###### Abstract
The impacts of these factors also extend to inventory and transportation management, which must be agile. Shelf life is directly linked to time, and the greater the time gap between production and retail, the less time this product will have to be purchased and consumed. Food quality, especially for fresh foods, is not constant and tends to decline until it reaches zero [18]. Perishable products may deteriorate during transport, and the permitted storage time is shortened [19]. Because it is challenging, planning involving temporality related to production, transportation, and storage directly impacts the product's valuable life. Techniques to combine decisions in an integrated way, focusing on improving solutions, such as Product Inventory Routing Problems (IRP), have motivated several types of research that grow in importance by including issues involving perishability [20]. After this brief report about what happens in the milk production chain, we propose investigating a practical problem by applying Automated Planning and Scheduling (APS). As this Artificial Intelligence (AI) tool is relatively new, we aim to understand its ability to optimize the supply chain management of perishable products. The real-world problem inspired the problem we will discuss here is as follows: industrial milk processing plants in different cities have independent production capacities and can produce different products. There is also the possibility of product processing starting in one production plant and finishing in another. The selection of final productsare perishable and must go through a quarantine period, after which they will go to a geographically dispersed distribution center within their useful life. The connections between plants and distribution centers use a heterogeneous truck fleet. One question stands out as follows: how can we optimize production planning, inventory management, and transportation to meet demand and consider the perishability of dairy products? We developed three distinct models to address the problem, one formulated in PDDL (Planning Domain Definition Language), another implemented a Genetic Algorithm using the DEAP framework (Distributed Evolutionary Algorithms in Python), and a third based on MILP (Mixed-Integer Linear Programming). The solution strategies comprise Automated Planning and Scheduling (APS), solved by a temporal planner, applying a Genetic Algorithm, and resolving the MILP model through a commercial optimization solver, representing the exact method. Another essential factor that is part of the scope of the problem is transportation, since there is a need to move products from one point to another. Many perishable materials and products are transported daily, and the limited shelf life of dairy products impacts operational and transport costs, environmental concerns, time constraints, stock relations, and high transporting frequency [7]. Transportation models related to the dairy industry have a distinct appeal from the point of view of supply chain logistics in general, and companies invest heavily in freight transport [21]. After understanding the dairy supply chain and its peculiarities in the issues of stretching and transport, this research applied optimization methods to the problem of inventory routing, coupled with the dairy production chain, with active production in 4 (four) processing plants, one distribution center, designating a mix of six products in a heterogenic fleet of 20 combustion vehicles and four electric forklifts. We developed a PDDL (Planning Domain Definition Language) and mathematical models to help solve the problem. The solving methods applied are Automated Planning and Scheduling, and we also implement a Genetic Algorithm and ran the mathematical model in a commercial solver. The main objective is minimizing the makespan of the processes during the middle-mile stage, reducing the total time required to complete all operations.