Intra- and inter-Serus job rotation scheduling through Teaching and Learning Based Optimization approach

Seru systems are among the most advanced repetitive manufacturing systems which produce in low or medium volumes. These systems consist of several cells and get the best flow time through the new methods of assigning operators. In the literature, more attention is paid to the aspects of the cells formation and allocation and sequencing of the various products into the cells, and the assignment of operators, which is considered to be the most important element in these systems, has not been studied in line with other decisions. Therefore, in this paper, these decisions have been studied independently and analyzed through intra and inter serus job rotation scheduling. The presented ILP model assigns a given set of operators to the cells so that the total number of stays in a cell in successive rotation periods be minimized. GAMS software has been used to solve the model and also, the teaching and learning based optimization algorithm has been designed to improve the efficiency for problems of medium and large sizes. Several test problems have been generated and solved in a variety of sizes to examine the validity of the model and the performance of the algorithm. Results show the proper efficiency of the algorithm and quality of its solutions.