Minimizing Non-decreasing Objective Functions for the Open Shop Scheduling Problem Using Genetic Algorithm

Subject Areas : Industrial Management

Ghorbanali Mohammadi ¹ (Associate Professor, Industrial Engineering Department, College of Engineering, Qom University of Technology, Qom, Iran)
Taher Daali Matoorian ² (Master of Science, Industrial Engineering Department, College of Industrial Engineering, Islamic Azad University, Khalij Fars International Branch, Khormashahr, Iran)

Received: 2017-03-04 Accepted : 2016-07-18 Published : 2016-08-25

Keywords:

Abstract :

Planning is currently considered as an important element for the whole personal, societal and organizational affairs, whereby every activity would be performed effectively. Scheduling is of high significance in operations research. Recently, scholars have drawn much attention to the application of mathematical modeling as an optimization approach in solving the complex scheduling problems. However, only a few have addressed the open-shop scheduling problem. This study was intended to investigate the effectiveness of meta-heuristic genetic algorithm in order to minimize non-decreasing separable objective functions for such problem. In this genetic algorithm, displacement mutation and partially matched crossover were adopted as two operators. Moreover, the obtained solutions were compared based on how to select the best chromosome by using methods of tournament selection, rank selection, and roulette wheel selection. The data were collected through literature review. It was exhibited that meta-heuristic genetic algorithm can rapidly find the optimal solution. Furthermore, rank selection resulted in more optimal solutions instead of the other two.   

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