Cost-oriented robotic assembly line balancing problem with setup times: multi-objective algorithms

Robots are extensively used during the era of Industry 4.0 to achieve high productivity, better quality and lower cost. While designing a robotic assembly line, production managers are concerned about the cost involved in such a system development. Most of the research reported till date did not consider purchasing cost while optimizing the design of a robotic assembly line. This study presents the first attempt to study the cost-oriented robotic assembly line balancing problem with setup times to minimize the cycle time and total purchasing cost simultaneously. A mixed-integer linear programming model is developed to formulate this problem. The elitist non-dominated sorting genetic algorithm (NSGA-II) and improved multi-objective artificial bee colony (IMABC) algorithm are developed to achieve a set of Pareto solutions for the production managers to utilize for selecting the better design solution. The proposed IMABC develops new employed bee phase and scout phase, which selects one solution in the permanent Pareto archive to replace the abandoned solution, to enhance exploration and exploitation. The comparative study on a set of generated instances demonstrates that the proposed model is capable of achieving the proper tradeoff between line efficiency and purchasing cost, and the proposed NSGA-II and IMABC achieve competing performance in comparison with several other multi-objective algorithms.