Comparing an Artificial Intelligence Planner with Traditional Optimization Methods: A Case Study in the Dairy Industry

Automated Planning and Scheduling (APS) is an area of artificial intelligence dedicated to generating efficient plans to achieve goals by optimizing objectives. This case study is based on a middle-mile segment of the dairy supply chain. This article focuses on applying and analyzing APS compared to the following classical optimization methods: mathematical modeling based on Mixed-Integer Programming (MILP) and the Genetic Algorithm (GA). The language supported for APS modeling is Planning Domain Definition Language (PDDL), and the temporal solver used is the OPTIC planner. Optimization methods are guided by a mathematical model developed specifically for the research scope, considering production, inventory, and transportation conditions and constraints. Dairy products are highly perishable; therefore, the main optimization objective is to minimize Tmax, i.e., the total time to meet demand, ensuring that the products are available at the distribution center with a viable shelf life for commercialization. The APS application showed limitations compared to the other optimization approaches, with the Exact Method proving the most efficient. Finally, all algorithms, models, and results are available on GitHub, aiming to foster further research and enhance operational efficiency in the dairy sector through optimization.