Title: Multi-objective optimisation of allocations and locations of incineration facilities with Voronoi diagram and genetic algorithm: case study of Hiroshima City and Aki County

Authors: Taketo Kamikawa; Takashi Hasuike

Addresses: Department of Industrial and Management Systems Engineering, School of Creative Science and Engineering, Waseda University, Shinjuku Ward, Japan ' Department of Industrial and Management Systems Engineering, School of Creative Science and Engineering, Waseda University, Shinjuku Ward, Japan

Abstract: This research focuses on the two objectives of maximising the amount of heat generated by incineration and minimising the waste collection distances divided by population densities, in determining allocations and locations of general waste incineration facilities as a case study of Hiroshima City and Aki County in Japan. For these objectives, we propose the version 2 of multi-objective optimisation with Voronoi diagram and genetic algorithm (MOVGA2). As for the maximisation of the amount of generated heat, we predict the amount by the regression equation of multiple linear regression analysis using 2013 to 2017 panel data and formulate it as the set partitioning problem (SPP) to maximise the prediction value. As for the minimisation of waste collection distances divided by population densities, we formulate it as the multi-Weber problem. To solve these two problems, we use MOVGA2, which has the seeds and weights of the Laguerre Voronoi diagram as a gene. As a result of the survey using data of the year 2017 of Hiroshima City and Aki County, in the case of two existing facilities, two new facilities and three closed facilities it was found that the calorific value increased enough to cover the power of 1,233 households (converted to housing complex) per year despite the increase of only 9% t-km for waste collection distances.

Keywords: genetic algorithm; Voronoi diagram; thermal energy; incineration facility; combustible waste.

DOI: 10.1504/IJCISTUDIES.2021.115425

International Journal of Computational Intelligence Studies, 2021 Vol.10 No.2/3, pp.99 - 126

Received: 22 Mar 2020
Accepted: 01 Jul 2020

Published online: 19 May 2021 *

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