管理工程学报2017，Vol.31Issue(4)：220-225,6.DOI:10.13587/j.cnki.jieem.2017.04.028
自适应粒子群算法求解资源受限多项目调度问题
Resource constrained multi-project scheduling based on adaptive particle swarm optimization algorithm
摘要
Abstract
With the continuons development of social economy,the scale of modem enterprises is expanding,and the scope of business is becoming more and more diversified,the traditional single project management mode has been not suitable for the development requirements of modern enterprises,so the need of multi-project management theory in the practice of multi project management become more and more urgent.Due to the need to deal with a number of constraints at the same time,the optimal allocation of project resources in a single project environment has been a NP-hard problem.In the multi-project environment,such problems are further complicated.In this paper,we study the problem of resource constrained project scheduling,extend the research object to multi-project environment,and construct multi-project schedule model aiming at the minimization of multi-project weighted duration on the base of multi-project priority evaluation.Under the condition of limited resources,we can provide the decision basis for the project manager to allocate resources rationally by making reasonable scheduling of the multiple parallel projects.In order to solve the problem of the standard particle swarm optimization algorithm is easy to premature which can affect the optimization results,this paper will adopt an adaptive particle swarm optimization algorithm with dynamic inertia weight.This algorithm will improve the seeking ability and effect of particle swarm optimization algorithm through the dynamic change of inertia weight to solve the resource constrained multi-project scheduling problem model more effectively.The test results of standard test functions show that the final fitness values obtained by the particle swarm optimization algorithm with adaptive variable weights strategy are the smallest,and the convergence speed has some advantages.To further verify the effectiveness of the DCWPSO algorithm used in this paper for solving the resource constrained multi-project scheduling problem,we can solve the same problem model by using the GA algorithm and basic PSO algorithm.Through the analysis of the calculated results and the computational performance,the results show that the DCWPSO algorithm has an advantage in solving the resource constrained multi-project scheduling problem model.Among them,in terms of the time spent on the algorithm,DCWPSO algorithm with variable inertia weight can search for the optimal scheduling scheme for the first time in a shorter time,so the seeking speed of DCWPSO algorithm is faster.In the algorithm iteration,the change of the objective function value is very large,and the slope of the curve shows that the convergence rate of the algorithm is higher.Thus,compared to the other two algorithms,the DCWPSO algorithm is more stable and effective in solving the resource constrained multi-project scheduling problem model,then it can guide the multi-project optimization scheduling effectively,so it provides a reference for the task scheduling and resource allocation of the project implementation process.关键词
粒子群算法/惯性权重/自适应/多项目调度Key words
Particle swarm optimization/Inertia weight/Adaptability/Multi-project scheduling分类
管理科学引用本文复制引用
王海鑫,王祖和,温国锋,李海霞..自适应粒子群算法求解资源受限多项目调度问题[J].管理工程学报,2017,31(4):220-225,6.基金项目
教育部人文社会科学研究规划基金资助项目(13YJA630100) （13YJA630100）
