An Integrated Optimization Method for Resource-Constrained Schedule Compression Under Uncertainty in Construction Projects

An integrated solution that considers the shortening of scheduling and the planning of resource integration was conceived. The proposed method allocates the resources and the execution mode costs effectively in order to minimize the project duration and the cost of construction activities. Costs are managed based on the management of the costs already in place for people and those costs involved in the modes of execution of the project, trying to decrease the cost as much as possible. The proposed method is used in order to achieve the maximum potential and minimum costs during a project, including direct costs, indirect costs, and delay penalties. Furthermore, it finds a balance between the costs of acquiring and releasing human resources. The most interesting aspect of the proposed method is that it suggests addressing problems with resource planning and project scheduling simultaneously under uncertainty. FS theory is used to model project activity duration and cost uncertainty in the method. In addition, the above approach involves a genetic algorithm (GA) for schedule optimization. The optimization method utilizes a GA as an optimization approach to identify a set of non-dominated solutions. In this paper, we discuss how string-based multi-object optimization can be solved with ES using the elitist non-dominated sorting genetic algorithm (NSGA-II). The method is implemented in Python (v3.12.9), the computer programming language, as a standalone automated computational tool for schedule optimization in order to subsequently reschedule.