TY - GEN
T1 - Multi-objective optimization of allocations and locations of incineration facilities with Voronoi diagram and genetic algorithm
T2 - 11th IEEE International Workshop on Computational Intelligence and Applications, IWCIA 2019
AU - Kamikawa, Taketo
AU - Hasuike, Takashi
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - This research focuses on the two purposes of maximizing the amount of heat generated by incineration and minimizing the collection distance of waste, in determining allocations and locations of general waste incineration facilities as a case study of Chiba northwest bay area. For these purposes, we propose the multi-objective optimization with Voronoi diagram and genetic algorithm (MOVGA). As for the maximization of the amount of generated heat, we predict the amount by using regression equation of multiple linear regression analysis and formulate it as the set partitioning problem (SPP) to maximize the prediction value. As for the minimization of waste collection distances, we formulate it as the multi-Weber problem. To solve these two problems, we use MOVGA, which has the seeds of the Voronoi diagram as a gene. As a result of the survey using data of 2015 year of Chiba northwest bay area, in the case of 3 facilities it was found that the calorific value increased enough to cover the power of 4,205 households (converted to housing complex) per year despite the increase of 3% t-km per year.
AB - This research focuses on the two purposes of maximizing the amount of heat generated by incineration and minimizing the collection distance of waste, in determining allocations and locations of general waste incineration facilities as a case study of Chiba northwest bay area. For these purposes, we propose the multi-objective optimization with Voronoi diagram and genetic algorithm (MOVGA). As for the maximization of the amount of generated heat, we predict the amount by using regression equation of multiple linear regression analysis and formulate it as the set partitioning problem (SPP) to maximize the prediction value. As for the minimization of waste collection distances, we formulate it as the multi-Weber problem. To solve these two problems, we use MOVGA, which has the seeds of the Voronoi diagram as a gene. As a result of the survey using data of 2015 year of Chiba northwest bay area, in the case of 3 facilities it was found that the calorific value increased enough to cover the power of 4,205 households (converted to housing complex) per year despite the increase of 3% t-km per year.
KW - Voronoi diagram
KW - combustible waste
KW - genetic algorithm
KW - incineration facility
KW - thermal energy
UR - http://www.scopus.com/inward/record.url?scp=85078888432&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85078888432&partnerID=8YFLogxK
U2 - 10.1109/IWCIA47330.2019.8955065
DO - 10.1109/IWCIA47330.2019.8955065
M3 - Conference contribution
AN - SCOPUS:85078888432
T3 - 2019 IEEE 11th International Workshop on Computational Intelligence and Applications, IWCIA 2019 - Proceedings
SP - 35
EP - 40
BT - 2019 IEEE 11th International Workshop on Computational Intelligence and Applications, IWCIA 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 9 November 2019 through 10 November 2019
ER -