Mohammad Abbasi - Department of Mechanical Engineering, Faculty of Engineering, Urmia University, Urmia, Iran
Shahram Khalilarya - Department of Mechanical Engineering, Faculty of Engineering, Urmia University, Urmia, Iran
Ali Navid - Department of Mechanical Engineering, Faculty of Vehicle system design, TU Chemnitz, Chemnitz, Germany

This research concerns the economic consequences of the engine optimization. This optimization has been done by a non-evolutionary NLPQL (Non-Linear Programming by Quadratic Lagrangian) and evolutionary Genetic algorithms. In order to have a faircomparison between them, the input variables (R, Di, injection angle and spray cone angle) and the objective functions ISFC (Indicated Specific Fuel Consumption), IMEP (Indicated Mean Effective Pressure), NO, SMD (Sauter Mean Diameter) are the same. The economicanalysis shows that by improving the combustion system not only the engine characteristics upgrades, but also the financial budget saved by this optimization is astounding. It is depicted that the added value to the engine’s horsepower worth is at least 2,340,000 $/yearand the fuel saved by this optimization is at least 12,303,070 $/year. By generalizing this method to all cars produced in the USA in 2015, the added value would be 108,000,000 $/year and the saved fuel worth is at least 503,880,000 $/year. This means that by just oneengine optimization it would be possible to create jobs to at least 818 persons per year and by generalizing this method to all cars, this quantity would increase to 34213 persons per year.

optimization, NLPQL algorithm, Genetic algorithm, economic view, job creation