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.     

The impact of contract duration on the cost of cash retention

Cash retention is a common means of protecting an employer from a contractor's insolvency as well as ensuring that contractors finish the work that they start. Similarly, contractors withhold part of payments due to their sub-contractors. Larger contracts tend to be subjected to smaller rates of retention. By calculating the cost of retention as an amount per year of a contract, it is shown that retention is far more expensive for firms whose work consists of short contracts. The extra cost is multiplied when the final payment is delayed, as it often is for those whose work takes place at the beginning of a project. This may explain why it is that main contractors are a lot less interested than sub-contractors in alternatives to cash retention, such as retention bonds.     

Parameter prediction for cash flow forecasting models

The paper describes the application of the DHSS formula to 27 completed construction projects comprising four types - steel-framed low rise buildings, new build housing developments, housing refurbishment projects, and multi-house ‘pre-paint’ maintenance contracts. Application of the formula to individual projects indicates that the ‘best’ parameter values offer a ten-fold improvement over the published values based on project size. Similar results occur when using the best parameter values for other two parameter models.

Various approaches are considered in attempting to predict the best parameter values of the models based on known characteristics of the project. A multiple linear regression with project value, duration, and type independent variables is shown not to produce any significant improvement on standard DHSS formula predictions. However, a reduction in the number of independent variables by cross validation produces an approximately 25% improvement on standard DHSS formula forecasts outside the data base. Examination of the models derived from this analysis indicate the type of project to be of major importance.     

A linear programming model for cash flow management in the Brazilian construction industry

A linear programming model has been developed for optimal cash flow management addressing specific cash flow issues related to the construction industry. These include typical financial transactions, possible delays on payments, use of available credit lines, effect of changing interest rates, and budget constraints that often occur in the construction industry. A small size project from the Brazilian construction industry is provided as a case study, aiming at evaluating the potential benefits from using the model. Different changes to the basic structure of the model allow and establish the consistency of the results. Alternative formulations are suggested to deal with uncertainties, longer planning horizons, and multiple subcontractors and suppliers. In addition to the tangible financial earnings derived from the optimization process, the simple structure of the model, as a network flow and corresponding equations, provides much visual insight concerning the relationships between the external inputs and the variables of the problem. Optimal results have yet to be achieved in a real life situation, but a better view of whole cash flow management is provided when using the model.     

Modelling financial decisions in construction firms

Some contractors predict their corporate cash flow on the basis of individual contracts without considering the relationships between the overall before-tax profit, risks, other crucial qualitative factors, or the allocation of resources within the company. Moreover, some contractors, in predicting their cash flow, focus only on the early-start progress in the project and their predictions of progress are too pessimistic, or result in the overuse of resource in order to make up for delays. In the present research a decision model is established for a contracting firm. It provides a methodical system for construction financial decision-making, and a way of solving a financial decision problem under qualitative and fuzzy circumstances. The model can be applied to the management of corporate cash flow, thereby facilitating the minimal use of resources. The information provided by the model allows the planner to eliminate excess use or idleness of resources during the scheduling of a project. Financial forecasting may also suggest the best time to invest in a new project. Four projects for a medium size construction firm in Hong Kong were employed as case studies in order to evaluate the mathematical model. The cases involve two objectives: maximize profit margin and minimize construction risk (consider in a qualitative factor). The model leads to a compromise optimal schedule that provides the contracting firm with the optimal schedule for achieving optimal profit and construction risk by making optimal use of the contractor's resources.     

Supporting financial decision-making based on time value of money with singularity functions in cash flow models

Existing budgeting approaches differ in whether or not they consider the time value of money. A novel use of singularity functions in construction management has the potential to enhance cash flow models in order to maximize their net present value. This type of function can model a complete schedule, which serves as the underlying timeline for all financial transactions. Their variable amounts and constraints are expressed by singularity functions, converted from costs via bills into payments, and compounded towards the overall net present value for financial decision-making. Contributions to the body of knowledge include deriving exact amounts of interest on variable balances for any duration, creating a valuation algorithm, and exploring how the uneven solution space that cash flow profiles create can be searched successfully with a genetic algorithm.     

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.     

