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The sample average approximation (SAA) approach is a widely used technique, based on Monte‐Carlo simulation, often applied to large‐scale stochastic optimization problems. In this approach, a set of sample average problems with multiple copies of sampled scenarios are generated and solved exactly. In other words, there is an implicit assumption that the sample average problems are solvable to optimality...
We consider the Geometric Connected Facility Location Problem (GCFLP): given a set of clients C ⊂ ℝ d , one wants to select a set of locations F ⊂ ℝ d where to open facilities, each at a fixed cost f≥0. For each client j ∈ C , one has to choose to either connect it to an open facility...
In this paper we consider the Robust Connected Facility Location (ConFL) problem within the robust discrete optimization framework introduced by Bertsimas and Sim (2003). We propose an Approximate Robust Optimization (ARO) method that uses a heuristic and a lower bounding mechanism to rapidly find high-quality solutions. The use of a heuristic and a lower bounding mechanism–as opposed to solving the...
We apply a greedy randomized adaptive search procedure (GRASP) to solve the connected facility location problem heuristically. Diversification property is assured by applying a randomized greedy algorithm to construct feasible solutions in a multi-start fashion. Intensification elements are guaranteed due to two facility-based local search techniques. The computational study is conducted on a parameterized...
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