A Hybrid Decision Support System for Efficient Planning and Management of Mega Projects

Recently, as urbanization and industrialization progresses, we have witnessed an increasing number of very large scale development projects (termed mega projects in this paper) all around the world, especially in developing and emerging countries such as Brazil, Russia, India, and China (BRICs). At the same time, issues with mega project decision-making are drawing special attention from investment sponsors, planners, designers, consultants, and project managers owing to the complexities and unique characteristics of such projects. The growing importance of sustainability also makes decision-making for mega projects more challenging, requiring comparisons of the pros and cons of macro-economic effects, environmental impacts, social influences, and energy consumption. This multiple criteria based decision-making goes beyond the traditional project decision-making paradigm that mainly focused on cash input-output flow and increases the challenges for decision support on mega project implementations. To efficiently deal with complexities during the decision-making process for mega projects, we have developed a decision support system that can take into account economic, energy, environmental, and other factors during planning and management of a certain type of mega project. The paper describes this integrated decision support system (referred to as a hybrid plan optimization approach) for selecting optimal alternatives for mega projects. The suggested approach combines the A* graph search algorithm with genetic algorithms (GA) to analyze all possible mega project alternatives and their trade-offs and determine the optimal solution. To demonstrate its effectiveness, two decision scenarios are introduced, using this suggested decision support system, to validate the proposed process for selecting mega project # Corresponding Author. Address: Room 229, SDGC,Y2E2 Bldg., 473 Via Ortega Rd, Stanford, CA 94305, USA. E-mail addresses: [email protected](K.-M. Li), [email protected](J. Wang), [email protected] or [email protected] (Y.-S Zheng), [email protected](L. Wang), [email protected] (R. Orr), [email protected] (Y.-K. Juan).