Solving multi-criteria hybrid flowshop scheduling problem with metaheuristic approach and an application in a textile company

Companies can provide savings in terms of cost, manpower or production times, increase customer satisfaction and gain more profitability by raising their scheduling abilities and making all the processes more efficient. Most of the optimization and scheduling problems are identified in NP-hard classification because of the many numbers of activities, manpower and input issues and precedence constraints of the activities. Most of the time, it needs more than exact solution methods to reach optimal solutions for that kind of problems. Hybrid flow shop scheduling problems contains two or more flow shop production stages which have more then one identical and parallel machines. Many orginizations have hybrid flow lines such as; electronics production, sector of medicine, industry of computers, paper production, sector of cosmetics, area of textile and in the production process of many products that most of us use in our daily lifes. In this thesis, a hybrid flow shop scheduling problem of a textile organization is solved by NeuroGenetic algorithm, which consists of genetic algorithm and artificial neural networks. In this hybrid method, genetic algorithm which is successful in global optimal search and artificial neural network which is successful in local optimal search are used interchangeably. These two methods are combined for the purpose of having improvements in terms of production planning and scheduling. The results obtained from the algorithm are compared with the real data of the organization and the efficacy of the algorithm is interpretted.