Journal of Computer and Communications, 2014, 2, 142-147 Published Online March 2014 in SciRes. http://www.scirp.org/journal/jcc http://dx.doi.org/10.4236/jcc.2014.24019 How to cite this paper: Tongur, V. and Ülker, E. (2014) Migrating Birds Optimization for Flow Shop Sequencing Problem. Journal of Computer and Communications, 2, 142-147. http://dx.doi.org/10.4236/jcc.2014.24019 Migrating Birds Optimization for Flow Shop Sequencing Problem Vahit Tongur 1 , Erkan Ülker 2 1 Computer Engineering Department, Necmettin Erbakan University, Konya, Turkey 2 Computer Engineering Department, Selçuk University, Konya, Turkey Email: vtongur@konya.edu.tr , eulker@selcuk.edu.tr Received November 2013 Abstract FSSP is a typical NP-Hard problem which is desired to be minimum makespan. This study consid- ers Migrating Birds Optimization (MBO) which is metaheuristic approach for the solution of Flow Shop Sequencing Problem (FSSP). As the basic MBO algorithm is designed for discrete problems. The performance of basic MBO algorithm is tested via some FSSP data sets exist in literature. Ob- tained results are compared with optimal results of related data sets. Keywords Migrating Birds Optimization; Flow Shop Sequencing Problem; Metaheuristic Optimization 1. Introduction Flow shop sequencing problem (FSSP) is a production planning problem. Production planning problem in to- day’s manufacturing systems planning process has a very important place. Therefore a lot of research has been done on scheduling problems. Researchers have developed various heuristic approaches because of most of the scheduling problems in NP-hard class. There are close optimum solutions for large scaled integrated optimization problems as an optimum solution. Close optimum solutions are generally found via heuristic algorithms. While some heuristic algorithms start to solution from zero and represent a graded solution, some of them start from a complete solution and try to en- hance the existing solution [1,2]. Most of the metaheuristic algorithms can also be called as neighboring or local search procedures. Metaheuristic algorithms are a common form of enchanging algorithms which find a better solution by looking for neighboring of existing solution in each iteration [2,3]. Neighboring attitude of each me- taheuristic algorithm is different. It can be said that differences of these algorithms, which basically have the same targets, are sourced from their neighboring attitudes. Until now, some metaheuristic algorithms such as genetic algorithm [4,5], tabu search [6,7], ant colony algorithm [8] are solved by FSSP problems. Flow shop sequencing problem minimizing the time between the beginning of perform of the first job on the first machine and the completion of perform of the last job on the last machine. This time is called make span. Assumptions for this problem are as follows. • Every job has to be processed at maximum once on machine.