The paper explains a paradigm for the integration of engineering knowledge with the search strategy of a Branch and Bound algorithm. The optimization is fairly generic and addresses industrial applications comprising power-generating units. The solution concerns the allocation of the units over time and considers expected variations in the heat load and power. The engineering knowledge exploits the Hardware Composites, a conceptual tool for the operation of utility systems. The knowledge is capitalized at three different levels: (i) to exclude redundant combinations of decision variables, (ii) to prioritize the branching of the algorithm, and (iii) to prune the binary tree. Using a non-commercial LP, an MILP solver is designed and compared with highly-valued, state-of-the-art commercial solvers. The comparisons are particularly impressive in that the customized development outperforms the sophisticated packages and accommodates accelerations of at least two orders of magnitude. (C) 2000 Elsevier Science Ltd.The paper explains a paradigm for the integration of engineering knowledge with the search strategy of a Branch and Bound algorithm. The optimization is fairly generic and addresses industrial applications comprising power-generating units. The solution concerns the allocation of the units over time and considers expected variations in the heat load and power. The engineering knowledge exploits the Hardware Composites, a conceptual tool for the operation of utility systems. The knowledge is capitalized at three different levels: (i) to exclude redundant combinations of decision variables, (ii) to prioritize the branching of the algorithm, and (iii) to prune the binary tree. Using a non-commercial LP, an MILP solver is designed and compared with highly-valued, state-of-the-art commercial solvers. The comparisons are particularly impressive in that the customized development outperforms the sophisticated packages and accommodates accelerations of at least two orders of magnitude.
