Interval multi-attribute bottleneck identification in job shop

Aiming at the Job shop bottleneck identification problem under random disturbance resulting in the difficulty of obtaining the determinate value of machine feature attribute, interval form was used to describe these uncertain attributes of machine. Furthermore, a new interval multi-attribute bottleneck identification model was established, and an interval TOPSIS bottleneck identification approach was proposed. By considering the close relationship between bottleneck utilization and bottleneck identification, an integrated framework, under which they could be solved simultaneously, was presented. This framework included two layers. In the first layer of bottleneck utilization, the Plant-Simulation platform was used to simulate random disturbance including equipment failure. Genetic algorithm (GA) was applied to perform optimization and simulation for the scheduling problems under the random disturbance and the optimum scheduling solution was obtained. In the second layer of bottleneck identification, based on scheduling optimization, interval TOPSIS bottleneck identification approach was proposed to identify bottleneck machines with considerations of multiple feature attributes. Comparing the proposed approach with machine utilization, bottleneck occurrence rate and shifting bottleneck detection method in the existing literatures, the results demonstrated the effectiveness of this approach. Finally, the influence of machining cost and material cost on bottleneck identification was analyzed.