TY - GEN
T1 - A tight upper bound for grid-based wind farm layout optimization
AU - Quan, Ning
AU - Kim, Harrison
N1 - Publisher Copyright:
© Copyright 2016 by ASME.
PY - 2016
Y1 - 2016
N2 - This paper uses the method developed by Billionnet et al. (1999) to obtain tight upper bounds on the optimal values of mixed integer linear programming (MILP) formulations in gridbased wind farm layout optimization. The MILP formulations in grid-based wind farm layout optimization can be seen as linearized versions of the 0-1 quadratic knapsack problem (QKP) in combinatorial optimization. The QKP is NP-hard, which means the MILP formulations remain difficult problems to solve, especially for large problems with grid sizes of more than 500 points. The upper bound method proposed by Billionnet et al. is particularly well-suited for grid-based wind farm layout optimization problems, and was able to provide tight optimality gaps for a range of numerical experiments with up to 1296 grid points. The results of the numerical experiments also suggest that the greedy algorithm is a promising solution method for large MILP formulations in grid-based layout optimization that cannot be solved using standard branch and bound solvers.
AB - This paper uses the method developed by Billionnet et al. (1999) to obtain tight upper bounds on the optimal values of mixed integer linear programming (MILP) formulations in gridbased wind farm layout optimization. The MILP formulations in grid-based wind farm layout optimization can be seen as linearized versions of the 0-1 quadratic knapsack problem (QKP) in combinatorial optimization. The QKP is NP-hard, which means the MILP formulations remain difficult problems to solve, especially for large problems with grid sizes of more than 500 points. The upper bound method proposed by Billionnet et al. is particularly well-suited for grid-based wind farm layout optimization problems, and was able to provide tight optimality gaps for a range of numerical experiments with up to 1296 grid points. The results of the numerical experiments also suggest that the greedy algorithm is a promising solution method for large MILP formulations in grid-based layout optimization that cannot be solved using standard branch and bound solvers.
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U2 - 10.1115/DETC2016-59712
DO - 10.1115/DETC2016-59712
M3 - Conference contribution
AN - SCOPUS:85008195200
T3 - Proceedings of the ASME Design Engineering Technical Conference
BT - 42nd Design Automation Conference
PB - American Society of Mechanical Engineers (ASME)
T2 - ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2016
Y2 - 21 August 2016 through 24 August 2016
ER -