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The paper aims to analyze chip formation during machining process since it can be a very important indicator for the quality of the machining process, as some chip forms can be undesirable.

It is essential to determine the sensitivity of the chip formation on the basis of different machining parameters. The main goal of the study was to analyze the sensitivity of the chip formation during the machining process by using adaptive neuro-fuzzy inference system (ANFIS).

According to the results, the chip formation is the most sensitive to feed rate.

Different cutting tests were performed to monitor the chip formation on the basis of the cutting forces and the cutting displacement. ANFIS was used to estimate the sensitivity of the chip formation during the cutting process on the basis of different parameters.

The purpose of this study is to show which filler metal is the best for hard facing. Because the quality of the surface layer has a great influence on the working life of parts, the purpose was to extend the working life of parts exposed to intensive wear. The tested hard-faced models were made of low carbon steel to save the expensive base metal and to analyze the possibilities of extending the service life of existing structural parts.

Samples were prepared from plates hard faced with various filler metals. Samples were then subjected to experimental testing – testing of tribological properties and hardness and microstructure. Testing was done in conditions similar to real ones – with a sliding speed of 0.25, 0.5 and 1 m/s and with a load of 50, 75 and 100 N and in most rigorous dry conditions. Research was done by using a combination of experimental and theoretical approaches.

The paper shows the results of the experimental testing of four different filler metals aimed for hard facing of parts exposed to highly intensive wear. Results shown that CrWC 600 alloy is the most favorable filler metal for hard facing of parts such as those of construction mechanization and those subjected to intensive abrasive wear at stone mines.

All obtained results are real and fully applicable, as there is a huge industrial need for these types of technologies. With the application of these technologies, beside money savings, the working life of parts can be significantly extended.

The research presented in this paper was conducted because of the lack of results from this area in Serbia and because of the necessity for application of obtained results in companies for road maintenance and stone excavation in the region of Šumadija, Serbia.

Pharmaceutical supply chains (PSCs) need a well-operating and faultless logistics system to successfully store and distribute their medicines. Hospitals, health institutes, and pharmacies must maintain extra stock to respond requirements of the patients. Nevertheless, there is an inverse correlation between the level of medicine stock and logistics service level. The high stock level held by health institutions indicates that we have not sufficiently excellent logistics systems presently. As such, selecting appropriate logistics service providers (drug distributors) is crucial and strategic for PSCs. However, this is difficult for decision-makers, as highly complex situations and conflicting criteria influence such evaluation processes. So, a robust, applicable, and strong methodological frame is required to solve these decision-making problems.

To achieve this challenging issue, the authors develop and apply an integrated entropy-WASPAS methodology with Fermatean fuzzy sets for the first time in the literature. The evaluation process takes place in two stages, as in traditional multi-criteria problems. In the first stage, the importance levels of the criteria are determined by the FF-entropy method. Afterwards, the FF-WASPAS approach ranks the alternatives.

The feasibility of the proposed model is also supported by a case study where six companies are evaluated comprehensively regarding ten criteria. Herewith, total warehouse capacity, number of refrigerated vehicles, and personnel are the top three criteria that significantly influence the evaluation of pharmaceutical distribution and warehousing companies. Further, a comprehensive sensitivity analysis proves the robustness and effectiveness of the proposed approach.

The proposed multi-attribute decision model quantitatively aids managers in selecting logistics service providers considering imprecisions in the multi-criteria decision-making process.

A new model has been developed to present a sound mathematical model for selecting logistics service providers consisting of Fermatean fuzzy entropy and WASPAS methods. The paper's main contribution is presenting a comprehensive and more robust model for the ex ante evaluation and ranking of providers.