Robotic stochastic assembly line balancing

To keep up with the Industry 4.0 technological revolution and get the upper hand over competitors, manufacturing companies replace human workers with robots in their assembly processes. A popular approach in the manufacturing industry is to design an assembly line with human-robot collaboration. In this study, we investigate a robotic stochastic assembly line balancing problem (RSALBP), with the motivation to observe the effects of robots on the cycle time in stochastic assembly lines where human workers and robots operate in different workstations. In the literature, robotic assembly line balancing is only studied with deterministic task times. However, assembly line balancing contains stochastic processes in real life. We assume that the processing time of each task follows a normal distribution whose parameters depend on the type of the operator performing the task with robots having much less (possibly zero) variation in task times than human workers. It is assumed that human workers are fully capable while robots are able to perform a subset of the tasks. We study type-II RSALBP which aims to minimize the cycle time for an assembly line with stochastic task times, given a fixed number of workstations and robots. This problem is NP-hard and includes non-linearity. We propose a mixed-integer second-order cone programming formulation and a constraint programming formulation to solve the problem. Instances from the literature are used to test the effectiveness of the proposed formulations. Additionally, the effects of robots on cycle times are evaluated by conducting a computational study with a comprehensive experimental design.