Project cash flow analysis in the presence of uncertainty in activity duration and cost

The inherent uncertainty and imprecision in project scheduling have motivated the proposal of several fuzzy set theory based extensions of activity network scheduling techniques. Building upon these, a cash flow calculation methodology for projects including activities with fuzzy durations and/or costs is proposed in this paper. According to the proposed approach, the project cash flow is represented by an S-surface (as opposed to the traditional S-curve) ensuing by connecting S-curves at different risk possibility levels. The methodology is exemplified by estimating the working capital requirements in a real world road construction project. Furthermore, the benefits of the methodology and its subsequent computerization are discussed. It is believed that the proposed approach may also be useful for both evaluating project proposals during feasibility studies and for performing earned value analysis for project monitoring and control.     

Evolutionary fuzzy decision model for cash flow prediction using time-dependent support vector machines

The ability of project managers to make reliable cash flow predictions enhances project cost flow control and management. Reliable cash flow prediction over the course of a construction project puts the project manager in a better position to identify potential problems and develop appropriate strategies to mitigate the negative effects of such on overall project success. Therefore, managers should monitor project progress using cash flow data, which has unique characteristics, as time series data. However, the complex, mutable nature of construction projects currently requires significant reliance on experience and expert opinions to predict cash flow on an ongoing basis. Recent studies have indicated good potential for using artificial intelligence to reduce reliance on human input in cash flow prediction processes. The Evolutionary Fuzzy Support Vector Machine Inference Model for Time Series Data (EFSIM_T), an artificial intelligence hybrid system focusing on the management of time series data characteristics which fuses fuzzy logic (FL), weighted support vector machines (weighted SVMs) and a fast messy genetic algorithm (fmGA), represents a promising alternative approach to predicting cash flow. Simulations performed on historical cash flow data demonstrate the EFSIM_T is an effective tool for predicting cash flow.     

Modelling standard cost commitment curves for contractors' cash flow forecasting

Cash flow forecasting and control are essential to the survival of any contractor. The time available for a detailed pre-tender cash flow forecast is often limited. Therefore, contractors require simpler and quicker techniques which would enable them to forecast cash flow with reasonable accuracy. This paper identifies causes behind the inaccuracy of current standard value S-curves (which are often used as an alternative approach for cash flow forecasting) and proposes the use of standard cost commitment models. The process of developing and testing the cost commitment models involved first collecting actual data for 150 completed projects. Several criteria were identified to classify these projects. Tests were conducted to identify which of these criteria affected the shape of the cost commitment curves. Projects were then distributed into different groups and S-curves were fitted into each using the logit transformation technique. Errors incurred when fitting these curves were measured and compared with those associates in fitting individual projects. Results showed that the difference between these errors was not significant. The reliability of selecting the cost commitment curve to model (instead of value curves) was evaluated. Results confirmed the hypothesis that cost commitment models are more accurate and reliable than value models. Finally, the paper outlines some of the practices involved in utilizing the proposed models.     

Applying fuzzy techniques to cash flow analysis

Construction managers are interested in the direction of movement of cash flow at valuation periods rather than its forecast value, and fuzzy set theory applied to decision making might help in this process. Fuzzy models are particularly suited to making decisions involving new technologies where uncertainties inherent in the situation are complex. The problem of healthy cash flow at valuation periods relates to the proper estimation of cash in and out flows and project progress. The paper presents an alternative approach to cash flow analysis for construction projects. This project is based on the assumption that cash flow at particular valuation stages of a project is ambiguous. The paper discusses the weaknesses of existing methods for cash flow and establishes the need for an alternative approach. Using an example of 30 cash flow curves, the advantage of fuzzy cash flow analysis is demonstrated. Results of the analysis are presented and discussed. The model can be used to analyse the cash flow curve of projects at any progress period to make sure it is reasonable.     

Project cash forecasting in the client organization

Project cash forcasting is a major problem faced by client organizations, due largely to inaccurate project estimates at the outset. Inadequate project definition and risk analysis at the approval stage and, in some cases, a tendency of the project manager to overestimate are major contributors to the inaccuracy of project estimates. In addition, some organizations expect project managers to develop project-approval estimates to within 10% accuracy, having completed less than 5% of the total project design. The reasons for failure of project cash forecasting are discussed, and the need for developing a forecasting strategy is established. Some solutions to the problems are suggested.     

水电工程多项目资金流预测模型

多项目资金流预测对水电工程项目业主做出投资决策、安排进度计划具有重要意义,而资金流预测的关键问题是建立简单易用的资金流出计算方法。模型对水电工程进行项目划分,以单位工程和s曲线方程作为资金流预测的基础,利用历史相似工程的实际成本数据,采用回归分析的方法,估算在建工程的成本曲线;再考虑具体的合同条件、付款时间差等影响因素,得出实际的资金流出曲线。经算例验证,该方法可满足多项目资金流预测的要求。     

工程浆液流型分析及意义

分别介绍了水基钻井液、油基钻井液、含气体可压缩钻井流体液三种工程浆液的发展历程,针对牛顿流型、宾汉流型、幂律流型和卡森流型四种钻井液理论流型的特点进行了分析,阐述了研究浆液流变型的意义,以期促进钻探工程的发展。     

An empirical examination of project contractors' supply-chain cash flow performance and owners' payment patterns

Supply-chain (SC) cash flow performance is an essential component of SC performance management. Despite the panoply of approaches to SC cash flow modeling, relatively few published studies assess the effect of SC cash flow performance on a project contractor's financial performance. Little research thus explores the behavioral patterns in the project owner-contractor dyad in the context of payment-term negotiation for improving the SC cash flow performance of a project contractor. Using data from 42 Taiwanese construction project contracting corporations, this paper systematically quantifies the effects of SC cash flow performance on the financial performance of construction project contractors. Further analysis using data from 118 returned, usable surveys reveals important behavioral patterns of project owners regarding payment terms with project contractors during the contracting phase of construction projects. These behavioral patterns provide project contractors with a base for supporting implementation efforts for improving SC cash flow performance.     

