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

作　　者：王海鑫 王祖和[1] 温国锋[2] 李海霞[2] 

机构地区：[1]山东科技大学经济管理学院,山东青岛266510 [2]山东工商学院管理科学与工程学院,山东烟台264000

出　　处：《管理工程学报》2017年第4期220-225,共6页Journal of Industrial Engineering and Engineering Management

基　　金：教育部人文社会科学研究规划基金资助项目(13YJA630100)

摘　　要：研究资源约束条件下的项目调度问题,并将研究对象扩展到多项目环境,在多项目优先级评价基础上以多项目加权工期最小化为目标,构建多项目进度计划模型。通过在资源限定条件下,对共享资源的多个并行项目进行合理调度安排,为项目管理者在资源限制条件下合理配置资源以满足各项目的工期要求并尽量缩短多项目加权总工期提供决策依据。针对标准粒子群容易早熟从而影响优化结果的问题,采用一种具有动态变惯性权重的自适应粒子群算法(DCWPSO)对模型进行求解。采用标准测试函数和具体算例进行检验,结果表明DCWPSO算法可以较好地解决RCMPSP问题。With the continuous development of social economy, the scale of modern 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 proble

关 键 词：粒子群算法 惯性权重 自适应 多项目调度 

分 类 号：F424.6[经济管理—产业经济]