Research And Application Of Resource Optimization Methods Of Project Management In ETO Mechanical Manufacturing Enterprises

As a unique field in management, project management has penetrated into every industry and become an effective management method to promote the rapid development of the manufacturing industry in developed countries. In order to obtain an advantageous position in competitive market, enterprises can not only rely on the update of equipment and techniques but also on the improvement of management level. Therefore the research on theoretical methods and their applications of multi-project management has become a hot topic. To the mechanical manufacturing enterprises which are classified as ETO (Engineer-to-order) type, they are typical project-oriented ones that are confronted with the problem of how to complete multiple projects simultaneously in the situation of limited resources, and the practice of the scientific project management mode is an inexorable trend in the development process of industry. To some extent, project management is a process of resource management, allocation and scheduling, therefore resource optimization scheduling m multi-project management is one of the key issues. The enterprise can save its resources, improve its service level and enhance its market competition by the application of the advanced project management principles and methods. In combination with the practice of ETO mechanical manufacturing enterprises in China, this dissertation studies the resource optimization method in multi-project management and puts forward some new ideas and methods. The main contents and results are as follows:(1) Analyze the three basic lines of product management, project management and resource management, according to the project management theory and the production characteristics of ETO mechanical manufacturing enterprises. Illustrate the necessity for the practice of project management mode in view of the problems which exist in project management fields in Chinese enterprises. Clear the objective of resource optimization in enterprise multi-project management and the main contents, combining with the features of enterprise resources.(2) Put forward multi-project priority evaluation index system to ETO mechanical manufacturing enterprises. Define evaluation index of the multi-project priorities, based on the analysis of the main factors including clients, finance and products, which affect priority of multi-projects, and put forward the reference method of quantitative indicators score. Determine the index weight by the use of AHP method, and establish multi-project priority assessment model based on GRA method. Verify the feasibility of the index system and method with an enterprise case.(3) Establish a mathematical model which optimizes schedule for the issue of resource-constrained multi-project by using minimum of the weighted total time of the project as a target. Propose improved methods for the lack of basic topological sort algorithm based on cellular automaton at the same time. Construct cellular automata model based on project networks by setting up multiple virtual nodes and consolidating several project networks to meet the task of logical constraints and resource conditions as the evolution of the rules. Look for the nodes waiting to be activated from the initial virtual node and propose two kinds of corresponding sorting order for the conditions when there are several nodes to be activated at the same time. Obtain the resource optimization scheduling program for multi-project schedule optimization through multiple rounds of comparisons and verify the validity of the algorithm through an enterprise case.(4) Establish the corresponding mathematical model and propose the multi-Agent particle swarm optimization for solving multi-project resource >ptimization scheduling problem which varies with the increase or decrease of the number of resource acquisition. In order to amend the deficiency which comes from the lack of consultation and collaboration among the particles in basic particle swarm optimization algorithm which leads to the slow rate of convergence, the new algorithm introduces the concept of Agent into the particle structures and makes use of Agent Self-learning of how to operate the local environment and gets the updated value of spatial location. Put forward the thoughts of Game Theory Particle Position Rounding-off (GTPPR), and finally obtain the global optimal scheduling scheme by continuous updates and verify the validity of the algorithm through an example.(5) Propose a sort of resource concentration share tactic based on the traditional resource separate allocation tactic for the combination with the features of dynamic multi-project management resource allocation and set up corresponding simulation models. Taking one assembly subsystem of an ETO mechanical manufacturing enterprise as the research objective, the simulation test results show that the higher the peak level of the system is, the more obvious improvement of the system service efficiency of the resource concentration share tactic is, compared with the traditional resource separate allocation tactic.