Dynamic flexible job shop scheduling with simulation optimization by using genetic algorithm

ABSTRACTDynamic Flexible Job Shop Scheduling with Simulation Optimization by Using Genetic AlgorithmDOSDOĞRU, Ayşe TuğbaM. Sc. in Industrial Eng.Supervisor: Assist. Prof. Dr. Faruk GEYİKJuly 2012, 97 pagesIn most real life manufacturing problems, certain operation of a part can be processed on more than one machine which makes the considered system (i.e. job shops) flexible. On one hand, flexibility provides alternative part routings which most of the time relaxes shop floor operations. On the other hand, increased flexibility makes operation machine pairing decisions (i.e., the most suitable part routing) much more complex. Thus, manufacturing systems must be scheduled by considering the flexibility to improve effectiveness and performance.The aim of the study is to develop a system that generates the best feasible part routings in a dynamic flexible job shop scheduling environment. For this purpose both the best feasible process plan for each part and the best feasible machine for each operation in a dynamic flexible job shop scheduling environment must be determined, respectively. In this respect, a genetic algorithm is adapted to determine best part processing plan for each part and then select appropriate machines for each operation of each part according to the determined part processing plan. Genetic algorithm solves to the optimization phase of solution methodology. Then these machine-operation pairings are utilized by discrete-event system simulation model to estimate their performances. These two phases of the study follow each other iteratively. The goal of the proposed methodology is to find the solution that minimizes total of average flow times for all parts. The results show that the objective function improves as the considered level of flexibility increases.Keywords: Flexible job shop scheduling, genetic algorithm, simulation optimization.