Bound-guided hybrid estimation of distribution algorithm for energy-efficient robotic assembly line balancing

Under the pressure of climate change, energy-efficient manufacturing has attracted much attention. Robotic assembly lines are widely-used in automotive and electronic manufacturing. It is necessary to consider the energy saving and economic criteria simultaneously when robots are utilized to operate assembly tasks replacing human labor. This paper addresses an energy-efficient robotic assembly line balancing (EERALB) problem with the criteria to minimize both the cycle time and total energy consumption. We present a multi-objective mathematical model and propose a bound-guided hybrid estimation of distribution algorithm to solve the problem. When designing the optimization algorithm, we adopt estimation of distribution algorithm (EDA) to tackle the task assignment, and design a non-dominated robot allocation (NGRA) heuristic which is embedded into the EDA to allocate suitable robot to each workstation. Moreover, we propose a bound-guided sampling (BGS) method, which is able to reduce the search space of EDA and focus the search on the promising area. The computational complexity of the proposed algorithm is analyzed and the effectiveness of the proposed NGRA and BGS is tested. In addition, we compare the performances of the proposed mathematical model and the proposed algorithm with those of the existing model and algorithms on a set of widely-used benchmark instances. Comparative results demonstrate the effectiveness of the proposed model and algorithm.