Role of Artificial Intelligence in the Management of Food Waste

Food waste is caused by a complex collection of interconnected behavior at both the supplier and customer levels. Computational and mathematical models provide various methods for simulating, diagnosing, and predicting various aspects of the dynamic food waste generation and prevention system. This paper describes three modeling methods that have been used to analyze food waste in the past. Bayesian networks and machine learning algorithms are applied to help determine how much food is discarded at the household level. Agent-Based Simulation was used to gain insight into how innovation and adoption of a particular technology can help minimize retail food waste. The first BN-ABM integrated model assesses consumer food waste levels affected by particular features and aspects, resulting in the model reaching equilibrium. Proofing that there is a need for policy interventions, including training, economic incentives, and campaigns, to obtain the resulting model transformation. These interventions are ready to be assessed by the model, but further study is needed to understand the effects of enforcing these structures on the accuracy of the BN-ABM predictive model. The second ABM model aims to determine the factors that establish the adoption of food waste reduction technology at the retail level, which is influenced by particular but not limited to economic factors, including a strong network between retailers and consumer's awareness regarding food waste reduction technology. These findings can study the effects of policy intervention regarding food waste reduction at the retail and consumer level. The third ABM model employed a general food chain network model consisting of consumers, traders, and producers to simulate the dynamic change of product flow between agents and to be able to assess the effect of agent's behavioral contrast. Resilience is measured by the ability to deal with shocks, and efficiency is the share of total food manages to be dispatched to consumers. At first glance, the simulation results seem to show a system trade-off between efficiency and resilience. Network chain structures with higher efficiency displayed more sensitivity to shocks, while networks with less efficiency show more resilience. However, there seem to be modifications in the results when applying several trading interactions and shock types. Resiliency and efficiency are affected by social aspects (trust and preference) in trading interaction between agents. An essential aspect of resilience is the agent's ability to switch links (trading partners) which shows the capability of reorganization. Insights regarding the research can be applicable when considering real-life food chain and structural reorganization to increase resilience and efficiency in meeting national food security goals.