Optimized resource-constrained method for project schedule compression

Purpose: This paper aims to present an integrated method for optimized project duration and costs, considering the size and cost of crews assigned to project activities' execution modes. Design/methodology/approach: The proposed method utilizes fuzzy set theory (FSs) for modeling uncertainties associated with activities' duration and cost and genetic algorithm (GA) for optimizing project schedule. The method has four main modules that support two optimization methods: modeling uncertainty and defuzzification module; scheduling module; cost calculations module; and decision-support module. The first optimization method uses the elitist non-dominated sorting genetic algorithm (NSGA-II), while the second uses a dynamic weighted optimization genetic algorithm. The developed scheduling and optimization methods are coded in python as a stand-alone automated computerized tool to facilitate the developed method's application. Findings: The developed method is applied to a numerical example to demonstrate its use and illustrate its capabilities. The method was validated using a multi-layered comparative analysis that involves performance evaluation, statistical comparisons and stability evaluation. Results indicated that NSGA-II outperformed the weighted optimization method, resulting in a better global optimum solution, which avoided local minima entrapment. Moreover, the developed method was constructed under a deterministic scenario to evaluate its performance in finding optimal solutions against the previously developed literature methods. Results showed the developed method's superiority in finding a better optimal set of solutions in a reasonable processing time. Originality/value: The novelty of the proposed method lies in its capacity to consider resource planning and project scheduling under uncertainty simultaneously while accounting for activity splitting.