Model and Algorithms for Balancing and Sequencing of Assembly Lines with Collaborative Robots

Abstract

Collaborative robots (cobots) have been increasingly utilized in assembly lines to assist the human workers or complete the assembly tasks independently to reduce the human worker cost. Two mixed-integer programming models are formulated to optimize the cycle time in assembly lines with different types of cobots and parallel collaboration. These two models are capable of solving the small-size instances optimally. Meanwhile, an improved artificial bee colony algorithm (IABC) and an improved migrating birds optimization algorithm (IMBO) are also developed to solve large-size problems. The proposed algorithms propose two vectors (task assignment vector and process alternative vector) for encoding, and develop a decoding procedure with a mathematical programming approach to obtain a feasible scheduling scheme. Specifically, IABC proposes improved onlooker phase toaccelerate the evolution of the whole population, improved scout phase to achieve new high-quality solution, and local search phase to enhance the exploitation capability. IMBO utilizes improved leader improvement phase and population improvement phase to accelerate the evolution of the whole swarm and avoid being trapping in local optimum, and a restart mechanism to enhance the algorithm’s exploration ability. To evaluate the proposed methods, they are compared with simulated annealing algorithm, late acceptance hill-climbing algorithm, genetic algorithm, particle swarm algorithm, original artificial bee colony algorithm, and original migrating birds optimization algorithm. Computational study demonstrates that the proposed methods outperform the original ones and achieve promising performance in comparison with other methods

Keywords: Assembly Line Balancing, Human-robot collaboration, mathematical programming, Migrating birds optimization algorithm, Artificial Bee Colony algorithm