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<p>Post-disaster waste management is one of the most crucial tasks in the recovery phase of the disaster cycle, and it was created to assist affected communities in returning to a stable state following a disaster. To develop an efficient post-disaster waste management strategy, this study presents a multi-objective two-stage stochastic mixed integer linear programming model for post-disaster waste management. The proposed mathematical model was developed based on a mixed strategy of on-site and off-site waste separation in the supply chain. This study aims to minimize not only the total cost and the environmental impact to provide waste flow decisions and choose collection and separation sites, recycling sites, landfill sites, and incineration sites throughout the supply chain under the uncertain situation. To solve a multi-objective problem, a normalized weighted sum method is used to find the solution. A numerical case based on realistic data is presented to validate and verify the proposed model. Based on the numerical example, the results demonstrated that the implementation of the mixed strategy for waste separation with the consideration of uncertain situations can reduce the total cost, balance the environmental impact, and determine the unexpected situation in the post-disaster waste supply chain efficiently.</p>
</abstract>

Post-disaster waste management is one of the most crucial tasks in the recovery phase of the disaster cycle, and it was created to assist affected communities in returning to a stable state following a disaster. To develop an efficient post-disaster waste management strategy, this study presents a multi-objective two-stage stochastic mixed integer linear programming model for post-disaster waste management. The proposed mathematical model was developed based on a mixed strategy of on-site and off-site waste separation in the supply chain. This study aims to minimize not only the total cost and the environmental impact to provide waste flow decisions and choose collection and separation sites, recycling sites, landfill sites, and incineration sites throughout the supply chain under the uncertain situation. To solve a multi-objective problem, a normalized weighted sum method is used to find the solution. A numerical case based on realistic data is presented to validate and verify the proposed model. Based on the numerical example, the results demonstrated that the implementation of the mixed strategy for waste separation with the consideration of uncertain situations can reduce the total cost, balance the environmental impact, and determine the unexpected situation in the post-disaster waste supply chain efficiently.

Multi-objective two-stage stochastic optimization model for post-disaster waste management

Department of Industrial Engineering, Faculty of Engineering

Production Engineering Archives

This work is licensed under the Creative Commons Attribution 4.0 International License.

Post-disaster waste management is one of the most crucial tasks in the recovery phase of the disaster cycle, and it was created to assist affected communities...

Multi-objective two-stage stochastic optimization model for post-disaster waste management

Publié en ligne: 15 févr. 2023

Pages: 58 - 68

Reçu: 01 nov. 2022

Accepté: 12 janv. 2023

DOI: https://doi.org/10.30657/pea.2023.29.8

Mots clés
Post-disaster, Waste management, Multi-objective, Two-stage stochastic model

© 2023 Chawis Boonmee et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Multi-objective two-stage stochastic optimization model for post-disaster waste management

Publié en ligne: 15 févr. 2023

Pages: 58 - 68

Reçu: 01 nov. 2022

Accepté: 12 janv. 2023

DOI: https://doi.org/10.30657/pea.2023.29.8

Mots clésPost-disaster, Waste management, Multi-objective, Two-stage stochastic model

© 2023 Chawis Boonmee et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Post-disaster, Waste management, Multi-objective, Two-stage stochastic model