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Matheuristic co-evolutionary algorithm for solving the integrated processing and transportation scheduling problem with processing-transportation composite robots

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Publicado en:Journal of Computational Design and Engineering vol. 12, no. 9 (Sep 2025), p. 131-162
Autor principal: Zhang, Meizhou
Otros Autores: Zhou, Min, Zhang, Liping, Zhang, Zikai
Publicado:
Oxford University Press
Materias:
Mathematical programming
Linear programming
Scheduling
Robots
Collaboration
Mixed integer
Integer programming
Operators (mathematics)
Genetic algorithms
Numerical analysis
Evolutionary algorithms
Acceso en línea:Citation/Abstract
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Resumen:With the rapid development of robotic technology, a new type of robot, the processing-transportation composite robot (PTCR), has been widely applied in manufacturing systems. It has multiple functions, such as transferring jobs between machines and processing tasks, thereby greatly enhancing production flexibility. Hence, this study investigates the integrated processing and transportation scheduling problem with PTCRs (IPTS-PTCRs) in a job shop environment to minimise the makespan. A mixed-integer linear programming (MILP) model is first designed to define this complex problem. Then, a hybrid algorithm incorporating mathematical programming and a collaborative evolutionary mechanism is designed to solve the model, named the matheuristic co-evolutionary algorithm (MCEA). This algorithm combines multiple heuristics with a random method, resulting in a two-stage collaborative initialisation that generates a high-quality and diverse initial population. A novel collaborative evolutionary mechanism is incorporated into the crossover and mutation operators to enhance interactions between sub-populations. A novel local search based on adaptive decomposed MILP is developed to conduct an in-depth exploration of the best solution. Finally, multiple sets of experiments are conducted to validate the effectiveness of the proposed MILP model and MCEA. The experimental results show that the MILP model can obtain optimal solutions for small-scale instances. The improved components enhance the average performance of the MCEA by 44.1%. The proposed MCEA outperforms five state-of-the-art algorithms in terms of numerical analysis, statistical testing, differential comparison, and stability evaluation.
ISSN:2288-5048
DOI:10.1093/jcde/qwaf089
Fuente:Engineering Database