资源均衡下的项目支付进度协同优化理论及应用研究
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摘要
资源均衡下的项目支付进度问题是研究在考虑资金的时间价值因素及资源均衡后,在项目周期内如何合理的安排活动进度以使项目实施双方经济利益最大化。优化项目支付进度安排是消除双方由于进度安排原因而产生经济纠纷,实现双赢的最有效方法。但传统的项目支付进度安排只是追求一种财务上的静态的优化,这是一种项目实施前的期望收益。事实上它忽视其它因素对进度计划安排的影响,尤其是资源的配置。由于在不同的进度安排下会引起不同的资源需求分布,会产生不同的实施成本,直接影响到项目双方的实际利益。因而在支付进度安排上仅仅考虑财务上双方的收益显然是不公平合理的。在本文中将协同考虑双方财务上收益和资源配置对项目支付进度安排的影响,寻求两者之间的平衡,找出相互可以接受的均衡点。为此,在本文中将开展网络资源均衡及其资源均衡下的投资支付进度协同优化的研究,具体内容如下:
     (1)针对传统资源均衡算法中有关活动和资源的执行模式假设条件和缺陷,分析了活动多模式和资源多种配置模式的实际执行情况,建立了在活动多模式下非常规资源配置的资源均衡优化模型和求解算法。
     (2)分析了传统的均衡方法中所采取的平移策略给项目计划带来了风险性和危害性,提出了允许活动间资源相互调度和变动资源的思路,给出了活动平移与活动上资源动态调整的均衡优化策略,并建立了在此策略下资源均衡优化模型和求解算法。
     (3)分析了活动间无延迟的结束~开始的时序关系的特殊性,研究了实际活动间的几种带有最小时距和最小最大时距的广义时序关系,建立了广义时序关系下的资源均衡优化模型和求解算法。
     (4)分析了不均衡资源分布对项目执行费用的影响,综合考虑了资源均衡及承包商与业主财务收益对项目支付进度的影响,研究了在资源均衡条件下项目支付进度计划问题,建立了支付进度协同优化的多目标优化模型。
     (5)针对网络计划优化问题属于典型的NP难题,尤其是大规模资源优化问题的精确解法的性能变差,难以在有效的计算时间内求解,为体现本文所建立模型均衡优化的实用性,同时基于模拟退火算法(SA)在求解组合优化方面已有了成功的应用,研究了近似最优解的改进的模拟退火的启发式求解算法,包括有记忆的SA、带返回随机搜索的SA和随机型多次寻优法。
The issue of project investment payment scheduling is to study how to arrange activities reasonably considering the time value of capital so that both parties of the project implementation could maximize their economic interests. By optimizing project investment payment scheduling, the root of economic dispute exists between both parties could be eliminated. It is the most effective method to realize win-win. However, traditional investment payment scheduling only pursues static optimization in finance, which is an expected return before the implementation of the project. In fact, influences from other factors on scheduling are neglected, especially that of resource allocation. Since different types of scheduling lead to different distribution of resource demand, and cause different implementation costs, the practical interests of both parties will be influenced directly. Thus, it is clearly neither fair nor reasonable that only consider both parties' return financially when dealing with the issue of payment scheduling. In the thesis, the influences from financial returns of both parties and resource allocation on project payment scheduling are considered collaboratively, and seek a balance between these two factors, and then find out a leveling point that is acceptable mutually. For that puipose, network resource leveling and collaborative optimization for investment payment scheduling are studied in this thesis. The specific contents are as follows:
     (1) Aiming at assumptions and defects of execution mode for activities and resource that traditional resource leveling algorithm has, practical executions of multi-modal activities and multiple resource allocation modes are analyzed, and a resource leveling optimization model for non-conventional resource allocation with multi-mode activities is constructed. A solution algorithm is also provided.
     (2) Risks and harms that translation strategy in traditional algorithm brings to the project are analyzed. An idea of allowing resource to be scheduled between a pair of activities is proposed. A leveling optimization strategy for activity translation and dynamic resource adjustment in activities is established, and a resource leveling optimization model and solution algorithm is also constructed under that strategy.
     (3) Specialties of Finish-to-Start precedence relation with zero time-lag between a pair of activities are analyzed. Several generalized precedence relations and their minimum time intervals are studied, and a resource leveling optimization model with generalized precedence relation and the solution algorithm are built.
     (4) The influence on project execution costs from imbalanced resource distribution is analyzed. The issue of investment payment scheduling in the condition of resource leveling is studied. Resource leveling and collaborative optimization of investment payment scheduling are considered comprehensively, and the associated multi-objective optimization model is built.
     (5) Network schedule optimization is the typical NP hard problem, and especially when the nature of accurate solution for large scale resource optimization goes worse, it is difficult to solute within effective calculation time. Thus, to show the practicality of the above leveling optimization model, and based on the achievements of SA in solving combinatorial optimization, a heuristic algorithm of improved SA which is approximate optimal solution is studied, including the SA with memory, the SA with going back and random seeking, the SA with random multiple optimization.
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