A New Uncertain Modeling of Production Project Time and Cost Based on Atanassov Fuzzy Sets

Uncertainty plays a major role in any project evaluation and management process. One of the trickiest parts of any production project work is its cost and time forecasting. Since in the initial phases of production projects uncertainty is at its highest level, a reliable method of project scheduling and cash flow generation is vital to help the managers reach successful implementation of the project. In the recent years, some scholars have tried to address uncertainty of projects in time and cost by using basic uncertainty modeling tools such as fuzzy sets theory. In this paper, a new approach is introduced to model project cash flow under uncertain environments using Atanassov fuzzy sets or intuitionistic fuzzy sets (IFSs). The IFSs are presented to calculate project scheduling and cash flow generation. This modern approach enhances the ability of managers to use their intuition and lack of knowledge in their decision-makings. Moreover, unlike the recent studies in this area, this model uses a more sophisticated tool of uncertain modeling which is highly practical in real production project environments. Furthermore, a new effective IFS-ranking method is introduced. The methodology is exemplified by estimating the working capital requirements in an activity network. The proposed model could be useful for both project proposal evaluation during feasibility studies and for performing earned value analysis for project monitoring and control.

[1]  Fionnuala M. Gormley,et al.  The utility of cash flow forecasts in the management of corporate cash balances , 2007, Eur. J. Oper. Res..

[2]  John-Paris Pantouvakis,et al.  Project cash flow analysis in the presence of uncertainty in activity duration and cost , 2012 .

[3]  张俊岭,et al.  AGGREGATION OPERATORS ON TRIANGULAR INTUITIONISTIC FUZZY NUMBERS AND ITS APPLICATION TO MULTI-CRITERIA DECISION MAKING PROBLEMS , 2014 .

[4]  H. Prade Using fuzzy set theory in a scheduling problem: A case study , 1979 .

[5]  Ka Chi Lam,et al.  A decision-making system for construction site layout planning , 2011 .

[6]  Ammar Peter Kaka,et al.  A novel multiple linear regression model for forecasting S-curves , 2006 .

[7]  Janusz Kacprzyk,et al.  How to measure the amount of knowledge conveyed by Atanassov's intuitionistic fuzzy sets , 2014, Inf. Sci..

[8]  Jyoti Neog Tridiv,et al.  An Application of Fuzzy Soft Sets In Medical Diagnosis Using Fuzzy Soft Complement , 2011 .

[9]  Hans-Jürgen Zimmermann,et al.  Fuzzy Set Theory - and Its Applications , 1985 .

[10]  Lynn Crawford,et al.  Fundamental uncertainties in projects and the scope of project management , 2006 .

[11]  Krassimir T. Atanassov,et al.  My Personal View on Intuitionistic Fuzzy Sets Theory , 2008, Fuzzy Sets and Their Extensions: Representation, Aggregation and Models.

[12]  Ying Wang,et al.  An Approach to Software Selection with Triangular Intuitionistic Fuzzy Information , 2012 .

[13]  Hongxing Li,et al.  Fuzzy Sets and Fuzzy Decision-Making , 1995 .

[14]  Janusz Kacprzyk,et al.  Intuitionistic Fuzzy Sets in some Medical Applications , 2001, Fuzzy Days.

[15]  Craig Lawson,et al.  THE APPLICATION OF A NEW RESEARCH AND DEVELOPMENT PROJECT SELECTION MODEL IN SMES , 2006 .

[16]  Ching-Hsue Cheng,et al.  Using intuitionistic fuzzy sets for fault-tree analysis on printed circuit board assembly , 2006, Microelectron. Reliab..

[17]  Andrés Barge-Gil,et al.  Public selection and financing of R&D cooperative projects: Credit versus subsidy funding , 2010 .

[18]  Rupak Bhattacharyya,et al.  A Grey Theory Based Multiple Attribute Approach for R&D Project Portfolio Selection , 2015 .

[19]  Raluca Vernic,et al.  On a fuzzy cash flow model with insurance applications , 2015 .

