Cost-Time Optimization of a Construction project using Genetic-Algorithm approach

Abstract

People's daily necessities are provided for both directly and indirectly by the construction sector. A construction project often entails the use of various resources (such as equipment, materials, labour, etc.) to create a finished product (such as a building, a bridge, a water distribution system, etc.) that meets the needs of the intended consumers. Budget restrictions, contractual time restrictions, safety and health concerns, sustainability ratings, local building rules, and the desired degree of quality are just a few of the challenges faced in construction projects. As a result, there are several goals for a construction project, including maximum productivity, lowest cost, shortest duration, and specified quality, safety, and sustainability. When attempting to combine multiple objectives into the best possible answer, decision-making can be challenging. In this thesis, a GA model with 152 decision variables, 462 constraints, and 361 optimal solutions was suggested. This study suggests dynamic programming-based GA for equipment management issues because we thought it would be able to address these issues more effectively than conventional approaches. The method's objectives were to reduce overall project costs and increase equipment performance so that, in the event of equipment failure, backup equipment would be accessible. Furthermore, it was decided to treat the equipment failure rate as a fuzzy variable to increase the method's dependability. The model was put to the test using a real hydroelectric project in India. The new approach outperformed the conventional methods in searching in the same environment.