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A Two-Level Approach to Large Mixed-Integer Programs with Application to Cogeneration in Energy-Efficient Buildings

Fu Lin(fulin***at***mcs.anl.gov)
Sven Leyffer(leyffer***at***mcs.anl.gov)
Todd Munson(tmunson***at***mcs.anl.gov)

Abstract: We study a two-stage mixed-integer linear program (MILP) with more than 1 million binary variables in the second stage. We develop a two-level approach by constructing a semi-coarse model (coarsened with respect to variables) and a coarse model (coarsened with respect to both variables and constraints). We coarsen binary variables by selecting a small number of pre-specified daily on/off profiles. We aggregate constraints by partitioning them into groups and summing over each group. With an appropriate choice of coarsened profiles, the semi-coarse model is guaranteed to find a feasible solution of the original problem and hence provides an upper bound on the optimal solution. We show that solving a sequence of coarse models converges to the same upper bound with proven finite steps. This is achieved by adding violated constraints to coarse models until all constraints in the semi-coarse model are satisfied. We demonstrate the effectiveness of our approach in cogeneration for buildings. The coarsened models allow us to obtain good approximate solutions at a fraction of the time required by solving the original problem. Extensive numerical experiments show that the two-level approach scales to large problems that are beyond the capacity of state-of-the-art commercial MILP solvers.

Keywords: Coarsened models, distributed generation, large-scale problems, two-level approach, multi-period planning, resource and cost allocation, two-stage mixed-integer programs.

Category 1: Integer Programming ((Mixed) Integer Linear Programming )

Citation: Preprint ANL/MCS-P5332-0415, Mathematics and Computer Science Division, Argonne National Laboratory, 9700 South Cass Ave Argonne, IL 60439

Download: [PDF]

Entry Submitted: 04/17/2015
Entry Accepted: 04/17/2015
Entry Last Modified: 04/17/2015

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