Genetic algorithm based production planning for alternative process production

Production planning under flexible job shop environment is studied. A mathematic model is formulated to help improve alternative process production. This model, in which genetic algorithm is used, is expected to result in better production planning, hence towards the aim of minimizing production cost under the constraints of delivery time and other scheduling conditions. By means of this algorithm, all planning schemes which could meet all requirements of the constraints within the whole solution space are exhaustively searched so as to find the optimal one. Also, a case study is given in the end to support and validate this model. Our results show that genetic algorithm is capable of locating feasible process routes to reduce production cost for certain tasks. Copyright Protection.