Research On Improved Ant Colony Optimization Algorithm Based Multi-Objective Resource Constrained Project Scheduling Problem

In recent years,with the acceleration of economic globalization,the high-efficiency approaches of the project management have been widely used in many fields such as construction engineering,software development,machinery manufacturing,computer industry,etc.,and these approaches have received extensive attention.The characteristics of real project management have become increasingly complex and large-scale,such as under the premise for meeting the constraints of resources and logics,how to schedule the priority of the activities for the considered projects in the best way which is called the resource constrained project scheduling problem(RCPSP).Therefore,research on the RCPSP has important practical application significance.Intelligent optimization algorithms are a kind of optimization algorithms which can effectively solve the resource constrained project scheduling problem.As a novel and efficient swarm intelligence algorithm,ant colony optimization(ACO)has received extensive attention in recent years,but its application in project scheduling is still not deep enough.In the view above,this paper studies to design and develop an efficient ant colony optimization algorithm to solve the scheduling problem of complex resource-constrained projects.The main works of this study are summarized as follows.Firstly,according to the properties of the RCPSP,an effective improved ant colony optimization(IACO)is proposed for the RCPSP,and the heuristic factor calculation rule is improved to influence the pheromone and the probability of the selection probability.Then the coding result is improved and the performance of the proposed algorithm is enhanced.Secondly,based on the neighborhood structure of Insert and Swap,an improved ant colony optimization(IACO_LS)is presented to balance global exploration and local exploitation.Then the characteristics of RCPSP based forward-backward local search is proposed to improve the performance of the proposed algorithm.Thirdly,for multi-objective RCPSP(MORCPSP-MS-RI)requires simultaneous consideration the characteristics of makespan and resource investment criteria,an efficient pareto solution and update mechanism are designed.IACO_LS is employed to improve the non-dominated solution for the multi-resource constraint project scheduling problem.In addition,considering the possible risks,the buffer is set up to ensure the success of the project.The influence of the parameter settings is discussed based on experimental design method,and the validity and efficiency of the proposed algorithm are verified by a large number of numerical simulations and comparisons based on PSPLIB.Finally,the real case is used to illustrate the practical application of this study,which can greatly reduce the company's time and resource costs,effectively improving the company's economic efficiency and competitiveness.