الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
 |  | from | 158 |  |  |
| | | from | 158 | | |
Abstract
In this thesis, a multiobjective optimization model for construction cash flow was introduced. First, necessary background material for cash flow issues and basic information about the problem were reviewed. This includes project cost estimating and cash flow. Previous research efforts for solving different cash flow problems were also discussed. Then, a brief of Genetic Algorithms optimization method was outlined and multiobjective optimization was also explained. A cash flow optimization model was developed. This study aims to improve financial statement and cash flow and reduce bid price by developing two models, first for cash flow prediction and second for improving unbalanced bidding schemes and integrating the previous two models. The model utilizes the artificial intelligence techniques for optimization and is supported by non-sorting genetic algorithm (NSGA) concept as a multiobjective sorting approach. The Pareto front sorting concept was used to evaluate the solutions considering all objectives. In order to facilitate the implementation of the proposed model, an automated system is presented. MS-Project and MS-Excel were used because of their simplicity in use and programmability features. The automated system facilitates several optimization options via different commands, cash flow, BOQ items price, and optimization. In order to validate the performance of the developed model, two real-life construction projects were tested. The first case study is part of civil works of ” Beni Suef Power Station Project” located in Beni Suef, Egypt.