A Multi-criteria decision support system for optimal allocation of subcontractors in construction projects


Thesis Type: Postgraduate

Institution Of The Thesis: Middle East Technical University, Faculty of Engineering, Department of Civil Engineering, Turkey

Approval Date: 2016

Thesis Language: English

Student: Semih Akkerman

Supervisor: RİFAT SÖNMEZ

Abstract:

Achieving the success in a construction project requires ultimate harmony of numerous disciplines that are involved in. In a construction project, in order to reach the targeted cost, time and quality performance, the allocation of the appropriate subcontractors to the appropriate parts of the work is essential. This thesis presents a multi-criteria decision support system using Analytical Network Process (ANP) and Pareto Front Optimization in order to select the most eligible subcontractors for core and shell works in a construction project. The main purpose of the thesis is to develop a multi-criteria decision support system for subcontractor selection in construction projects. For this purpose, a 4-module tool is created using MS Excel and Visual Basic for Applications (VBA) which assess the tendering stage of a core and shell project portfolio limited to 15 work parts and 35 candidate subcontractors. Within the process, the weights of the factors affecting the “credibility” of the candidate subcontractors are determined by the decision maker using 15 predefined credibility factors obtained from a comprehensive literature research. The relevant information is gathered from the candidate subcontractors and final credibility indexes of each subcontractor are determined using the Analytical Network Process. Finally, the bids are gathered from the candidate subcontractors and the tender matrix is constructed considering the eligibility of the subcontractors due to their limitations regarding project timing out, bank references, and work completion. The cumulative cost versus credibility plots belonging different allocation scenarios are drawn using a heuristic optimization algorithm and Pareto optimal solutions are presented. Two case studies for a private organization tendering process are used to illustrate the proposed subcontractor selection decision support system.