Grants and Contributions:

Title:
Closed-loop supply chain configuration and optimization
Agreement Number:
RGPIN
Agreement Value:
$110,000.00
Agreement Date:
May 10, 2017 -
Organization:
Natural Sciences and Engineering Research Council of Canada
Location:
Ontario, CA
Reference Number:
GC-2017-Q1-02016
Agreement Type:
Grant
Report Type:
Grants and Contributions
Additional Information:

Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023)

Recipient's Legal Name:
Hassanzadeh Amin, Saman (Ryerson University)
Program:
Discovery Grants Program - Individual
Program Purpose:

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.