Methods Dealing with Complexity in Selecting Joint Venture Contractors for Large-Scale Infrastructure Projects

The magnitude of business dynamics has increased rapidly due to increased complexity, uncertainty, and risk of large-scale infrastructure projects. This fact made it increasingly tough to “go alone” into a contractor. As a consequence, joint venture contractors with diverse strengths and weaknesses cooperatively bid for bidding. Understanding project complexity and making decision on the optimal joint venture contractor is challenging. This paper is to study how to select joint venture contractors for undertaking large-scale infrastructure projects based on a multiattribute mathematical model. Two different methods are developed to solve the problem. One is based on ideal points and the other one is based on balanced ideal advantages. Both of the two methods consider individual difference in expert judgment and contractor attributes. A case study of Hong Kong-Zhuhai-Macao-Bridge (HZMB) project in China is used to demonstrate how to apply these two methods and their advantages.

[1]  Xiaoling Zhang,et al.  Agglomeration and competitive position of contractors in the international construction sector , 2017 .

[2]  Zhaohan Sheng,et al.  Bidding Strategy to Support Decision-Making Based on Comprehensive Information in Construction Projects , 2016 .

[3]  Nureize Arbaiy,et al.  Multi-Attribute Decision Making in Contractor Selection Under Hybrid Uncertainty , 2011, J. Adv. Comput. Intell. Intell. Informatics.

[4]  Martin Skitmore,et al.  Contractor selection using multi criteria utility theory: an additive mode , 1998 .

[5]  Mahmoud Efatmaneshnik,et al.  On optimal modularity for system construction , 2016, Complex..

[6]  Lotfi A. Zadeh,et al.  The concept of a linguistic variable and its application to approximate reasoning - II , 1975, Inf. Sci..

[7]  Peter E.D. Love,et al.  Statistical analysis of injury and nonconformance frequencies in construction: negative binomial regression model , 2017 .

[8]  Zheng Pei,et al.  An approach to multiple attribute group decision making based on linguistic intuitionistic fuzzy numbers , 2015, Int. J. Comput. Intell. Syst..

[9]  Vahidreza Yousefi,et al.  Proposing a neural network model to predict time and cost claims in construction projects , 2016 .

[10]  Bon-Gang Hwang,et al.  Risk identification and allocation in underground rail construction joint ventures: contractors’ perspective , 2015 .

[11]  Xiangyu Wang,et al.  Research trends and opportunities of augmented reality applications in architecture, engineering, and construction , 2013 .

[12]  Miroslaw J. Skibniewski,et al.  A probabilistic approach for safety risk analysis in metro construction , 2014 .

[13]  F. Seraj,et al.  Risk Analysis in the Management of Urban Construction Projects From the Perspective of the Employer and the Contractor , 2015 .

[14]  Xiangyu Wang,et al.  A theoretical framework of a BIM-based multi-disciplinary collaboration platform , 2011 .

[15]  Zhijing Yang,et al.  Binary artificial algae algorithm for multidimensional knapsack problems , 2016, Appl. Soft Comput..

[16]  Jian Chai,et al.  Decomposition-based multi-objective firefly algorithm for RFID network planning with uncertainty , 2017, Appl. Soft Comput..

[17]  Jian Chai,et al.  Reference tag supported RFID tracking using robust support vector regression and Kalman filter , 2017, Adv. Eng. Informatics.

[18]  Chike F. Oduoza,et al.  Key risks in construction projects in Italy: contractors’ perspective , 2017 .

[19]  Nieves Arranz,et al.  Complex joint R&D projects: From empirical evidence to managerial implications , 2009 .

[20]  Yongtao Tan,et al.  Relationships between Main Contractors and Subcontractors and Their Impacts on Main Contractor Competitiveness: An Empirical Study in Hong Kong , 2017 .

[21]  Young-Ki Huh,et al.  Multivariate discriminant analysis for assessing residential development projects from a contractor’s perspective , 2017 .

[22]  Maryam Darvish,et al.  Application of the graph theory and matrix methods to contractor ranking , 2009 .

[23]  Ka Chi Lam,et al.  Decision support system for contractor pre-qualification : artificial neural network model , 2000 .

[24]  Gwo-Hshiung Tzeng,et al.  Combining grey relation and TOPSIS concepts for selecting an expatriate host country , 2004, Math. Comput. Model..

[25]  Jurgita Antucheviciene,et al.  Selecting a Contractor by Using a Novel Method forMultiple Attribute Analysis: Weighted Aggregated SumProduct Assessment with Grey Values (WASPAS-G) , 2015 .

[26]  Tarek Zayed,et al.  Contractor Selection Model for Highway Projects Using Integrated Simulation and Analytic Network Process , 2013 .

[27]  Chi-Jen Lin,et al.  A causal analytical method for group decision-making under fuzzy environment , 2008, Expert Syst. Appl..

[28]  A. Kaufmann,et al.  Introduction to fuzzy arithmetic : theory and applications , 1986 .

[29]  Yongqiang Chen,et al.  Promoting cooperation in construction projects: an integrated approach of contractual incentive and trust , 2015 .

[30]  Mohan M. Kumaraswamy,et al.  Conceptual Model of Partnering and Alliancing , 2007 .

[31]  Kok Lay Teo,et al.  A hybrid approach to constrained global optimization , 2016, Appl. Soft Comput..

[32]  Chung-Li Tseng,et al.  Managing Project Performance Risks under Uncertainty: Using a Dynamic Capital Structure Approach in Infrastructure Project Financing , 2017 .

[33]  Hamid Lesani,et al.  Financial planning for the preventive maintenance of power distribution systems via fuzzy AHP , 2016, Complex..

[34]  Qiming Li,et al.  Developing Competitive Advantages in Political Risk Management for International Construction Enterprises , 2014 .

[35]  Hans Voordijk,et al.  Beyond theory: Towards a probabilistic causation model to support project governance in infrastructure projects , 2017 .

[36]  Weisheng Lu,et al.  A computer-aided decision support system for assessing a contractor's competitiveness , 2003 .

[37]  Xiangyu Wang,et al.  Case Study of Selecting Decision-Making Schemes in Large-Scale Infrastructure Projects , 2017 .

[38]  Charles Miers,et al.  Cloud resource management: towards efficient execution of large-scale scientific applications and workflows on complex infrastructures , 2017, Journal of Cloud Computing.

[39]  Farzad Khosrowshahi,et al.  Neural network model for contractors’ prequalification for local authority projects , 1999 .

[40]  Murat Gunduz,et al.  Fuzzy Assessment Model to Estimate the Probability of Delay in Turkish Construction Projects , 2015 .

[41]  Wu Gao,et al.  The Portfolio Balanced Risk Index Model and Analysis of Examples of Large-Scale Infrastructure Project , 2017, Complex..

[42]  Bon-Gang Hwang,et al.  Project network management: risks and contributors from the viewpoint of contractors and sub-contractors , 2015 .

[43]  Patrick S. W. Fong,et al.  Final contractor selection using the analytical hierarchy process , 2000 .

[44]  Kok Lay Teo,et al.  An exact penalty function-based differential search algorithm for constrained global optimization , 2015, Soft Computing.

[45]  Y. Konno.  An empirical analysis of the discontinuance of business for startup contractors and property companies in Japan , 2015 .