基于蚁群算法的资源均衡优化决策及其MATLAB实现

Resource Leveling Based on Ant Colony Algorithm and MATLAB Optimization Decision

资源均衡问题已被证明属于组合优化中的NP-hard问题,随着网络计划的复杂化,传统的数学规划法和启发式算法已很难解决该问题。本文以各种资源标准差的加权之和作为衡量资源均衡的评价指标,建立了资源均衡优化决策的数学模型,其次,自行设计蚁群算法步骤,利用Matlab编程进行实现,将蚂蚁随机分布在可行域中,蚂蚁根据转移概率进行全局搜索或局部搜索,经迭代求解资源平衡的全局最优和对应的各工序的开始工作时间,最后使用单资源均衡和多资源均衡两个算例对算法进行了测试,验证了该算法的有效性。

The resource leveling problem has been shown to belong to NP-hard problem in combinatorial optimization. As the network becomes complex,it is difficult to solve the problem with traditional mathematical programming and the heuristic algorithms. The sum of standard deviation of resources is used as the measure of resource leveling,and a mathematical model of resource leveling optimization decisions is built. Secondly,using Matlab,design steps of ant colony algorithm are realized,and using ant colony algorithm,the scope of the non-critical process start work time determined,the ants randomly distributed in the feasible region. Then the ants will conduct global search and local search according to the transition probability,to solve the global optimization of resource leveling. Finally,a single resource leveling and multi-resource leveling are taken for examples,to verify the effectiveness of ant colony algorithm. The convergence speed of the algorithm is fast,and it overcomes the defects of traditional algorithm of easily falling into local optimum,and it is suitable for solving the problems of all resources leveling,with good generality. But the optimization model of this paper assumes that any activity must be in continuous construction,and in between all kinds of resources are independent of each other. It is often not consistent with actual situation,and future research can be further optimized,and the ant colony algorithm can be combined with other algorithms to solve the problem.