Research And Application On Dynamic Resource-Constrained Project Scheduling Problem

Resource-constrained project scheduling problem is an important part of project management, therefore, studying of its theory and implementation method has important practical significance. Combining with the enterprise project demands, an improved adaptive particle swarm optimization was proposed, then mathematical models of dynamic resource-constrained project scheduling problem and dynamic fuzzy resource-constrained project scheduling problem were established and solved, developed project scheduling management system. Finally, the theory and method proposed in this paper was used to solve the actual project scheduling of injection molding machine development, and it shows that the method and technique possess considerable feasibility and validity.This paper includes:Chapter 1 introduced the background and significance of the resource-constrained project scheduling problem, analyzed the existing algorithms and models problem, and presented the research content and the frame of the dissertation.Chapter 2 introduced the overview of particle swarm algorithm and proposed a damping adaptive particle swarm optimization. In the algorithm, in order to strike a balance between global and local searching ability, the damping adaptive inertia weight with cyclical attenuation non-linear change strategy based on improvement of the model of damping motion was put forward; for the premature convergence problem of particle swarm algorithm, an improved mutation strategy based on the average distance of particle swarm was given.Chapter 3 presented the model and scheduling algorithm of the classic resource-constrained project scheduling problem, because fixed allocation of task resource is difficult to achieve dynamic and efficient scheduling, the thought of dynamic resource allocation was proposed, it allows a task to start while the resource is not fully ready and the use of resources can be dynamically adjusted during the scheduling. The definition of resource threshold and calculation of equivalent duration were proposed, established the mathematical model of dynamic resource-constrained project scheduling problem, given the conditions of cutting down the project duration. Extended the strategy of dynamic allocation of resources to the fuzzy resource-constrained project scheduling problem, established the mathematical model of dynamic fuzzy resource-constrained project scheduling problem with fuzzy processing time denoted by six-point fuzzy numbers. Serial scheduling scheme and parallel scheduling scheme were improved to meet the requirements of models.Chapter 4 adopted damping adaptive particle swarm optimization to solve dynamic resource-constrained project scheduling problem and dynamic fuzzy resource-constrained project scheduling problem. A new task list based on bound probability and delimitation rule was proposed for particle encoding, and the mixed strategy based on priority rule and the random number was given to generate initial population, the updating mechanism of particle by fixed position cross method was given to guarantee the feasibility of particle sequence list after every iteration. Analyzed the effect of different coding methods, resource levels and different algorithms by testing of universal test library and a typical example, the result shows that dynamic resource allocation strategy for scheduling and damping adaptive particle swarm optimization are proved to be able to effectively make use of resources and cut down the duration of the project.Chapter 5 developed project scheduling management system, given the architecture and function modules of the system and applied the system to the scheduling process of a specific injection machine project, dynamic resource allocation strategy for scheduling and improvement of the algorithm are proved to be able to effectively make use of resources and cut down the duration of the project through analysis of different scheduling models. The system has been successfully implemented at enterprises.Chapter 6 summarized the research results of this dissertation and the ways for further research were pointed out.