Research On Robust Resource Constrained Project Scheduling Problem Under Fuzzy Uncertainty

With the globalization of the market economy, competition between enterprises is becoming increasingly fierce, the size of the project becoming increasingly large and the requirements of the project management becoming increasingly high. Plan and control activities, resources, time these three variables effectively is the key to ensure the project success. Project scheduling problems (PSP) as the core content of project management has been extensively studied in recent decades, but its complexity, dynamic randomness and multi-objective attributes have not yet solved by a systematic method or theory. There is a big gap between the theoretical research and practical application of PSP.This paper discusses the resource constrained project scheduling problems (RCPSP) which exists widely in construction, software development, aircraft and ship manufacturing, and other single or small batch production mode of the enterprise. RCPSP is widely used in practice, and the model of it is very rich in theory. Most of RCPSP belongs to NP-hard problem and it's hard to solve. So far, there are many effective algorithms to solve the deterministic RCPSP. The environment of the project is dynamic. There is variety of uncertainties that make the traditional project scheduling model no longer reliable. In order to truly reflect the actual situation of the project, this paper discusses uncertain resource constrained project scheduling problems using fuzzy numbers to describe uncertain durations of activities as well as the due date of project.The main efforts of this paper are as follows:(1) A robust project scheduling model is created by maximizing clients' satisfaction degree and minimizing the uncertainty of the completion time of project based on the fuzzy set theory and fuzzy number addition, comparison operations. This model embodies an anti-risk awareness and can minimize the uncertainty of the fuzzy completion time of the project to provide reliable data for the decision makers. (2) The process of human and other higher vertebrates'immune system produces antibodies using clonal selection mechanism is essentially a Darwinian selection and mutation process. If the antigen, antibody, and the robust project scheduling values were seen as the problem to be optimize, a feasible solution, the feasible solution fitness separately, the process of the immune system produces antibodies is an optimization process. This paper proposed a modified immune algorithm (MAIA) based on this assumption to solve the RCPSP in fuzzy environment. The algorithm includes immune selection and mutation operations which are used to prevent the degradation of the population. The selection mechanism based on density of antibodies and the immune gene pool mechanism can accelerate the generation of antibodies with high fitness while reducing the generation of antibodies with high density. An extensive experiment was conducted and the computational results show that the algorithm is effective for the proposed problem compared with other algorithms.(3) No matter how strong the robustness of the project schedule is, in the actual project implementation, the project will be subject to various uncertainties disturbances. The impact of these disturbances on the project can not be determined in advance. So when the impact of the disturbances on the project has accumulated to a certain extent, the reschedule problems of the project occur. This paper discussed the project rescheduling architecture in four areas, and created a robust rescheduling model in fuzzy environment by considering the effectiveness and the stability of the reschedule. A specific instance of this model was solved by the proposed MAIA.