Representation And Computation Of Multiple Construction Programs For Complex Construction Projects

There are often a number of feasible construction sequence in large engineering projects,while the traditional project scheduling tools lack the ability to model alternative construction programs.Utilizing the representation schema of the complex time relationships and logical relationships proposed in previous research,multiple alternative sequences among construction works can be abstracted,which have enhanced the ability to express the schedule of complex construction projects.However,there is no efficient algorithm for solving large-scale schedule with the complex time relationships and logical relationships.In detail,the exact algorithm for such complex schedules is only applicable to small and medium-sized schedules;and the meta-heuristic algorithms has better solving efficiency in solving large-scale problems,but such a meta-heuristic algorithm should be specifically accustomed and optimized for complex construction project scheduling problems.In this regard,this paper uses Boolean variables to represent the logical variables.Based on the developed conversion framework,the original model of complex construction schedule can be transformed into a mixed integer linear(MILP)programming model.The methodology of the Interpretative Structural Modelling(ISM)is utilized to identify the conduction path among dozens of Boolean variables existing in the MILP model,and then the computation priority order between the Boolean variables is determined,which can used to improve the efficiency of the branch and bound algorithm and the genetic algorithm for solving the scheduling problem.As for the improved branch and bound method,the concept of logical branch is developed in order to shorten the path connecting logical variables,and therefore the number of calls of the linear programming solver is accordingly reduced.On the other hand,the coding of chromosome of the improved genetic algorithm also considers the computation priority order between the Boolean variables.Meanwhile,the mechanism for detecting and resolving restriction conflicts arising from the iteration of the genetic algorithm.Finally,the paper verifies the correctness and practicability of the developed algorithms through a case study of a balanced cantilever bridge.In addition,a large-scale scheduling problem is specifically designed for comparing the efficiency of the improved genetic algorithm with the traditional genetic algorithm.The results show that the computation efficiency of the improved genetic algorithm is significantly improved in comparison with the traditional genetic algorithm.