solving the problem of scheduling unrelated parallel machines with limited access to jobs

nowadays, by successful application of on time production concept in other concepts like production management and storage, the need to complete the processing of jobs in their delivery time is considered a key issue in industrial environments. unrelated parallel machines scheduling is a general mood of classic problems of parallel machines. in some of the applications of unrelated parallel machines scheduling, when machines have different technological levels and are not necessarily able to process each one of the existing jobs in the group of jobs and in many of the industrial environments, a sequence dependent setup time takes place during exchanging jobs on the machines. in this research, the unrelated parallel machines scheduling problem has been studied considering the limitations of sequence dependent setup time of processing of jobs and limited accessibility to machines and jobs with the purpose of minimizing the total weighting lateness and earliness times. an integer scheduling model is proposed for this problem. also, a meta-heuristically combined method consisting of genetic algorithm and particle swarm optimization (pso) algorithm for its solutions is proposed. the obtained results of the proposed algorithm show that the proposed algorithm is very efficient especially in problems with large dimensions.