Simulation-based genetic algorithm for optimizing a municipal cooperative waste supply chain in a pandemic

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Title: Simulation-based genetic algorithm for optimizing a municipal cooperative waste supply chain in a pandemic
Authors: Peiman Ghasemi (Department of Business Decisions and Analytics, Faculty of Business, Economics and Statistics, University of Vienna), Alireza Goli (Department of Industrial Engineering and Future Studies, Faculty of Engineering, University of Isfahan), Fariba Goodarzian (Edinburgh Business School (EBS) and School of Social Sciences, Heriot-Watt University), Jan Fabian Ehmke (Department of Business Decisions and Analytics, Faculty of Business, Economics and Statistics, University of Vienna)
Source: Engineering Applications of Artificial Intelligence ; issn:0952-1976
Publisher Information: Elsevier BV
Publication Year: 2025
Collection: University of Vienna: Phaidra
Subject Terms: Supply chain network, Simulation-optimization model, Urban waste management, Simulation-based genetic algorithm, Cooperative game
Description: The abstract is available here: https://uscholar.univie.ac.at/o:2110673 ; The abstract is available here: https://uscholar.univie.ac.at/o:2110673
Document Type: article in journal/newspaper
File Description: application/pdf
Language: English
Relation: hdl:11353/10.2110673; https://phaidra.univie.ac.at/o:2110673
DOI: 10.1016/j.engappai.2024.109478
Availability: https://doi.org/10.1016/j.engappai.2024.109478
https://phaidra.univie.ac.at/o:2110673
Rights: © 2024 The Authors ; open access ; http://creativecommons.org/licenses/by/4.0/
Accession Number: edsbas.B04E0200
Database: BASE
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  Data: Simulation-based genetic algorithm for optimizing a municipal cooperative waste supply chain in a pandemic
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  Data: Engineering Applications of Artificial Intelligence ; issn:0952-1976
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  Data: 2025
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  Data: The abstract is available here: https://uscholar.univie.ac.at/o:2110673 ; The abstract is available here: https://uscholar.univie.ac.at/o:2110673
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  Data: 10.1016/j.engappai.2024.109478
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  Data: https://doi.org/10.1016/j.engappai.2024.109478<br />https://phaidra.univie.ac.at/o:2110673
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  Data: © 2024 The Authors ; open access ; http://creativecommons.org/licenses/by/4.0/
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