Cash farming in building and construction: a stochastic analysis

Cash flow management is a significant issue in the management of a building or construction firm. This paper steps back from the well researched area of poor cash management and its relationship with failure, to focus on the funds which are generated through operations, and the positive benefits which can follow in a well managed organization. A stochastic model is developed which illustrates how an average of 16% of turnover can be available for reinvestment. This is sufficient to allow investment in non-liquid assets, provided that this is managed carefully and precautions are taken against a severe reduction in turnover. This level of funds is sufficient to encourage firms to enter the industry with the motivation of generating funds, rather than a desire to build. This has implications for large clients and for government when dealing with the industry.     

Application of fuzzy techniques to develop an assessment framework for building design eco-drivers

Sustainable building design dynamism is a complex process which involves various design eco-indicators. Consideration of different aspects such as environment, economy and society in addition to design characteristics makes the process of design even more complex. Also the subjectivity in design decisions makes the process of ecological assessment quite vague and difficult. Fuzzy logic techniques could help to compensate for the lack of full knowledge and subjectivity of design parameters. Hence, a fuzzy methodology is proposed in this paper for modelling and representing eco-building design indicators. The model is based on three linguistic variables. The developed model is able to indicate the low eco-efficient and high eco-efficient bands of a particular building design based on a set of eco-design indicators. The process, findings and implication of the proposed model are presented in this paper.     

Systems analysis of project cash flow management strategies

Cash flow management is one of the most important determinants of the success of construction project management. Overdraft, retainage, financing, payment and billing policies constitute the most significant financial issues that contractors must plan, control and manage for the successful completion of construction jobs. Particularly, in an attempt to reduce project costs, contractors must balance cost savings of material discounts due to early payments and extra interest expenses because of additional overdraft. Through identifying feedback loops in project cash flows, a system dynamics model is developed for project cash flow management. The model is flexible to incorporate typical front‐end and back‐end loading cash flow management strategies and provides an interactive predication of project cash flows. A warehouse project is discussed to demonstrate how various cash flow strategies improve overdraft financing requirements and profitability. Especially, the analysis shows an 11% reduction on overdraft requirements while using an overbilling strategy, and 30% reduction if the trade credit strategy is implemented.     

模糊控制理论及音谱分析技术在机械故障诊断中的应用

介绍一种利用模糊控制理论及音谱分析技术对机械设备进行故障诊断的方法。通过检测及分析声音信号,可以在强噪声环境下准确地探测到故障源并分析出其故障类型。     

Cost-effectiveness fuzzy analysis for an efficient reduction of uncertainty

Many planning and production processes are characterized by uncertain data and uncertain information. For realistic modeling of such processes these uncertainties have to be considered. The new approach presented in this paper takes epistemic uncertainty into account, for which fuzzy set theory is applicable. In some cases it is possible and useful to reduce epistemic uncertainty by additional monetary investments. It is postulated that uncertain forecast values, e.g. expected safety, quality, or the completion date of a structure, can be improved or scheduled more precisely by a higher investment. Aim of the presented cost-effectiveness fuzzy analysis is the evaluation of the effectiveness of monetary investments on the reduction of uncertainty of the analyzed forecast values.     

Applying global sensitivity analysis to the modelling of flow and water quality in sewers

While several approaches for global sensitivity analysis (GSA) have been proposed in literature, only few applications exist in urban drainage modelling. This contribution discusses two GSA methods applied to a sewer flow and sewer water quality model: Standardised Regression Coefficients (SRCs) using Monte-Carlo simulation as well as the Morris Screening method. For selected model variables we evaluate how the sensitivities are influenced by the choice of the rainfall event. The aims are to i) compare both methods concerning the similarity of results and their applicability, ii) discuss the implications for factor fixing (identifying non-influential parameters) and factor prioritisation (identifying important parameters) and iii) rank the important parameters for the investigated model. It was shown that both methods lead to similar results for the hydraulic model. Parameter interactions and non-linearity were identified for the water quality model and the parameter ranking differs between the methods. For the investigated model the results allow a sound choice of output variables and rainfall events in view of detailed uncertainty analysis or model calibration. We advocate the simultaneous use of both methods for a first model assessment as they allow answering both factor fixing and factor prioritisation at low computational cost.     

交流变频调速与液压流量间的特性分析

采用变频技术对异步电机的调速已普遍应用在电机调速方面,另一方面,多媒体信息交互设备,PLC工业控制及传感器技术、通讯技术在液压系统上已广泛应用,本课题在此基础上,针对交流变频调速应用于液压流量控制中的工作原理、特性进行分析探究,得出的一些结论表明,变频调速技术在液压系统方面有广阔的应用前景。     

应用模糊综合评判模型评价历史街区保护的研究

与传统方法相比,应用模糊综合评判模型对城市历史街区保护现状和影响进行评价,不仅能解决定性和定量指标难统一的问题,还能融合空间数据的定位特点,真实、有效地反映历史街区的保护状况,为城市历史文化遗产保护规划决策提供科学依据.     

