Nanomaterials are a new group that has quickly found a wide range of applications in medicine, cosmetology, the food, weapons or automotive industry. They are also used as a fuel additive. This paper reviews the literature and assesses the current state of knowledge regarding the use of nanoparticles in automotive engine fuels. The results obtained so far are presented and further research directions in this field are identified Conclusion: The results of the review showed a discrepancy, selected groups favor the reduction of harmful gas emissions, while others do not and even increase emissions, e.g. the use of carbon nanotubes contributes to the increase in the emission of environmentally harmful nitrogen oxides, while the presence of graphene oxide reduces it. An interesting observation is also the fact that groups such as titanium and graphene oxide reduce the emission of harmful carbon monoxide by improving fuel combustion from semi-combustion to complete combustion, but at the same time increase CO2 emissions, which in turn is a greenhouse gas The whole group of nanomaterials contributes to the reduction of hydrocarbon emissions Nanomaterials improve the quality of fuel combustion The review shows tests only on diesel and a mixture with biodiesel in the review there were no studies for gasoline

The travelling salesman problem (TSP) is one of combinatorial optimization problems of huge importance to practical applications. However, the TSP in its “pure” form may lack some essential issues for a decision maker—e.g., time-dependent travelling conditions. Among those shortcomings, there is also a lack of possibility of not visiting some nodes in the network—e.g., thanks to the existence of some more cost-efficient means of transportation. In this article, an extension of the TSP in which some nodes can be skipped at the cost of penalties for skipping those nodes is presented under a new name and in a new mathematical formulation. Such an extension can be applied as a model for transportation cost reduction due to the possibility of outsourcing deliveries to some nodes in a TSP route. An integer linear programming formulation of such a problem based on the Gavish–Graves-flow-based TSP formulation is introduced. This formulation makes it possible to solve the considered problem by using any integer linear programming optimization software. Numerical examples and opportunities for further research are presented.

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