This paper considers a job shop environment where machines are shared by several sub-production systems. The local objective of a sub-production system is the minimization of total completion time. In a centralized environment, a single decision maker has complete information of processing time, job routing and local objectives. In this case, the problem is a traditional job shop scheduling problem to minimize the total completion time which is well-known NP-hard problem. Meanwhile, it is assumed that no sub-production system has a complete view of the entire system in a distributed environment. This paper proposes a distributed scheduling methodology that maintains autonomy of each sub-production system while pursuing system-wide performance in job shop environment. The proposed method is compared to the performance of centralized solutions.