an efficient genetic algorithm for solving the multi-mode resource-constrained project scheduling problem based on random key representation

in this paper, a new genetic algorithm (ga) is presented for solving the multi-mode resource-constrained project scheduling problem (mrcpsp) with minimization of project makespan as the objective subject to resource and precedence constraints. a random key and the related mode list (ml) representation scheme are used as encoding schemes and the multi-mode serial schedule generation scheme (mssgs) is considered as the decoding procedure. in this paper, a simple, efficient fitness function is proposed which has better performance compared to the other fitness functions in the literature. defining a new mutation operator for ml is the other contribution of the current study. comparing the results of the proposed ga with other approaches using the well-known benchmark sets in psplib validates the effectiveness of the proposed algorithm to solve the mrcpsp.