Hierarchical clustering-based algorithms for optimal waste collection point locations in large-scale problems: A framework development and case study

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F23%3APU148046" target="_blank" >RIV/00216305:26210/23:PU148046 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/70883521:28140/23:63563338

Výsledek na webu

<a href="https://doi.org/10.1016/j.cie.2023.109142" target="_blank" >https://doi.org/10.1016/j.cie.2023.109142</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.cie.2023.109142" target="_blank" >10.1016/j.cie.2023.109142</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Hierarchical clustering-based algorithms for optimal waste collection point locations in large-scale problems: A framework development and case study

Popis výsledku v původním jazyce

The cities face the challenge of optimizing investments in waste management to meet EU standards while maintaining economic affordability. One of the issues is the optimal location for specialized waste collection points. The main target is to find the lowest number of collection points that would still attain waste production, and the average walking distance to the waste container would be kept beneath the tolerable limit for citizens. The population density and waste production vary over city parts; thus, the need for specialized containers in more populated city centers, industrial zones, or household streets differs. This paper develops a new compu-tational approach providing a robust generalized decision-support tool for waste collection bin location and allocation. This task leads to a mixed-integer linear program which is not solvable for larger cities in a reasonable time. Therefore, hierarchical clustering is applied to simplify the model. Two strategies for solving waste bin allocation (for multiple variants of the model formulation) are implemented and compared - sub-problem definition and representative selection approaches. The resulting framework is tested on the artificial instance and a few case studies where the structure and properties of results are discussed. The combination of presented approaches proved to be appropriate for large-scale instances. The representative selection approach leads to a better distribution of containers within the area in the single-objective model formulation.

Název v anglickém jazyce

Hierarchical clustering-based algorithms for optimal waste collection point locations in large-scale problems: A framework development and case study

Popis výsledku anglicky

The cities face the challenge of optimizing investments in waste management to meet EU standards while maintaining economic affordability. One of the issues is the optimal location for specialized waste collection points. The main target is to find the lowest number of collection points that would still attain waste production, and the average walking distance to the waste container would be kept beneath the tolerable limit for citizens. The population density and waste production vary over city parts; thus, the need for specialized containers in more populated city centers, industrial zones, or household streets differs. This paper develops a new compu-tational approach providing a robust generalized decision-support tool for waste collection bin location and allocation. This task leads to a mixed-integer linear program which is not solvable for larger cities in a reasonable time. Therefore, hierarchical clustering is applied to simplify the model. Two strategies for solving waste bin allocation (for multiple variants of the model formulation) are implemented and compared - sub-problem definition and representative selection approaches. The resulting framework is tested on the artificial instance and a few case studies where the structure and properties of results are discussed. The combination of presented approaches proved to be appropriate for large-scale instances. The representative selection approach leads to a better distribution of containers within the area in the single-objective model formulation.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

COMPUTERS & INDUSTRIAL ENGINEERING

ISSN

0360-8352

e-ISSN

1879-0550

Svazek periodika

178

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

20

Strana od-do

„“-„“

Kód UT WoS článku

000956557700001

EID výsledku v databázi Scopus

2-s2.0-85163670241