This paper investigates a real-world distribution problem arising in the vehicle production industry, particularly in a logistics company, in which cars and vans must be loaded on auto-carriers and then delivered to dealerships. A solution to the problem involves the loading and optimal routing, without violating the capacity and time window constraints for each auto-carrier. A two-phase heuristic algorithm was implemented to solve the problem. In the first phase the heuristic builds a route with an optimal insertion procedure, and in the second phase the determination of a feasible loading. The experimental results show that the purposed algorithm can be used to tackle the transportation problem in terms of minimizing total traveling distance, loading/unloading operations and transportation costs, facilitating a decision-making process for the logistics company.

Dysphonia is a prevalent symptom of some respiratory diseases that affects voice quality, even for prolonged periods. For its diagnosis, speech-language pathologists make use of different acoustic parameters to perform objective evaluations on patients and determine the type of dysphonia that affects them, such as hyperfunctional and hypofunctional dysphonia, which is important because each type requires a different treatment. In the field of artificial intelligence this problem has been addressed through the use of acoustic parameters that are used as input data to train machine learning and deep learning models. However, its purpose is usually to identify whether a patient is ill or not, making binary classifications between healthy voices and voices with dysphonia, but not between dysphonias. In this paper, harmonic-to-noise ratio, cepstral peak prominence-smoothed, zero crossing rate and the means of the Mel frequency cepstral coefficients (2-19) are used to make multiclass classification of voices with euphony, hyperfunction and hypofunction by means of six machine learning algorithms, which are: Random Forest, K nearest neighbors, Logistic regression, Decision trees, Support vector machines and Naive Bayes. In order to evaluate which of them presents a better performance to identify the three voice classes, bootstrap.632 was used. It is concluded that the best confidence interval ranges from 87% to 92%, in terms of accuracy for the K Nearest Neighbors model. Results can be implemented in the development of a complementary application for the clinical diagnosis or monitoring of a patient under the supervision of a specialist.