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

Result code in 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>

Alternative codes found

RIV/70883521:28140/23:63563338

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

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

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

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

Publication year

2023

Confidentiality

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

Name of the periodical

COMPUTERS & INDUSTRIAL ENGINEERING

ISSN

0360-8352

e-ISSN

1879-0550

Volume of the periodical

178

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

20

Pages from-to

„“-„“

UT code for WoS article

000956557700001

EID of the result in the Scopus database

2-s2.0-85163670241