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Solving human-robot collaborative mixed-model two-sided assembly line balancing using multi-objective discrete artificial bee colony algorithm
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AI
DOI：10.1016/j.cie.2023.109776.png)
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Human-robot collaboration (HRC) is an emerging technology that aims to optimize production efficiency and reduce ergonomic risks by enabling humans and collaborative robots to work together in a shared workspace. This paper investigates HRC mixed-model two-sided assembly line balancing problem, which is a challenging real-world problem in manufacturing systems. The problem is formulated using a mixed-integer programming model with the objectives of minimizing ergonomic risk, energy consumption, and cycle time. To solve the problem efficiently, a multi-objective discrete artificial bee colony algorithm with specialist bees (MDABCSB) is proposed. MDABCSB utilizes a new five-vector encoding and decoding scheme and incorporates two types of neighbor structures for each vector. Additionally, the algorithm introduces specialist bees to search for the optimal solution for each objective, combines a tabu list to help them jump out of local optima, and employs variable neighborhood descent to enhance their exploration capabilities. Furthermore, the algorithm adopts a new temperature-based food sources update mechanism to probabilistically update the food sources of employed bees to reinforce exploitation. By comparing the proposed algorithm with other algorithms, the results demonstrate that MDABCSB performs better in terms of solution performance.
Keyword:
Two-sided assembly line balancing
Human -robot collaboration
Collaborative robots
Artificial bee colony algorithm
Ergonomic risk
Energy consumption
期刊
IF:
6.5
论文数:
1.0W
被引数:
3.8W
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Models and algorithms for U-shaped assembly line balancing problem with collaborative robots.png)
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