Optimization Of Production Logistics In Flow Manufacturing Systems

Production logistic is an integration of the company's production and logistics activities. Logistics optimization to reduce production cost is an effective means to enhance the competitiveness of an enterprise. Therefore, production logistics has been recognized as one of the most important issues in manufacturing systems. This dissertation focuses on the production logistic of the flow manufacturing system, studies the issues in production process optimization, layout of production equipment and the routing of material delivery to improve the smoothness of the logistics, reduce the logistic cost, and enhance the production efficiency. In order to enhance the efficiency of the whole production, the theory of constraints, fuzz logic and evolutionary algorithms have been employed to integrate the production processes of products, improve the layout of the equipment and optimize the routing of material distribution respectively.A literature review of the research in the optimization of production logistic was carried out to identify characteristics and challenges. The main tasks of the optimization of production logistic are then defined. Different technologies and methods for the optimization of production logistic are studied and the details are given as follows:1. Based on the Theory of Constraints (TOC), an integrated optimization method for the production processes of flow manufacturing systems has been developed. The integrated optimization of production processes (IOPP) is the process of analyzing, rearranging, optimizing, redesigning and actualizing the production process based on such elements as the work flows, the process flows and the P/Q maps, and so on. It is the adjustment and improvement of the production line. Based on the analysis of the process flow charts, the engineering diagrams and the P/Q maps of the existing products in the flow manufacturing system, the integrated optimization method is proposed to integrate the flow manufacturing process. In addition, through the formation and analysis of the simulation model of the flow manufacturing process, the bottlenecks of the production processes are identified and the improved DBR technique is introduced to solve these problems.2.For the equipment re-layout caused by the products transitions and processes change, a multi-objective optimization model is developed to minimize the total costs of the logistic and the re-layout of equipment and maximize the satisfaction of the neighborhood and distances requirements of the equipments. For the large scale dynamic layout optimization problem, an improved multi-objective evolutionary algorithm is proposed. This algorithm defines the fuzzy fitness function and the evaluation function based on the satisfaction. It also defines the fuzzy optimization objectives such as the satisfaction, and designs the novelty representation of the individuals, the specifically crossover method and the multi-mode mutation to enhance the performance of the algorithm. A real case was used to demonstrate that the proposed algorithm can solve the dynamic layout optimization problem effectively with the satisfaction solutions3.As vehicles utilization in the material distribution system is lower, and the traditional VRP model and routing optimization methods are not suitable for the flow manufacturing system, a new material distribution method that divides the required materials in the workstations into the fixed mode and the split mode has been developed, and the different routing optimization models and solution algorithms have been developed respectively. For the fixed mode, the travel distances of the vehicles, the number of vehicles and the utilization of the vehicles were taken into account. Based on the routing pool, a multi-objective evolutionary algorithm has been proposed. It defines a routing pool through the heuristics method to enhance the use of the good genes, and designs the special representation of the individuals, novel crossover and mutation mode to improve the performance of the algorithm. For the split mode, a routing optimization and vehicle scheduling model was also developed by considering the complex time constraints. A multi-level progressive method was developed to solve this problem.A comprehensive summary and the future research are given in the final chapter of the dissertation.