甲供资源约束条件下南水北调工程项目群工期-费用优化模型研究

在研究项目群工期—费用优化问题时,首先剖析了项目群工期—费用优化机理。然后建立了甲供资源约束条件下项目群实施前的工期—费用优化模型,对初始网络计划进行优化,使业主支付款净现值最小。但由于项目群中各合同项目间存在资源竞争和工期冲突等矛盾,可能引起某一合同项目的工期延误。基于此,构建项目群实施过程中的工期—费用优化模型,对实施前优化后的网络计划进行动态调整。最后结合南水北调某X项目群对比了不同工期—费用优化下的结果。结果显示:两阶段的项目群工期—费用优化可实现项目群的费用控制目标,为项目经理确定项目群中各合同项目的起始时间提供依据。

Duration-Cost Optimization Model of Program of South-to-North Water Diversion Project under Restriction of Resources Supplied by Employer

Since 2015, the National Development and Reform Commission, the Ministry of Foreign Affairs and the Ministry of Commerce jointly released the Vision and Actions for Promoting the Building of the Belt and Road Initiative. Afterwards, more and more large and medium-sized construction projects are undertaken at home and abroad. In order to make sure that the three control objectives of duration, cost and quality can be implemented successfully, the program management of large and medium-sized construction projects came into being. Based on this, this paper focuses on the duration-cost optimization of program, and establishes a time-cost optimization model before the implementation of the program under the condition of resource constraints, then the initial network plan is optimized. After that, taking the penalty fee (f(T_#em/em#)) into consideration, which is proposed by the employer to the contractor due to the delay of the bidding section on the non-critical path, the duration-cost optimization model during the implementation of the program is constructed, then the optimized network plan before implementation is dynamically adjusted, so that the net present value of the employer's payment can be minimized. Finally, the results of different duration-cost optimization are illustrated by an example:program X of South-to-North Water Diversion. The results show that the two-stage proposed model can actualize the optimization of program's cost control objective and provide the basis for the employer to determine the starting time of each bidding section in the program. In this way, the program optimization theory can be further improved.