Evolutionary fuzzy hybrid neural network for project cash flow control

Cheng, Min‐Yuan; Tsai, Hsing‐Chih; Sudjono, Erick

doi:10.1016/j.engappai.2009.10.003

Cited by 28 publications

(15 citation statements)

References 25 publications

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“…Cheng et al [50] [51] have an evolutionary-fuzzy hybrid neural network for project cash flow control. Promote the use of a model that receives as input data indicators on cash operations.…”

Section: Some Of the Most Representative Work In The Field Of Projecmentioning

confidence: 99%

Project Control and Computational Intelligence: Trends and Challenges

Garcia¹,

Peña²,

Pérez³

et al. 2017

IJCIS

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Project monitoring and control by using key performance indicators has become a widespread method for decisionmaking in project-oriented organizations. However, the current schools and IT tools created for this purpose require an upgrade in design due to imprecision, vagueness or uncertainty present in the raw data and changing conditions in management styles. Moreover, the use of proprietary technologies in developing nations represents high costs for governments and obstacles to achieving its technological sovereignty. This paper studies the trends and challenges in project control through computational intelligence methods. It also examines schools and technological tools to manage projects, as well as open source software for the application of computational intelligence techniques over the past decades. Current tendencies and improvement areas, valuing niche markets with high applicability around the thematic goal it is also analyzed. The contribution of this study is related to the predicted necessity of constructing new models and IT tools for project control which integrate machine learning-based approaches and treatment of imprecision, vagueness or uncertainty in the information, using key performance indicators linked to fundamental knowledge areas. The implementation of new libraries for learning evaluation in project control with open source software tools, opens a field of research related to increase technological integration with IT project management tools. The content under discussion provides support to improve decision-making in project-oriented organizations.

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“…Cheng et al [50] [51] have an evolutionary-fuzzy hybrid neural network for project cash flow control. Promote the use of a model that receives as input data indicators on cash operations.…”

Section: Some Of the Most Representative Work In The Field Of Projecmentioning

confidence: 99%

Project Control and Computational Intelligence: Trends and Challenges

Garcia¹,

Peña²,

Pérez³

et al. 2017

IJCIS

View full text Add to dashboard Cite

show abstract

“…Scholars utilized a systemic analysis for project cash flows to provide prediction of cash flows and to improve overdraft financing requirements and profitability (Cui et al 2010). Computational intelligence is a typical concept for coping with cash flow management (Afshar, Fathi 2009;Fathi, Afshar 2010;Cheng et al 2009Cheng et al , 2010Lam et al 2009). Recently scholars have been searching other financial tools successful in industries other than the construction industries such as real option and credit guarantee fund (Chiara, Garvin 2007;Chen, Hsu 2008).…”

Section: Disbursement and Financing Of Construction Projectsmentioning

confidence: 99%

Contractor Costs of Factoring Account Receivables for a Construction Project / Rangovo Sąnaudos, Statybų Projekto Gautinas Sumas Perdavus Faktoringo Įmonei

Chen

2012

Journal of Civil Engineering and Management

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Abstract. Literature reveals that approximately 66% of construction project funds are raised from financial institutions. The burden of capital costs on contractors is heavy and financial alternatives for the reduction of capital costs are always desired. The objective of this study is to derive a mathematical way of defining the contractor's costs for factoring account receivables, which is a form of commercial finance whereby a business sells its account receivables at a discount. Factoring can be thus considered as a contractor selling his/her accounts receivable to a factor, a financial institution that provides the services of financing, credit management, and collection. Nevertheless, factoring has far not been used for construction project financing. The relevant literature, empirical practices, and factoring theories from outside the construction industry are all evaluated and the features needed to derive the cost function are explored and integrated. This includes commission costs, expected debt costs, and credit monitoring costs. The case study is utilized and discussed to illustrate the use of factoring for a construction project and its related costs. Under the given assumptions that represent the most common financial conditions in Taiwan, the simulation results show that the contractor's factoring costs without recourse for the construction project make up only 0.8% of the total costs of the examined project which is relatively lower than that of most traditional financing. In addition, the application of factoring has the advantage of facilitating financial management, instantly improving cash flow, enhancement of investment efficiency, avoiding extra loan procedures, improving credit rating, and transfer of financial risk. Factoring is indeed a feasible financial tool for construction projects.

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“…In many cases one can achieve much higher performance while the system complexity is only slightly increased (Tallon-Ballesteros and Hervas-Martinez, 2011). The most popular hybrid systems include evolutionary-neural (Font et al, 2010;Chandra et al, 2011;Su et al, 2011;Tong and Schierz, 2011;Yang and Chen, 2012;Zhang et al, 2011), evolutionary-fuzzy (Cheng et al, 2010;Lin and Chen, 2011;Antonelli et al, 2009;Cheshmehgaz et al, 2012;Aydogan et al, 2012) and neuro-fuzzy systems (Shahlaei et al, 2012;Czogała and Łęski, 2000;Tadeusiewicz, 2010b;Tadeusiewicz and Morajda, 2012).…”

Section: Introductionmentioning

confidence: 99%

Approximation of phenol concentration using novel hybrid computational intelligence methods

Pławiak¹,

Tadeusiewicz²

2014

International Journal of Applied Mathematics and Computer Science

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This paper presents two innovative evolutionary-neural systems based on feed-forward and recurrent neural networks used for quantitative analysis. These systems have been applied for approximation of phenol concentration. Their performance was compared against the conventional methods of artificial intelligence (artificial neural networks, fuzzy logic and genetic algorithms). The proposed systems are a combination of data preprocessing methods, genetic algorithms and the Levenberg-Marquardt (LM) algorithm used for learning feed forward and recurrent neural networks. The initial weights and biases of neural networks chosen by the use of a genetic algorithm are then tuned with an LM algorithm. The evaluation is made on the basis of accuracy and complexity criteria. The main advantage of proposed systems is the elimination of random selection of the network weights and biases, resulting in increased efficiency of the systems.

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Evolutionary fuzzy hybrid neural network for project cash flow control

Cited by 28 publications

References 25 publications

Project Control and Computational Intelligence: Trends and Challenges

Project Control and Computational Intelligence: Trends and Challenges

Contractor Costs of Factoring Account Receivables for a Construction Project / Rangovo Sąnaudos, Statybų Projekto Gautinas Sumas Perdavus Faktoringo Įmonei

Approximation of phenol concentration using novel hybrid computational intelligence methods

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