企业经营战略的现金流量分析

论述了企业经营战略的概念、内涵及类型，并对企业实施不同经营战略的现金流量特征进行了分析，以利于企业对自身经营战略有效性的分析和对竞争对手的竞争战略做出合理评价。     

Enhanced heuristic for finance-based scheduling of construction projects

Typically, construction contractors operate under cash-constrained operating conditions. The lag between the time when contractors spend money to accomplish work on site and the time when payments are actually made by clients, which partially compensate contractors for the accomplished work, constantly creates a finance deficit. Contractors often supplement finance deficits using external funds procured through establishing credit-line bank accounts which typically allow contractors to withdraw cash up to specified credit limits. This makes the task of project scheduling considering the constraints of specified finance very important for financial and operational planning. This scheduling concept and technique are referred to as finance-based scheduling. An enhanced heuristic is proposed to devise finance-based schedules of multiple projects within contractors’ portfolios. The enhancement is achieved by replacing the exhaustive enumeration technique employed in the heuristic to specify activities’ start times with a polynomial shifting algorithm. This enhancement resulted in a substantial reduction in the number of solutions explored before a feasible solution is encountered. The enhanced heuristic was validated through comparison with the integer programming technique using 240 problems of randomly generated networks of sizes that range from 30 to 240 activities. Further, it was proved that the enhanced heuristic can be easily scaled up to handle portfolios of multiple large-size projects.     

物流园区产生交通量预测的研究

阐述了预测物流园区产生交通量的重要性,探讨了综合型物流园区产生交通量的预测思路,即通过分别预测货运与非货运交通量并叠加获得路网中新增交通量,其中针对货运交通量预测采用了两种不同的思路.     

Monthly river flow forecasting by an adaptive neuro-fuzzy inference system

In this study, the applicability of an adaptive neuro-fuzzy inference system (ANFIS) to forecast for monthly river flows is investigated. For this, the Göksu river in the Seyhan catchment located in southern Turkey was chosen as a case study. The river flow forecasting models having various input structures are trained and tested by the ANFIS method. The results of ANFIS models for both training and testing are evaluated and the best-fit forecasting model is determined. The best-fit model is also trained and tested by feed forward neural networks (FFNN) and traditional autoregressive (AR) methods, and the performances of the models are compared. Moreover, ANFIS and FFNN models are verified by a validation data set including river flow data records during the time period 1997–2000. The results demonstrate that ANFIS can be applied successfully and provides high accuracy and reliability for monthly river flow forecasting.     

城市天然气贸易计量流量计的选型建议

本文简要介绍了适合应用于城市天然气贸易计量的各种流量计的特性,分析各类不同用户的具体用气特点,指出了城市天然气贸易计量流量计选型的基本依据,提出了针对流量计种类、规格型号的选型建议。     

论流水施工在公路工程中的局限性

结合工程项目实践,探讨了流水施工在公路工程应用中的局限性,分析了流水施工的影响因素,并提出了相关的应用改进措施和建议,从而达到有效提高施工作业效率的目的。     

贯流风机的试验研究

研究了各种不同的贯流风机外壳对风机气动性能的影响，讨论了与风机性能有关的几个主要几何参数。     

A neural networks approach for cost flow forecasting

Artificial neural networks, which simulate neuronal systems of the brain, are useful methods that have attracted the attention of researchers in many disciplinary areas. They have many advantages over traditional methods in situations where the input-output relationship of the system under study is not explicitly known. This paper investigates the feasibility of using neural networks for predicting the cost flow of construction projects, explains the need for cost flow forecasting, and demonstrates the limitation of the existing models. It then introduces neural networks as an alternative approach to those mathematical and statistical methods. The method used in collecting data and modelling the cost flow is described. Results of the testing are presented and discussed.     

交通流量的混沌特性分析及预测模型研究

基于混沌动力系统的相空间重构和非线性系统的Volterra级数,分析交通流量的混沌特性,研究了一种交通流量的自适应预测模型。在合理选取嵌入维数和延滞时间实现交通流量时间序列相空间重构的基础上,应用小数据量法计算重构交通流量时间序列的最大Lyapunov指数,根据该指数值对交通流量的混沌特性进行分析,并采用庞卡莱截面法对分析结果进行验证;构建交通流量的Volterra预测模型,并采用LMS自适应算法对模型系数进行调整。通过对实际采集的高速公路交通流量数据的仿真研究表明,小数据量法能对交通流混沌特性进行准确判别,构建的二阶Volterra自适应预测模型能够有效地预测交通流量的变化。因此,在判定交通流量存在混沌特性时,可以应用论文构建的二阶Volterra自适应预测模型对其进行准确的预测。