Nowadays, managing and allocating resources for projects has becomeincreasingly essential for managers. A critical factor affecting the success of a projectis the work assignment plan for workers to optimize the completion time. Currentsolutions to project scheduling problems have not been thoroughly addressed
thus,in this study, we model the labor assignment process in project production as ascheduling problem. To solve this problem, we use an improved genetic algorithmnamed GA-RT (Genetic Algorithm with Random Crossover and NegativeTournament Selection) and conduct experiments on the iMOPSE standard dataset.Experimental results show that the proposed GA-RT algorithm can effectively solvethe project scheduling problem, achieving better performance compared toexisting algorithmsNowadays, managing and allocating resources for projects has becomeincreasingly essential for managers. A critical factor affecting the success of a projectis the work assignment plan for workers to optimize the completion time. Currentsolutions to project scheduling problems have not been thoroughly addressed
thus,in this study, we model the labor assignment process in project production as ascheduling problem. To solve this problem, we use an improved genetic algorithmnamed GA-RT (Genetic Algorithm with Random Crossover and NegativeTournament Selection) and conduct experiments on the iMOPSE standard dataset.Experimental results show that the proposed GA-RT algorithm can effectively solvethe project scheduling problem, achieving better performance compared toexisting algorithms