Results

For the test cases that have been tried the solutions produced by our decomposition algorithm are close to the optimal solution, but the latter is not always reached. The reason behind this is that the decomposition algorithm is a sequence of iterations that may accumulate errors at different levels---lowering the tolerances used as stopping criterion in the submodels most of the time does not improve the solution. However, the configuration of the decomposition algorithm itself shows some impact on the solution: in phases 1 and 2 one may choose, for instance, to stop once the first submodel returns no improvement or continue until no more proposals are generated. Generating more proposals sometimes helps finding a better solution, but it also increases the number of times (sub)problems are solved and hence prolongates the solving time.