ORTIZ-AGUILAR, Lucero; XOCA-OROZCO, Luis Ángel    ANDA-SUAREZ, Juan de. Design of Routes for Collaborative Robotsin the Automobile Painting Processthrough a Comparison of Perturbative Heuristicsfor Iterated Local Search. Comp. y Sist. []. 2024, 28, 2, pp.803-820.   31--2024. ISSN 2007-9737.  https://doi.org/10.13053/cys-28-2-5024.

The automotive manufacturing industry faces diverse challenges, including designing new parts, optimizing schedule times, developing aerodynamic car designs, refining painting processes, and advancing autonomous driving. The car painting process is a new optimization area in the computational context due to the complexity and damage caused by various factors. Our research focuses on designing a robotic arm with five degrees of freedom that operates in a two-dimensional plane and is integrated with metaheuristics for path optimization. Our methodology consists of defining and limiting the problem, analyzing requirements, designing the robotic arm, implementing routes, and conducting tests. For the design of data instances, the Methodology proposed by [42] was used in this work. Subsequently, a pool of perturbation heuristics and an iterated local search algorithm are used, which allowed us to design the best combination of heuristics that can provide a competitive solution to the problem of route design for the robotic arm in the automobile painting process. This study includes a comprehensive review of related work, theoretical concepts, and the application of metaheuristics. The results highlight the effectiveness of the proposed heuristics, with the K-OPT heuristic demonstrating superior performance. Statistical tests confirm the significance of the differences among the heuristics. This paper concludes with insights into future research directions, emphasizing the importance of safety practices and Industry 4.0 technology in mitigating health risks associated with the automotive painting process.

: Iterated local search; routing design; heuristics.

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