求解资源受限多项目调度的改进遗传规划算法

针对优先级规则调度不具备优化能力的缺陷,提出了一种应用于资源受限多项目调度的改进超启发式遗传规划算法以进化出更理想的优先级规则。通过分析现有优先级规则构建出适用多项目调度的归一化属性集和顶层判别编码方式,并结合NSGA-Ⅱ虚拟适应度分配方法对种群进行评估以实现多目标优化。设计了一种多样性种群更新方式,以避免传统遗传规划易陷入局部最优的缺陷和提高搜索能力。通过基于标准数据集PSPLIB所构造的算例和飞机总装装配线的生产实例验证了该方法的有效性和可行性。

基于资源共享的延迟惩罚最小化的多项目调度优化

对可并行进行的多项目调度问题进行定性描述,在共享资源的数量约束下,构建了以多项目延迟惩罚总和为目标函数的优化模型。鉴于问题的强NP-hard属性,为模型设计了迭代循环求解的启发式遗传算法。运用实际案例对研究进行验证,分析关键参数对合同双方项目收益的影响,得到如下结论:与实际进度安排相比,满意进度安排下承包商因延期完工而遭到来自于业主的罚款数额出现了明显的下降;业主的单位延迟罚款费用、共享资源池中的资源数量也会影响承包商对项目的进度安排,并对双方的项目收益产生重要影响。

Solving Resource-constrained Multiple Project Scheduling Problem Using Timed Colored Petri Nets

To solve the resource-constrained multiple project scheduling problem(RCMPSP) more effectively,a method based on timed colored Petri net(TCPN) was proposed.In this methodology,firstly a novel mapping mechanism between traditional network diagram such as CPM(critical path method)/PERT(program evaluation and review technique) and TCPN was presented.Then a primary TCPN(PTCPN) for solving RCMPSP was modeled based on the proposed mapping mechanism.Meanwhile,the object PTCPN was used to simulate the multiple projects scheduling and to find the approximately optimal value of RCMPSP.Finally,the performance of the proposed approach for solving RCMPSP was validated by executing a mould manufacturing example.