Efficient structures of achievement functions for goal programming models

Following the ideas of Li, Romero and Chang, this paper proposes several efficient structures of achievement functions for goal programming and interval goal programming models. These proposed structures of achievement functions are more efficient than the traditional structures of achievement functions such as Lexicographic, Weight, MINMAX (Chebyshev) and interval achievement functions. The structures of achievement functions are comprehensive in terms of the breadth of goal programming and interval goal programming. In order to demonstrate the superiority of the proposed methods in terms of the number of iterations and execution time, a computational experiment was conducted on a set of generated test examples.