Contractors Prequalification

This Book explains the existing practices of
contractors pre-qualification in newly developed
countries. The most known models for contractor's
pre-qualification that could be used for
contractor's pre-qualification is presented.
The Book concentrates on client's pre-qualification
for contractors at the level of standing list. The
main focus of this Book is the integration of
contractor pre-qualification attributes with the
project success factors (client s objectives) in
terms of time, cost and quality (i.e. How the client
could predict the contractors performance given his
current attributes). For this purpose, a Genetic
Neural Network (GNN) model has built an automated
system to predict the output of a given contractor
attributes in terms of overruns (OR) of contractor's
performance factors (time, cost and quality).
This book is written for both clients and
contractors. The project managers who are involved
in contracts procurement, tendering and contractors
selection are targeted. The contractors
should be of interest of this book to cope with the
requirement of clients conditions for contractor
selection

Autorentext
Dr. Nabil I El-Sawalhi received his PhD degree from the School of the Built Environment, The University of Salford, UK. He is currently occupying the Emergency Shelter Assistance Coordinator at UNRWA-Gaza Strip. The author is the chairman of Engineering Arbitration Center - Engineers Syndicate-Gaza Governorate.

Klappentext
This Book explains the existing practices of contractors pre-qualification in newly developed countries. The most known models for contractor's pre-qualification that could be used for contractor's pre-qualification is presented. The Book concentrates on client's pre-qualification for contractors at the level of standing list. The main focus of this Book is the integration of contractor pre-qualification attributes with the project success factors (client's objectives) in terms of time, cost and quality (i.e. How the client could predict the contractors performance given his current attributes). For this purpose, a Genetic Neural Network (GNN) model has built an automated system to predict the output of a given contractor attributes in terms of overruns (OR) of contractor's performance factors (time, cost and quality). This book is written for both clients and contractors. The project managers who are involved in contracts procurement, tendering and contractors selection are targeted. The contractors should be of interest of this book to cope with the requirement of clients conditions for contractor selection