One of the most important managers’ concerns about production or service systems in global competitive market is superiority over competitors via attracting and retaining customers. If the delivery duration of products exceeds the due date, customer dissatisfaction and his/her tendency to competitor companies in addition to imposing extra costs could take place. Therefore, managers tend to minimize tardiness measures, such as total tardiness, weighted total tardiness, maximum tardiness, and number of tardy jobs. Also, since importance of tardiness of jobs could differ due to having special customer and product types, different jobs could have different tardiness weights. Furthermore, production managers tend to reduce final products inventories due to high storage costs. As a result, production managers want to minimize earliness measures, such as total earliness, weighted total earliness, and maximum earliness. Also, since importance of earliness of jobs are different due to having different storage costs and different perishability rates, different earliness weights are assigned to different jobs. For this reason, two criteria of maximum weighted earliness and number of tardy jobs are considered in this thesis. Also, since maximum weighted earliness and weighted number of tardy jobs measures don’t have the same dimensions, the weighted normalized summation of the two criteria is considered as the objective function of the problem. The purpose of this study is minimizing normalized weighted summation of maximum weighted earliness and weighted number of tardy jobs in single machine environment. For this purpose, an optimal algorithm is developed for the problem of minimizing the maximum weighted earliness in single machine environment. Then a ltr"
