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Titre du projet de recherche

Conception d'une chaîne d'approvisionnement inverse pour les déchets municipaux solides

niveau d'étude



Directeur : Maha Ben Ali

Codirecteur(s) : Rim Larbi, École de Technologie Supérieure

Fin de l'affichage

31 juillet 2024

Domaines d'expertise

Génie industriel

Modélisation mathématique

Pôle(s) d'excellence secondaire(s)

Environnement, économie et société

Modélisation et intelligence artificielle

Unité(s) et département(s)

Département de mathématiques et de génie industriel

Description détaillée

In Canada, the total amount of solid waste generated increased by 16% from 2012 to 2018, reaching 35.6 million tons, while the diversion rate was approximately 28% in 2018 [1]. Given these trends and Canada's goal of zero plastic waste and net-zero greenhouse gas emissions by 2050, there is a real need for resilient and sustainable strategies and tools for integrated and circular waste reverse supply chains that bring together the various stakeholders. One of the main challenges in Municipal Solid Waste (MSW) is the uncertainty associated with MSW production, while in the literature uncertainty and sustainability are rarely embedded in MSW optimization models [2]. On the other hand, Industry 4.0 will play a key role in overcoming the challenges of current approaches [2, 3, 4]. Thus, there are new opportunities to improve MSW logistics and supply chain planning models. This research project aims to integrate Industry 4.0 technologies and to consider the uncertainty related to MSW and the three pillars of sustainability: economic, environmental, and social criteria in the design and management of the MSW supply chain. The MSW network design will be studied under different strategies/policies such as waste separation at source and adaptive MSW services.


1. Canada, S. Solid waste diversion and disposal. 2020; Available from: https://www.canada.ca/en/environmentclimate‐change/services/environmental‐indicators/solid‐waste‐diversion‐disposal.html.

2. Zhao, Y. and H. Li, Understanding municipal solid waste production and diversion factors utilizing deep‐learning methods. Utilities Policy, 2023. 83: p. 101612.

3. Gutierrez‐Lopez, J., R.G. McGarvey, C. Costello, and D.M. Hall, Decision Support Frameworks in Solid Waste Management: A Systematic Review of Multi‐Criteria Decision‐Making with Sustainability and Social Indicators. Sustainability, 2023. 15(18): p. 13316.

4. Maddikunta, P.K.R., et al., Industry 5.0: A survey on enabling technologies and potential applications. Journal of Industrial Information Integration, 2022. 26: p. 100257.

Possibilité de financement

Funding is available for 4 years, subject to research progress.

Maha Ben Ali

Maha Ben Ali

Professeure adjointe

Fiche complète