模糊综合评价法在施工前隧道塌方风险分析中的应用

修建于崇山峻岭之中的山岭隧道,由于其复杂的地质条件和施工扰动,造成坍塌、突泥、涌水等地质灾害频发。在此方面,为了达到减轻灾害的目的,普遍开展隧道施工前的安全风险评估和施工开挖后的稳定性评价工作,取得了显著的进展,但是鲜有施工过程中对此类灾害的潜在危险性和危害性的辨识和评估。本文采用模糊综合评价法对碑仔头隧道断层破碎带塌方风险性进行分析,详细阐述模糊综合评价法的具体应用,为隧道穿越断层破碎带时施工风险性做出定量分析,对指导施工具有重要经济意义和安全意义。     

Application of two-stage fuzzy set theory to river quality evaluation in Taiwan

An indicator model for evaluating trends in river quality using a two-stage fuzzy set theory to condense efficiently monitoring data is proposed. This candidate data reduction method uses fuzzy set theory in two analysis stages and constructs two different kinds of membership degree functions to produce an aggregate indicator of water quality. First, membership functions of the standard River pollution index (RPI) indicators, DO, BOD(5), SS, and NH(3)-N are constructed as piecewise linear distributions on the interval [0,1], with the critical variables normalized in four degrees of membership (0, 0.33, 0.67 and 1). The extension of the convergence of the fuzzy c-means (FCM) methodology is then used to construct a second membership set from the same normalized variables as used in the RPI estimations. Weighted sums of the similarity degrees derived from the extensions of FCM are used to construct an alternate overall index, the River quality index (RQI). The RQI provides for more logical analysis of disparate surveillance data than the RPI, resulting in a more systematic, less ambiguous approach to data integration and interpretation. In addition, this proposed alternative provides a more sensitive indication of changes in quality than the RPI. Finally, a case study of the Keeling River is presented to illustrate the application and advantages of the RQI.     

Coupled analysis of flow, stress and damage (FSD) in rock failure

Rock is a heterogeneous geological material that contains natural weakness of various scales. When rock is subjected to mechanical loading, these pre-existing weaknesses can close, open, grow or induce new fractures, which can in turn change the structure of the rock and alter its fluid flow properties. Experimental results provide strong evidence that rock permeability is not a constant, but a function of stresses and stress-induced damage. A flow-stress-damage (FSD) coupling model for heterogeneous rocks that takes into account the growth of existing fractures and the formation of new fractures is proposed herein. Implemented with the Rock Failure Process Analysis code (F-RFPA^2D), this FSD model is used to investigate the behaviour of fluid flow and damage evolution, and their coupling action, in samples that are subjected to both hydraulic and biaxial compressive loadings. The modeling results suggest that the nature of fluid flow in rocks varies from material to material, and strongly depends upon the heterogeneity of the rocks.     

Risk analysis in ultra deep scientific drilling project — A fuzzy synthetic evaluation approach

Exploration of deep earth requires ultra deep drilling attempts on the sea or continent, which is the main goal of scientific drilling projects currently established. Uncertain geological complexity, high requirement for R&D of critical equipment as well as high demand of practical performance has to be encountered during a scientific drilling project, making it full of challenge and risks. Risk management, therefore, is critically proposed for scientific drilling projects in order to reduce the risks. However, many traditional risk assessment methods may not perform well in the project due to lack of high quality data of historical record and sufficient information. This paper, therefore, proposes a fuzzy synthetic evaluation approach for scientific drilling project risk assessment. Four criteria — probability, severity, non-detectability and worsening factor are utilized to evaluate individual and overall risks comprehensively. Linguistic terms instead of numerical values are employed to evaluate each risk normally done by experts. AHP/ANP is used to determine sensible weights of each criterion. Values of risk indices are calculated to represent the level of each risk and the overall risk. Finally, a case study on risk analysis of SinoProbe-09 project conducted in Jilin University is tested to demonstrate the procedure of the method and to validate the proposed method. Results show that the risks of the scientific drilling project can be assessed effectively and efficiently.