The Proactive Scheduling Research For Resource-constrained Project Scheduling Problem With Activity Splitting Under Uncertainty Environment

In the study of RCPSP, those literatures which considering to use proactive scheduling in project scheduling plan, activities splitting and the uncertainty environment at the same time are few. So, on the basis of reading those research, determine the RCPSP with activity splitting as the research object, to improve the robustness of project scheduling as the target, comprehensive use of fuzzy random theory, proactive scheduling method and optimization strategy, software programming and case analysis to study the robustness of RCPSP under fuzzy random environment.Firstly, the research objective is RCPSP with activity splitting, and using fuzzy random variables to describe the uncertainty of each activity’s duration and each resource unit. The RCPSP model with fuzzy random variables is established which it’s objective function is to achieve the maximum of free buffer in the project scheduling target by using of "Highest cumulative instability weight first" and "resource slack". The experimental results verify those proactive scheduling strategies can effectively increase the robustness of RCPSP, meanwhile can increase the project total complete time. So it requires the project managers in making decisions is to weigh the scheduling scheme robust and project completion time. Secondly, this paper establishes a two-stage model which contain fuzzy random variables about RCPSP with activity splitting under uncertainly environment. The first stage is the shortest total project duration as the goal of project activity splitting model; the second stage is maximize the amount of free buffer in the project scheduling. After use local search particle swarm algorithm to split those activities in the project, though a case verify those proactive scheduling strategies can effectively increase the robustness of RCPSP, meanwhile maybe increase the project total complete time. Thirdly, RCPSP with activity splitting which considers the cost of activity splitting under uncertainty environment is studied in this paper. A fuzzy random model which includes the cost of activity splitting and project scheduling duration is built in the first step, and by using local search particle swarm algorithm to split those activities in the project. Then with the goal of maximizing the total free buffering capacity in the scheduling scheme, and to verify the effectiveness of the above mentioned two proactive scheduling strategy can increase the scheduling robustness.Whether "Highest cumulative instability weight first" and "resource slack" can improve the robustness of RCPSP with activity splitting under fuzzy uncertainty environment or not were studied in this paper. Though a case verify the effectiveness of the above mentioned proactive scheduling strategies increase the scheduling robustness and play a certain positive roe to eliminate the influence of uncertainty.