I metalworking ,rollingis a metal forming rocess in which metal tock is passed through one or more pairs ofrollto reduce the thickness and to make the thickness. Rolling is (metallurgy)" href="https://en.wikipedia.org/wiki/Recrystallization_(metallurgy)" recrystallization temperature, then the process is known ahot rolling. If the temperature of the metal is below its recrystallization temperature, the process is known acold rolling. In the hot strip mills, a typical challenge is the rolling time schedule, which plays an important role in cost reduction, delivery time, and quality improvement. In this research, optimum design of scheduling problem in hot rolling is investigated. This problem includes selecting a number of slabs from slab yard and determining their best sequence for sending into hot rolling mill. Since this feature requires the study of all possible permutation in slab selection, the complexity of the problem space is very large. Thus, the solution for this optimization problem is very complex. The problem has been designed based on the production conditions in the largest steel production facility in Iran, Mobarakeh Steel Complex (MSC). In this study, in contrast to the previous studies that concentrated on the rolling timing schedule, mechanical considerations are focused as well. The mechanical wear parameter is chosen as the main cause to terminate each program in the optimization problem. Mathematical formulation of the problem is performed in MATLAB software using the actual data from the mill. Four different formulations are introduced. Formulation uses the basic idea of Travelling Salesman Problem (TSP) and three dynamic packages that are programmed by the author. Thetravelling salesman problemis a science" href="https://simple.wikipedia.org/wiki/Computer_science" computer science . It is focused o optimization . In this contextetter solutiooften meaa solution that is cheaper. It is most easily expressed as a graph describing the locations of a set of nodes. The first formulation uses binary variables. The second formulation uses integer variables, third formulation checks out smaller categories of all slabs permutations and the forth, checks out all the possible permutations.To solve the optimization problem, genetic algorithm is utilized. In this study, two different objective functions are used. Maximizing the number of slabs is chosen as the first objective function and minimizing the wear crown of the work roll is chosen as the second objective function. Moreover, reducing width jump and thickness jump are considered as the optimization constraints. To reduce the computation time, penalty method is used for replacing a constrained optimization problem to unconstrained problem. At the end, the accuracy and efficiency of the offered program is compared with other programs being used in the hot strip mill unit of the Mobarakeh Steel Complex. The result of this comparison shows that the created model, compared with previous models, reduces wear and, thereby, reduces costs and increases production efficiency. Keywords: Hot rolling, Scheduling, Wear crown, Optimization, Genetic algorithm, Traveling salesman problem, Penalty function, Slabs Sequence, permutation