Intelligent Scheduling Of YC PDP Pump R&D Project Based On RCPSP

Project scheduling is an important process in project management and project problem solving.Resource Constrained Project Scheduling Problem(RCPSP)provides an important modeling solution.It aims to arrange the durations of project activities under the constraints of activity dependence and limited resource to achieve the optimized project outcome.The artificial intelligence based method has become the research focus of a lot researchers and practitioners.It has the advantage of not assuming the relevance of problem and algorithm.Artificial intelligence based method can also adopt various model improvement strategies for application need.The integration of RCPSP and artificial intelligence algorithms is a core component of the project scheduling decision support system.From the perspective of system management and decision theory,this study identifies the problems in the current practice of the PDP pump R&D project considering the situation of mutual control and constraints between the various layers of the organizing system.Using the method of scope management and on-site management,the characteristics and difficulties of our target problem are analyzed,and the applicable scope of problem solving is verified.After that,the RCPSP is designed as a decision-supporting method to solve the on-site real time project problems.In the designing process,the WBS of project is first reviewed before the RCPSP model for on-site scheduling was derived.Compared to traditional RCPSP model considering only single layer time granularity,the proposed RCPSP can model “dual” scheduling process by allowing hourly level and day level scheduling.The overrun of time and overtime scenarios are also considered in the proposed achieve a reasonable progress acceleration.On the base of above proposed model,the new artificial intelligence algorithm BFO(bacterial foraging optimization algorithm)is used to solve the scheduling problem.DPCBFO is proposed by improving the position update equation,disturbance mode and algorithm structure of the BFO algorithm.Then simulation experiments are used to verify the effectiveness of the algorithm compared to benchmark algorithms.In the last stage,by designing the coding,adaptive value function,and calculation process,the DPCBFO is combined with PSGS(parallel schedule generation mechanism)and applied to the problem scenario of the case project.Results demonstrate its effectiveness in solving the job interruption and slow task execution caused by lack of management support.The potential improvements are then summarized.This study provide important implications on solving the on-site scheduling problem of project-oriented organizations.