Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
A SUPPORT VECTOR MACHINE METHOD FOR BID/NO BID DECISION MAKING
Download
10.3846:13923730.2017.1281836.pdf
Date
2017-01-01
Author
Sönmez, Rifat
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
294
views
159
downloads
Cite This
The bid/no bid decision is an important and complex process, and is impacted by numerous variables that are related to the contractor, project, client, competitors, tender and market conditions. Despite the complexity of bid decision making process, in the construction industry the majority of bid/no bid decisions is made informally based on experience, judgment, and perception. In this paper, a procedure based on support vector machines and backward elimination regression is presented for improving the existing bid decision making methods. The method takes advantage of the strong generalization properties of support vector machines and attempts to further enhance generalization performance by eliminating insignificant input variables. The method is implemented for bid/no bid decision making of offshore oil and gas platform fabrication projects to achieve a parsimonious support vector machine classifier. The performance of the support vector machine classifier is compared with the performances of the worth evaluation model, linear regression, and neural network classifiers. The results show that the support vector machine classifier outperforms existing methods significantly, and the proposed procedure provides a powerful tool for bid/no bid decision making. The results also reveal that elimination of the insignificant input variables improves generalization performance of the support vector machines.
Subject Keywords
Construction management
,
Support vector machine
,
Bidding
,
Decision making
,
Decision support systems
,
Classification
,
Machine learning
URI
https://hdl.handle.net/11511/47330
Journal
JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT
DOI
https://doi.org/10.3846/13923730.2017.1281836
Collections
Department of Civil Engineering, Article
Suggestions
OpenMETU
Core
A Decision Support System for Project Portfolio Management in Construction Companies
Bilgin, Gozde; Dikmen Toker, İrem; Birgönül, Mustafa Talat; ÖZORHON ORAKÇAL, BELİZ (2022-11-01)
Project portfolio management requires a systematic process that comprises assessment of portfolio risk and expected profitability, as well as strategic fit of individual projects with company objectives. After a needs analysis based on literature findings and surveys with experts, in this study, a process model and a tool, COPPMAN (COnstruction Project Portfolio MANagement), were developed to support project portfolio decisions in construction companies. COPPMAN was developed in collaboration with construct...
A Prototype Risk Management Decision Support Tool for Construction Projects
Arıkan, Ae; Dikmen Toker, İrem; Birgönül, Mustafa Talat (null; 2009-09-30)
Although risk management (RM) is accepted as one of the critical success factors for construction projects, project participants generally do not have sufficient knowledge pertinent to RM concept and the number of support tools which facilitate the process is rather low. Decision support tools are necessary for the systematic identification of risks, scenario generation, and proactive management of risk and integration of RM activities with other project management functions. The aim of this study is to int...
A Support vector regression method for conceptual cost estimate of construction projects
Yolasığmaz, İsmet Berki; Sönmez, Rifat; Department of Civil Engineering (2015)
Conceptual cost estimate is very important for initial project decisions when the design information is limited and the scope is not finalized at the early stages of the construction projects. It has serious effects on planning, design, cost management and budgeting. Therefore, the decision makers should be as accurate as possible while estimating the conceptual cost at the initial stage since a misestimation on the conceptual cost may lead to serious problems during feasibility analysis or at the later sta...
A Life cycle costing based decision support tool for cost-optimal energy efficient design and/or refurbishments
Emekci, Şeyda; Tanyer, Ali Murat; Department of Architecture (2018)
In construction sector, deciding on building investment / refurbishments can be a complex process because it involves multiple criteria and generally conflicting objectives. For this reason, in the early phase, it is necessary to carry out an analysis that can enhance the predictability of these decisions taken, determine the optimum points of conflicting decisions and at the same time increase the social, environmental and economic sustainability. In the analysis, the total cost incurred building life cycl...
A hierarchical decision support system for workforce planning in medical equipment maintenance services
Cihangir, Çiğdem; Bayındır, Zeynep Pelin; Tan, Tarkan; Department of Industrial Engineering (2010)
In this thesis, we propose a hierarchical level decision support system for workforce planning in medical equipment maintenance services. In strategic level, customer clusters and the total number of field engineers is determined via a mixed integer programming and simulation. In MIP, we aim to find the minimum number of field engineers. Afterwards, we analyze service measures such as response time via simulation. In tactical level, quarterly training program for the field engineers is determined via mixed ...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
R. Sönmez, “A SUPPORT VECTOR MACHINE METHOD FOR BID/NO BID DECISION MAKING,”
JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT
, pp. 641–649, 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/47330.