[20]  S. Meysam Mousavi,et al.  A New Optimization Model for Project Portfolio Selection Under Interval-Valued Fuzzy Environment , 2015 .

[21]  Che-Hung Liu,et al.  Estimating a project's profitability: A longitudinal approach , 2013 .

[22]  James Nga-Kwok Liu,et al.  A New Rule-Based SIR Approach to supplier Selection under Intuitionistic Fuzzy Environments , 2012, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[23]  Paulo S. F. Barbosa,et al.  A linear programming model for cash flow management in the Brazilian construction industry , 2001 .

[24]  Fatih Emre Boran,et al.  The Evaluation of Renewable Energy Technologies for Electricity Generation in Turkey Using Intuitionistic Fuzzy TOPSIS , 2012 .

[25]  Reza Tavakkoli-Moghaddam,et al.  A Fuzzy Stochastic Multi-Attribute Group Decision-Making Approach for Selection Problems , 2011, Group Decision and Negotiation.

[26]  A. P. Kaka,et al.  A Decision Support Model for Construction Cash Flow Management , 2007, Comput. Aided Civ. Infrastructure Eng..

[27]  Robert L. K. Tiong,et al.  Model on cash flow forecasting and risk analysis for contracting firms , 2002 .

[28]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[29]  Ammar Peter Kaka,et al.  A neural networks approach for cost flow forecasting , 1998 .

[30]  Zeshui Xu,et al.  Intuitionistic Fuzzy Analytic Hierarchy Process , 2014, IEEE Transactions on Fuzzy Systems.

[31]  Keon-Myung Lee,et al.  Fuzzy Information Processing for Expert Systems , 1995, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[32]  James T. O'Connor,et al.  Cash Flow Projections for Selected TxDoT Highway Projects , 2007 .

[33]  Ali Touran,et al.  Analysis of the United States Department of Transportation Prompt Pay Provisions , 2004 .

[34]  Min-Yuan Cheng,et al.  Cash flow prediction for construction project using a novel adaptive time-dependent least squares support vector machine inference model , 2015 .

[35]  Franco Caron,et al.  A cash flow-based approach for assessing expansion options stemming from project modularity , 2014 .

[36]  Tiesong Hu,et al.  An integration of the fuzzy reasoning technique and the fuzzy optimization method in construction project management decision-making , 2001 .

[37]  Ali Asghar,et al.  A Fuzzy Statistical Expert System for Cash Flow Analysis and Management under Uncertainty , 2013 .

[38]  Raja R. A. Issa,et al.  Construction Project Cash Flow Planning by Pareto Optimality Efficiency Network Model , 2011 .

[39]  Krassimir T. Atanassov,et al.  Intuitionistic fuzzy sets , 1986 .

[40]  S. Chanas,et al.  THE USE OF FUZZY VARIABLES IN PERT , 1981 .

[41]  Hepu Deng,et al.  Comparing and ranking fuzzy numbers using ideal solutions , 2014 .

[42]  B. Cooke,et al.  Cost and financial control for construction firms , 1979 .

[43]  Min-Yuan Cheng,et al.  Evolutionary fuzzy decision model for cash flow prediction using time-dependent support vector machines , 2011 .

[44]  Achilles Kameas,et al.  Using a Combined Intuitionistic Fuzzy Set-TOPSIS Method for Evaluating Project and Portfolio Management Information Systems , 2011, EANN/AIAI.

[45]  Denis F. Cioffi A tool for managing projects: an analytic parameterization of the S-curve , 2005 .

[46]  E. Lee,et al.  Project network analysis with fuzzy activity times , 1988 .

[47]  Anh N. Duong,et al.  Rate-Decline Analysis for Fracture-Dominated Shale Reservoirs , 2011 .

[48]  Awad S. Hanna,et al.  Assessment of working capital requirements by fuzzy set theory , 2000 .

[49]  Qing Zhang,et al.  Applying Quality Function Deployment Techniques in Lead Production Project Selection and Assignment , 2014 .