Supply chain complexity and risk mitigation - A hybrid optimization-simulation model

- We address a historical policy issue about food safety and quality within the Canadian wheat industry.
- We explicitly model the Canadian wheat supply chain in a realistic manner.
- We combine the beneficial features of both simulation and optimization for general modeling applications.
- Our results have direct implications for the optimal testing strategy for Canadian CWRS wheat.

With food safety a growing concern in agriculture, the structure and management of agricultural supply chains has become a significant policy issue. In turn, agricultural supply chains are often analytically complex, characterized by feedback and time sensitive, often random parameters. Modern commodity chains such as wheat handling in Canada are no exception. Recently, the Canadian government classes of wheat, replacing it by a new wheat segregation system that relies on trust and self-declaration of wheat type by individual farmers. To maintain food safety as well as operate cost-effectively in this new trust-based system, wheat handlers may be forced to develop a set of contamination testing strategies to maintain historical wheat quality and consistency. In contrast to much of the extant literature, this research builds a hybrid optimization-simulation model representing the new Canadian wheat supply chain, with the goal of identifying cost efficient varietal testing strategies. After solving for a base scenario, sensitivity analysis is conducted on key variables that influence wheat quality testing strategies. Our results validate the utility of currently employed wheat quality testing strategies in the Canadian supply chain.