Subventions et des contributions :

Titre :
Closed-loop supply chain configuration and optimization
Numéro de l’entente :
RGPIN
Valeur d'entente :
110 000,00 $
Date d'entente :
10 mai 2017 -
Organisation :
Conseil de recherches en sciences naturelles et en génie du Canada
Location :
Ontario, Autre, CA
Numéro de référence :
GC-2017-Q1-02016
Type d'entente :
subvention
Type de rapport :
Subventions et des contributions
Informations supplémentaires :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023)

Nom légal du bénéficiaire :
Hassanzadeh Amin, Saman (Ryerson University)
Programme :
Programme de subventions à la découverte - individuelles
But du programme :

In a forward supply chain, products are sent from suppliers to manufacturers and finally, to customers. In a reverse supply chain, some of the products are returned by customers. A closed-loop supply chain (CLSC) encompasses both forward and reverse supply chains. The goal of a CLSC (e.g. tire remanufacturing), is to gain economic and environmental values from both new and returned products. This program will pursue three primary areas of research in regards to CLSCs. First, this research is concerned with several sources of uncertainty within CLSCs, including the demand, return rate and quality of returned products. Our goal in this instance is to identify and analyze the different sources of uncertainty operating simultaneously in CLSC networks. Second, this research will consider how to minimize waste across CLSC networks, studying relevant environmental factors with a view to uncovering sustainable solutions. Third, this research will investigate the impact of Big Data on CLSC network configuration and optimization, given that large amounts of data are sometimes available within CLSC parameters.
This research program will examine real case studies in Canada, CLSCs with a focus on four types of products: paper, tires, computers and hazardous materials. It will consider the typical CLSC networks of products, related recovery options (e.g. recycling), and federal and provincial policies in Canada. Moreover, it will propose mathematical models and develop appropriate solutions based on operations research techniques, e.g. robust optimization, multi-objective programming, and metaheuristic algorithms. Finally, it will provide relevant managerial insights and suggestions for practitioners focusing on Canadian industries.