Research On Stochastic Discrete Time-Cost Optimization Algorithm Of Construction Project

Construction industry is the pillar industry of China’s national economy.Since the 21 st century,along with the rapid economical development and technological progress,the construction projects are becoming larger and more complex.Therefore,good project management is related to the economic benefits of enterprises.As an important part of construction project planning and management,how to realize the trade-off and optimization of construction period and cost is the focus of construction project management.The time-cost optimization problem can be divided into deterministic problem and stochastic problem according to whether the duration and cost of its process are deterministic constants.In real life,the environment of the construction project is easily affected by uncertain factors such as the fluctuation of production efficiency and the change of construction site conditions.Therefore,the cost and duration of the process in the project under a given execution mode are more suitable to be described as random variables.Considering that in engineering practice,the resource allocation of each process in the construction project often presents the characteristics of discretization,and its implementation mode can only be selected from a limited number of execution modes.Therefore,this research studies the stochastic discrete time-cost optimization problem,and improves the computational efficiency of the existing optimization algorithms.Firstly,the calculation method of construction duration and cost is introduced.By selecting the execution mode of process as independent variable,converts the construction period goal into constraint conditions and introducing probability description,a general model of stochastic discrete time-cost optimization problem is established.Secondly,the proposed optimization algorithm which has a double-layer loop algorithm framework is proposed.Aiming at outer cycle,the advantages and disadvantages of existing optimization algorithms are analyzed and summarized,and the genetic algorithm which is more suitable for solving discrete problems is adopted;For inner cycle,Monte Carlo simulation algorithm is used to estimate the completion probability on schedule because of the strong universality.In order to improve the computational efficiency of the algorithm,an efficient and dynamic allocation strategy of computational resources is proposed based on the probability characteristics of the estimated completion probability on schedule(using Monte Carlo simulation).For the generation of initial population in outer circulation,Markov Chain Monte Carlo method is used instead of the random generation method.Subsequently,a decision support system based on the recommended optimization algorithm is designed.According to the sorting out of information requirements,the construction of Access database is introduced.Functional modules for the estimation of project completion probability and cost and time-cost trade-off optimization are designed.Using App Designer to interface the design and layout of different modules,and embedding the recommended optimization algorithm to achieve the function.Finally,in order to verify the performance of the proposed optimization algorithm,two cases are used to verify the feasibility of the proposed optimization algorithm solution.Comparing the results by proposed algorithm with those of the existing optimization algorithm to verify the efficiency improvement of the proposed optimization algorithm.On the premise of similar accuracy and stability,the calculation efficiency of the proposed optimization algorithm is significantly improved.To test the function of decision support system,a large-scale case is used to validate the functions of different modules of the system.The result shows that the system can effectively solve the stochastic discrete time-cost optimization problem in construction project,and it is feasible and practical.