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