Research On Optimal Production And Inventory Control Policies Based On Fuzzy Control

Along with the improvement of science, technology, management and the gradually deepening of economic globalization, enterprises all over the world face increasingly great competitive pressure. For more profit, even survival, all enterprises have to make great efforts to sharpen their competitive edge in different aspects.Production-inventory control plays a very important role in the management of a manufacturing system. Efficient production-inventory control system will reduce inventory cost, production cycle time, improve service level for customers, and find many incubating problems. Research work on production-inventory control theory have never intermitted in the past decades. Experts in this field have a common recognition that there is still much work need to do in this field. All these show that research on production-inventory control attaches important significance.Fuzzy control is with unique advantage in controlling complex nonlinear system. It is independent of system's mathematic models, with strong robustness and competent for all kinds of nonlinear control problems that make so much trouble to traditional control methods. Production- inventory system is a typical complex nonlinear system with lots of fuzzy information sometimes and accurate theory and method can't treat with these information. So to control a production-inventory system with fuzzy control is with great importance.In this dissertation, based on the review of production-inventory control theory and fuzzy control theory, fuzzy control, simulation and genetic algorithm (GA) are used to find the optimal policies of admission control and quantity-of-order-placement control (QOPC) in serial line and assembly line, respectively.In the admission control system of serial line, the production line is viewed as a queueing network. A fuzzy controller is designed for the admission control of raw material. An illustrative example is studied to compare fuzzy control system to constant work-in-process (CONWIP) system. The results show that fuzzy control system is not worse than CONWIP.In the admission control system of assembly line, the production line is viewed as a queueing network, too. Fuzzy controllers are designed to control the parts that are ready to enter the stages of the assembly line. A GA is designed to optimize the parameters of fuzzy controllers. The fuzzy control system is compared with a multi-stage CONWIP and a push system in an illustrative example. The results show that the fuzzy control system is with better performance.In the QOPC of serial line, a periodic review model based on fuzzy control is built. Fuzzy controllers are design to control the quantity of order that will be placed into eachnode (station). A greedy algorithm is designed to solve the trade-off between inventory cost and customer's satisfied rate (service level). Then an improved GA is designed to solve a multi-objective program model with service level constraint, whose objective is to minimize work-in-process (WIP) level and fluctuation level of order placement. In the numeral examples the fuzzy control system is compared to CONWIP, Kanban and Generic Pull system. The results show that the fuzzy control system maintains not only a lower WIP level, but also a lower fluctuation level of order placement. Finally, a simple but effective method is proposed to solve the optimization problem that will determine the controller's quantity and location, via deleting the number of controller's input and the number of fuzzy controller.In the QOPC of assembly line, a periodic review model based on fuzzy control is built, too. Fuzzy controllers are design to control the quantity of order that will be placed into each stage. An improved GA is designed to solve a multi-objective program model with service level constraint, whose objective is to minimize WIP level and fluctuation level of order placement. In the illustrative examples the fuzzy control system is compared to CONWIP, Kanban and Generic Pull system, too. And an identical conclusion like in the serial QOPC is drawn.Finally a summary conclusion of this dissertation is drawn and some suggestions are given to future research.