: Supply chain is set of factors that are in collaboration to produce a product or service for customers. Supply chain management is an important subject which is theoretically and applicably considered by researchers for years. This subject is very widespread and variable, so it has denoted a lot of researches to itself. One of the main purposes that this subject has been noticed widely in literature is integrating and coordinating decisions in supply chain. Production and distribution are two crucial operations in supply chain which should be scheduled integrated to achieve an optimum efficiency. Considering the importance of total weighted number of tardy jobs, in this thesis exact and approximate methods are proposed to solve the problem of integrated production and distribution scheduling in supply chain with Vehicle routing Problem (VRP) to minimize total weighted number of tardy jobs and delivery costs. This problem which is strongly NP-Hard is considered for the first time. In this thesis a mixed integer programing (MIP) model is proposed to solve the problem. Because of high complexity of the problem, MIP model is not able to solve big instances in a reasonable time. For this reason, a heuristic algorithm and two metaheuristic algorithms including Genetic Algorithm (GA) and Tabu Search (TS) are proposed to solve this problem. Also computational tests are used to survey the efficiency of proposed algorithms. ANalysis Of VAriance (ANOVA) is used to analyze the results. The computational results show that our heuristic and metaheuristic algorithms are efficient. Computational results show that GA outperforms TS in both large and small instances.