Dissertation Defense - Sheida Etemadidavan

What:  Engineering Management and Systems Engineering Public Dissertation Defense
When: Friday November 15^th, 2024, 9:00 - 11:00 am ET
Where:  Online 

https://odu.zoom.us/j/91310586425?pwd=8NwoalyxvcCOXvHVNWq2tK7ZpB8x1f.1

Meeting ID 913 1058 6425

Who:  Sheida Etemadidavan, Doctoral Candidate

TITLE: 

AN AGENT-BASED APPROACH FOR DESIGNING A SUSTAINABLE SUPPLIER SELECTION IN STOCHASTIC ENVIRONMENT: A SIMULATION STUDY

ABSTRACT:

In the 21^st century, businesses are constantly striving for competitive advancement and increasing their economic performance in the rapidly changing market. Collaboration with other businesses and partnerships with them are becoming more common. As such, businesses are starting to invest in finding the right partners to stay competitive in an ever-changing business landscape. Suppliers play a crucial role in businesses' decision-making as they provide the materials, goods, and services necessary for a company to operate and succeed. Therefore, their selection, management, and relationship must be carefully considered to ensure optimal performance and profitability. A sustainable framework in a business's relationship with suppliers helps to ensure ethical and environmentally responsible practices and long-term viability for both parties. So, the supply chain will benefit from the right supplier selection and incorporating a sustainability framework. However, supplier selection is a complex adaptive system with uncertainty and dynamics. Moreover, incorporating a sustainability framework with economic, environmental, and social dimensions into the supplier selection process would add an extra layer of complexity to the decision-making process. Decision-makers have long faced difficulties in modeling supplier selection, especially large-scale, due to the level of complexity. While many modeling approaches are documented in the literature, not all of them can adequately account for stochastics and large-scale complex supplier selection. Agent-based modeling and simulation (ABMs) is an effective modeling approach for investigating stochastic environments. ABMs provides decision-makers with a deeper understanding of complex systems involving multiple autonomous interacting agents by using simple rules to design, model, and analyze them. This research proposes an agent-based model of sustainable supplier selection to show simulation ability in addressing more uncertainty factors and dynamism in large-scale supplier selection and hoping to provide valuable insights and meaningful results to the supply chain field.