We address the optimal design of a Distribution Network (DN), presenting a procedure employing Multi-Objective Genetic Algorithms (MOGA) to select the (sub) optimal DN configuration. Using multi-objective genetic optimization allows solving a nonlinear design problem with piecewise constant contributions in addition to linear ones. The MOGA application allows finding a Pareto frontier of (sub) optimal solutions, which is compared with the frontier obtained solving the same problem with linear programming, where piecewise constant contributions are linearly approximated. The two curves represent, respectively, the upper and the lower limit of the region including the real Pareto curve. Both the genetic optimization model and the linear programming are applied under structural constraints to a case study describing the DN of an Italian enterprise.
A Multi-objective Genetic Optimization Technique for the Strategic Design of Distribution Networks / Bevilacqua, Vitoantonio; Dotoli, Mariagrazia; Falagario, Marco; Sciancalepore, Fabio; D’Ambruoso, Dario; Saladino, Stefano; Scaramuzzi, Rocco (LECTURE NOTES IN COMPUTER SCIENCE). - In: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence: 7th International Conference, ICIC 2011, Zhengzhou, China, August 11-14, 2011, Revised Selected Papers / [a cura di] De-Shuang Huang, Yong Gan, Phalguni Gupta, M. Michael Gromiha. - STAMPA. - Berlin; Heidelberg : Springer, 2011. - ISBN 978-3-642-25943-2. - pp. 243-250 [10.1007/978-3-642-25944-9_32]
A Multi-objective Genetic Optimization Technique for the Strategic Design of Distribution Networks
Vitoantonio Bevilacqua;Mariagrazia Dotoli;Fabio Sciancalepore;
2011-01-01
Abstract
We address the optimal design of a Distribution Network (DN), presenting a procedure employing Multi-Objective Genetic Algorithms (MOGA) to select the (sub) optimal DN configuration. Using multi-objective genetic optimization allows solving a nonlinear design problem with piecewise constant contributions in addition to linear ones. The MOGA application allows finding a Pareto frontier of (sub) optimal solutions, which is compared with the frontier obtained solving the same problem with linear programming, where piecewise constant contributions are linearly approximated. The two curves represent, respectively, the upper and the lower limit of the region including the real Pareto curve. Both the genetic optimization model and the linear programming are applied under structural constraints to a case study describing the DN of an Italian enterprise.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
