Integration of artificial intelligence and machine learning in national food service distribution networks

The unprecedented COVID-19 pandemic has revealed a chaotic vulnerability in the distribution of food service. People around the world were prevented from getting food products, which weakened international relations and placed governments on high alert. Meanwhile, in developed countries, an oversupply of food commodities caused serious food-waste problems, while in developing countries, food scarcity became critical. In addition, about 1 million people worldwide are food-insecure and in need of food assistance. How to effectively allocate food for food service is key to reduce excess surplus and perform food distributions. This chapter considers the complex problem of food distribution networks by integrating artificial intelligence and machine learning techniques in the process of demand-supply forecasting, food distribution allocation, and vehicle routing. The demand data from food banks and the supply-inventory data from food suppliers can only be better predicted through effective analytics and algorithms, and the transportation routes can only be better optimized through effective routing intelligence. In the post-Covid-19 era, the new technologies brought by these artificial-intelligent and machine-learning techniques provide the food industry with greater opportunities for enhancements. Food assistance is essential to society, where a supply surplus cannot solve hunger, but distributions do; however, the transportation of food products from suppliers to food service recipients is complicated, as these services are often provided by multiple food banks and distributors. With the aid of the latest artificial-intelligence and machine-learning algorithms, the food shortage can be addressed and the food waste shortage can be reduced through better demand forecasts and larger allocation efficiencies. To minimize the transportation times and maximize the safety of food recipients, shorter transportation routes are also desirable.