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.

This paper considers a single machine scheduling problem where the machine is shared by multiple sub-production systems. Each sub-production systems has heterogeneous local objectives (e.g., minimization of total completion time, maximum tardiness and makespan). In a distributed manufacturing environment, no sub-production system has complete information (e.g., processing time, due date) of the entire system. This paper provides a distributed scheduling method to find close-to-optimal coordination on the shared machine using minimum local information sharing among sub-production systems. The proposed method is compared to pareto solution that can be found in a centralized environment.