Tarek Hegazy

Bio: Tarek Hegazy is an academic researcher from University of Waterloo. The author has contributed to research in topics: Project management & Construction management. The author has an hindex of 35, co-authored 130 publications receiving 5563 citations. Previous affiliations of Tarek Hegazy include Concordia University & Mansoura University.

Optimization of Resource Allocation and Leveling Using Genetic Algorithms

Abstract: Resource allocation and leveling are among the top challenges in project management. Due to the complexity of projects, resource allocation and leveling have been dealt with as two distinct subproblems solved mainly using heuristic procedures that cannot guarantee optimum solutions. In this paper, improvements are proposed to resource allocation and leveling heuristics, and the Genetic Algorithms (GAs) technique is used to search for near-optimum solution, considering both aspects simultaneously. In the improved heuristics, random priorities are introduced into selected tasks and their impact on the schedule is monitored. The GA procedure then searches for an optimum set of tasks' priorities that produces shorter project duration and better-leveled resource profiles. One major advantage of the procedure is its simple applicability within commercial project management software systems to improve their performance. With a widely used system as an example, a macro program is written to automate the GA proced...

Neural network model for parametric cost estimation of highway projects

Abstract: This paper uses a neural network (NN) approach to effectively manage construction cost data and develop a parametric cost-estimating model for highway projects. Eighteen actual cases of highway projects constructed in Newfoundland, Canada, have been used as the source of cost data. Rather than using black-box NN software, a simple NN simulation has been developed in a spreadsheet format that is customary to many construction practitioners. As an alternative to NN training, two techniques were used to determine network weights: (1) simplex optimization; and (2) genetic algorithms (GAs). Accordingly, the weights that produced the best cost prediction for the historical cases were used to find the optimum NN. To facilitate the use of this NN on new projects, a user-friendly interface was developed using spreadsheet macros to simplify user input and automate cost prediction. For practicality, sensitivity analysis and adaptation modules have also been incorporated to account for project uncertainty and to reoptimize the model on new historical data. Details regarding model development and capabilities have been discussed in an attempt to encourage practitioners to benefit from the NN technique.

Optimization of construction time-cost trade-off analysis using genetic algorithms

Abstract: In the management of a construction project, the project duration can often be compressed by accelerating some of its activities at an additional expense. This is the so-called time-cost trade-off ...

Neural networks as tools in construction

Abstract: The competitive and risk-averse nature of the construction industry and its heuristic problem-solving needs, among other reasons, have contributed to the development of nontraditional decision-making tools. Research in artificial intelligence (AI), a branch of computer science, has provided more suitable tools to the construction industry. Expert systems have steadily been introduced for different applications in the industry. However, the performance of these systems, during the last decade, is far from ideal. Neural networks research in AI has recently provided powerful systems that work as a supplement or a complement to such conventional expert systems. In this paper, neural networks are introduced as a promising management tool that can enhance current automation efforts in the construction industry, including expert systems applications. Basic neural network architectures are described, and their potential applications in construction engineering and management discussed. A neural network application is developed for optimum markup estimation. Future possibilities of integrating neural networks and expert systems as a basis for developing efficient intelligent systems are described.

Getting to Yes: Negotiating Agreement without Giving in

Abstract: (2002). Getting to Yes: Negotiating Agreement without Giving in. Quality Management Journal: Vol. 9, No. 2, pp. 73-74.