Presenting a model for solving lot-streaming hybrid flow shop scheduling problem by considering independent setup time and transportation time

Lot streaming, which has much attention in recent years, is an effective technique to increase production efficiency in a production system by splitting a job into several smaller parts in a multi-stage production system. But important assumptions that exist in the real-world scheduling environment are always ignored. Hence, in this paper, these assumptions are discussed and the results are reviewed. In this paper, the aim is solving a multi objective mathematical model for solving hybrid flow shop scheduling problem with lot-streaming, setup time and transportation time.

At first, a multi objective mathematical programming model is presented for solving the problem. Then, by wighting method, the multi objective model convert to single objective model and GAMS software is used to solve the small size problems to show the performance of the mathematical mothel. Inspired by previous studies, two multi objective metaheuristic algorithms based on the genetic algorithm is used to solve the large-scale problems. To illustrate the performance of the proposed metaheuristic algorithms, the obtained results of the algorithms compared with GAMS outputs in single mode.

To validate the proposed model, a sample is solved using GAMS software and compared with the genetic algorithm. The obtained results show the performance of the mathematical model. Then, two proposed algorithms are used to solve the large-scale problems. For this purpose, 30 instance problems are randomly generated and six indicators are used to compare the algorithms. After performing the experiments and comparing the algorithms with each other, the results show NRGA algorithm performs bether than NSGA-II.

Originality/Value:

 In this paper, for solving a multi objective hybrid flow shop scheduling problem with lot-streamingm mathematical model with the aim of minimizing the makespan and total tardiness, the sequence-dependent setup time and the transportation time constraints between consecutive stages are considered. Since the problem is NP-hard, NSGA-II and NRGA algorithms were used to solve the proposed problem.