Study On Optimal Deployment Of Construction Equipment In Earthwork

Deployment of construction equipment is considered as a highly important content to constructional organization design, especially in the big-scale project. The optimal deployment of Construction Equipment not only ensures the project finishes on time and Saves the cost, but also Brings many benefits for the builder during the construction. Therefore, it is a Significant topic about optimal deployment of construction equipment, because research to it not only has very good theory significance, but also has very big usability.The ressarch of this problem in the early stage is mainly based on methods such as queuing theory, expert system, computer simulating, linear programming, etc., and has made some achievements. This paper chooses the topic on optimal deployment of construction equipment in earthwork as the research object, which is basis on the forefathers' study. A algorithms named particle swarm optimization is adopted to solve the deployment model.Particle swarm optimization algorithm is a new optimization approach that developed recently. Compared with other algorithms, the particle swarm optimization algorithm has a quicker rate of convergence, higher accuracy and the better global convergence ability. Furthermore it has little requirement to the goal questions. First of all, regard mechanical type and quantity as the independent variable in this algorithm, secondly, regard the time-cost goal which is based on dynamic weight coefficient as the evaluation function, finally, through of verify the feasibility of the algorithm by recalculating an example which is gaven in another paper.The concrete research work and achievement in this paper are as follows: Firstly, construct the loading system model on the basis of analyzing the characteristic of loading system in the earthwork, which used in earthwork procedure simulation using queueing theory, then through the operation of the model can receive index of each scheme such as total time, total cost,etc.Secondly, construct deployment model by utilizing multi-objective optimization and take time-cost goal as its evaluateion function, then, introduce dynamic weight coefficient in the model, which is able to distribute the weight coefficient of the goal at random.Thirdly, utilize particle swarm optimization algorithm to solve the model, and the result reveals this algorithm not only is effective, but also can offer many kinds of scheme.