Artificial intelligence approaches to achieve strategic control over project cash flows

Cheng, Min‐Yuan; Tsai, Hsing‐Chih; Liu, Chih-Lung

doi:10.1016/j.autcon.2008.10.005

Cited by 60 publications

(26 citation statements)

References 32 publications

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“…Applications that use three AI techniques include Cheng et al (2009) to achieve strategic control over project cash flow. In this work, FL and NN are employed in the EFNIM to develop a neural-fuzzy model that can deal with uncertainties and knowledge mapping and a GA is used to optimize the membership functions of FL and NN parameters globally.…”

Section: Hybrid Ai In Process Planning and Controlmentioning

confidence: 99%

A survey of AI in operations management from 2005 to 2009

Kobbacy

Vadera

2011

Journal of Manufacturing Technology Management

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Purpose: The use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research.Design/methodology/approach: The paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004(Kobbacy et al. 2007. Like the previous survey, it uses the Elsevier's ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings:The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research.Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research.

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Section: Hybrid Ai In Process Planning and Controlmentioning

confidence: 99%

A survey of AI in operations management from 2005 to 2009

Kobbacy

Vadera

2011

Journal of Manufacturing Technology Management

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“…For this purpose, various methods, tools and techniques for planning and monitoring construction projects are being developed, e.g., the fuzzy set theory [11,12] which is used to assess the impact of quantitative and qualitative factors on the assessment of the demand for working capital in construction projects. Within the framework of the proposed research methods that use artificial intelligence, other methods, apart from fuzzy logic, can also be used for monitoring cash flows, such as: k-means grouping, genetic algorithms, and artificial neural networks [13,14]. When planning the costs of construction projects in the life cycle of a building, there are also models that take into account cost risks [15], as well as risks related to construction works and situations in which events may occur randomly and change the duration and cost of the project or reduce its quality [16,17].…”

Section: Approach To Cost Managementmentioning

confidence: 99%

Methodology of Planning the Course of the Cumulative Cost Curve in Construction Projects

Konior

Szóstak

2020

Sustainability

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Appropriate planning and effective monitoring of the execution of construction projects is important with regard to their successful sustainment of implementation. Time and cost are key elements that determine the success or failure of construction projects. The obtaining of a rational S-curve course before the start of a construction project that reflects reality is important for all the participants involved in implementing an investment task. The article proposes an original methodology for planning the course of the cumulative cost curve in construction projects. It uses a method of shaping the S-curve, which is well-known in both literature and practical approaches. On the basis of the authors' own research carried out in a homogeneous research group of hotel facilities, the areas of the curve for the correct planning of costs in construction projects were designated, which determine the boundaries of the predicted costs accumulated over time. The data for the development of the authors' research methodology is the result of the authors' own experience and professional work. The authors carried out Bank Investment Supervision in the years 2006-2019 on behalf of the banks that grant investment loans for non-public contracts. Knowing the total cost and duration of the planned construction project, which were determined on the basis of project documentation, cost estimates, and also their own database regarding planned and completed deadlines and budgets of similar investments, 6th degree polynomials of the real costs of the construction works were determined. This approach enabled the correct planning of costs over time and the determination of planned monthly amounts of construction works to be executed.

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“…Due to the uncertain and context-dependent nature of construction projects, it is usually expensive to develop deterministic models for EAC prediction. In this case, an approximate inference which is cost effective and fast may be the viable alternative [13]. Inference models are used to formulate new facts from historical data, and its processes adaptively changes when the historical data are altered.…”

Section: Introductionmentioning

confidence: 99%

Estimation at Completion Simulation Using the Potential of Soft Computing Models: Case Study of Construction Engineering Projects

AlHares

Budayan

2019

Symmetry

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“Estimation at completion” (EAC) is a manager's projection of a project's total cost at its completion. It is an important tool for monitoring a project's performance and risk. Executives usually make high-level decisions on a project, but they may have gaps in the technical knowledge which may cause errors in their decisions. In this current study, the authors implemented new coupled intelligence models, namely global harmony search (GHS) and brute force (BF) integrated with extreme learning machine (ELM) for modeling the project construction estimation at completion. GHS and BF were used to abstract the substantial influential attributes toward the EAC dependent variable, whereas the effectiveness of ELM as a novel predictive model for the investigated application was demonstrated. As a benchmark model, a classical artificial neural network (ANN) was developed to validate the new ELM model in terms of the prediction accuracy. The predictive models were applied using historical information related to construction projects gathered from the United Arab Emirates (UAE). The study investigated the application of the proposed coupled model in determining the EAC and calculated the tendency of a change in the forecast model monitor. The main goal of the investigated model was to produce a reliable trend of EAC estimates which can aid project managers in improving the effectiveness of project costs control. The results demonstrated a noticeable implementation of the GHS-ELM and BF-ELM over the classical and hybridized ANN models.

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Artificial intelligence approaches to achieve strategic control over project cash flows

Cited by 60 publications

References 32 publications

A survey of AI in operations management from 2005 to 2009

A survey of AI in operations management from 2005 to 2009

Methodology of Planning the Course of the Cumulative Cost Curve in Construction Projects

Estimation at Completion Simulation Using the Potential of Soft Computing Models: Case Study of Construction Engineering Projects

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