Abstract
We consider global optimization of mixed-integer bilinear programs (MIBLP) using discretization-based mixed-integer linear programming (MILP) relaxations. We start from the widely used radix-based discretization formulation (called R-formulation in this paper), where the base R may be any natural number, but we do not require the discretization level to be a power of R. We prove the conditions under which R-formulation is locally sharp, and then propose an \(R^+\)-formulation that is always locally sharp. We also propose an H-formulation that allows multiple bases and prove that it is also always locally sharp. We develop a global optimization algorithm with adaptive discretization (GOAD) where the discretization level of each variable is determined according to the solution of previously solved MILP relaxations. The computational study shows the computational advantage of GOAD over general-purpose global solvers BARON and SCIP.
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Acknowledgements
The authors are grateful to the Natural Sciences and Engineering Research Council of Canada for the Discovery Grant RGPIN 418411-13 and the Collaborative Research and Development Grant CRDPJ 485798-15.
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Appendices
Appendix A: Parameters for the multiperiod pooling problems
See Table 7.
Appendix B: The SB formulation for the multiperiod pooling problems
See Table 8 for the list of symbols in the SB formulation
The SB formulation
Objective:
s.t. Bilinear terms:
Mass Balance:
Quality Bounds:
Tank Capacity:
Pipe Capacity:
Individual Flow Constraints:
Operation Mode:
Variable Bounds:
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Cheng, X., Li, X. Discretization and global optimization for mixed integer bilinear programming. J Glob Optim 84, 843–867 (2022). https://doi.org/10.1007/s10898-022-01179-3
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DOI: https://doi.org/10.1007/s10898-022-01179-3