The Study On The Fuzzy Production Planning Problem With Credibility Service Levels

Production planning is viewed as the plans and arrangements of the production mission and progress in production scheduled time. In real-word production plan-ning problems, the parameters of the production planning problems have fuzziness because of measure errors and data noise. In this thesis, we employ credibility theory to study the fuzzy production planning problem with credibility service levels.First of all, this thesis presents a fuzzy expected value production planning model with credibility service levels. In the case that the fuzzy demands are inde-pendent normal fuzzy variables, the model can be turned into its equivalent one with deterministic constraints. Approximation approach (AA) to the model is discussed and the convergence of the AA is proved. We design a hybrid algorithm which inte-grates AA and particle swarm optimization (PSO) algorithm to solve the proposed model. A numerical example is given to illustrate the effectiveness of the designed hybrid algorithm. Then, we propose a fuzzy minimum-risk production planning model with credibility service levels. When the fuzzy demands are independent gamma fuzzy variables, the credibility constraints of the model can be turned into their deterministic equivalent ones. We design a hybrid algorithm which integrates AA and PSO to solve the proposed model. A numerical example is given to illustrate the effectiveness of the designed hybrid algorithm.The main work of this thesis can be summarized as follows:(1) Two kinds of prodution planning model-fuzzy expected value production planning model with credibility service levels and fuzzy minimum-risk production planning model are built.(2) In the cases that demands are normal or gamma fuzzy variables, the methods of the credibility constraints being turned into their deterministic equivalent ones are proposed.(3) A hybrid algorithm which integrates AA and PSO is designed to solve the proposed models, moreover, a numerical example is given to illustrate the modeling idea and effectiveness of the designed hybrid algorithm.