A SUPPORT VECTOR MACHINE METHOD FOR BID/NO BID DECISION MAKING

SÖNMEZ R., Soezgen B.

JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT, cilt.23, sa.5, ss.641-649, 2017 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 23 Sayı: 5
  • Basım Tarihi: 2017
  • Doi Numarası: 10.3846/13923730.2017.1281836
  • Dergi Adı: JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.641-649
  • Anahtar Kelimeler: construction management, support vector machine, bidding, decision making, decision support systems, classification, machine learning, CASH FLOW PREDICTION, INFERENCE MODEL, CONSTRUCTION PROJECT, NEURAL-NETWORKS, CLASSIFICATION, COMPLETION, SOFTWARE, SYSTEM, COST, SVM
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

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.