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-ge
nerating units. The solution concerns the allocation of the units over time
and considers expected variations in the heat load and power. The engineer
ing knowledge exploits the Hardware Composites, a conceptual tool for the o
peration of utility systems. The knowledge is capitalized at three differen
t levels: (i) to exclude redundant combinations of decision variables, (ii)
to prioritize the branching of the algorithm, and (iii) to prune the binar
y tree. Using a non-commercial LP, an MILP solver is designed and compared
with highly-valued, state-of-the-art commercial solvers. The comparisons ar
e particularly impressive in that the customized development outperforms th
e sophisticated packages and accommodates accelerations of at least two ord
ers of magnitude. (C) 2000 Elsevier Science Ltd. All rights reserved.