Modern manufacturing process requires to increase productivity, sustainability and quality at the same time. However, the simultaneous increase of these three parameters is very difficult as because of their tendency to increase is inversely proportional. Thus the optimization of these three parameters plays a vital role towards the development of any modern manufacturing industry. At the initial stage researchers have implemented trial and error method for the optimization of various parameters of a manufacturing process. However the use of this technique is costly as well as time consuming. On the other hand the use of scoring techniques is limited for the manufacturing problems with small discrete alternatives. Due to which researchers have given more stress on the development of various optimization techniques for optimizing manufacturing parameters. The optimization techniques can be classified into two types: (i) Deterministic technique and (ii) Stochastic technique. The deterministic techniques enable the researchers to predict exact optimal solution. Several deterministic techniques namely geometric programming, linear programming, nonlinear programming, dynamic programming and quadratic programming were used by the previous researchers. But, these techniques become least popular while solving manufacturing problems with non-convex search spaces and mixed discrete-continuous variables. This is due to their high computational cost. At the later stage, researches implemented stochastic techniques to solve such complex manufacturing problems. Unlike deterministic techniques these techniques approximated the optimal solution. While the faster convergence speed of these algorithms make them popular in the field of manufacturing.