A robotrconveyor system is an important part of the manufacturing system. As such, the robotrconveyor system must comply with all the requirements of a modern manufacturing system: high flexibility, high efficiency, and smart reasoning processes to lead the system to its next status based on its current status and varied inputs. An expert system consisting of a knowledge base and an inference engine was developed to operate and control a robotrconveyor system. A large variety of robotrconveyor system runs were performed to explore the efficiency of its operations. These system runs explore all the parameters associated with robotrconveyor system operations: manipulators specifications and velocities, pick-up methods, types of arriving parts, their locations, and orientations. The performed runs verified a well-known fact that a robotrconveyor system based on variable pick-up locations is more efficient than a robotrconveyor system based on a single, predefined, and unchanged pick-up location. The implementations of the variable pick-up locations method is associated with the need to implement real-time complicated software algorithms to track the arriving parts and to synchronize their maneuvers with the conveyor belt. Therefore, the implementation of variable pick-up locations method must be performed only when they result in most significant benefits. The system runs show that the benefits of the variable pick-up locations approach are inversely related to manipulations' velocities. If the variable pick-up location method is incorporated with efficient and reliable tracking algorithms, most efficient robotrconveyor systems will be resulted. Therefore, future efforts should address the advance of tracking algorithms of arriving parts, thus improving the synchronization of these parts with manipulator's maneuver, in order to achieve the most efficient robotrconveyor system.