Optimization of the Multi-Objective Flexible Job Shop Scheduling model by applying NSGAII and NRGA Algorithms

Scheduling is one of the key parameters to maintain competitive advantage of organizations, and can directly affect productivity, reduce production time and increase the profitability of an organization. Job shop scheduling problem (JSSP) seeks to find the optimal sequence of performing various jobs related to group of machines. The purpose of this paper is to provide a multi objective to optimize makespan, energy consumption and machine erosion in flexible JSSP. The problem of this paper is to assign each operation to a machine and to order the operations on the machines, such that the maximal completion time (makespan) of all operations is minimized. The obtained model belongs to NP-Hard class of optimization problems. In terms of overcoming NP-hardness of the proposed model and solve the complicated problem, a non-dominated sorting genetic algorithm (NSGAII) is employed. As there is no benchmark available in the literature, the non-dominated ranking genetic algorithm (NRGA) is developed to validate the results obtained and test problems are provided to show the applicability of the proposed methodology and evaluate the performance of the algorithms. In this study, to evaluate the performance of these algorithms, they were statistically analyzed using T-test. Ultimately, results of the selected model were ranked by applying the technique for order of preference by similarity to ideal solution (TOPSIS).