy that minimizes the regret of wrong decisions. A given solution is termed robust if it minimizes the sum over the scenarios of the weighted upper difference between the objective function value for the solution and the objective function value for the optimal solution for each scenario, while satisfying certain nonanticipativity constraints, This approach results in a huge model with a network submodel per scenario plus coupling constraints. Several decomposition approaches are considered, namely Dantzig—Wolfe decomposition, various types of Benders decomposition and different quadratic network approaches for approximating Augmented Lagrangian decomposition. We present computational results for these methods, including two implementation versions of the Lagrangian based method: a sequential implementation and a parallel implementation on a network of three workstationsPublicad

Improved Full-Newton Step O(nL) Infeasible Interior-Point Method for Linear Optimization

We present several improvements of the full-Newton step infeasible interior-point method for linear optimization introduced by Roos (SIAM J. Optim. 16(4):1110–1136, 2006). Each main step of the method consists of a feasibility step and several centering steps. We use a more natural feasibility step, which targets the ?+-center of the next pair of perturbed problems. As for the centering steps, we apply a sharper quadratic convergence result, which leads to a slightly wider neighborhood for the feasibility steps. Moreover, the analysis is much simplified and the iteration bound is slightly better.Software TechnologyElectrical Engineering, Mathematics and Computer Scienc