Explore the vast range of titles available.

The processing-transportation composite robots, with their dual functions of processing and transportation, as well as comprehensive robot-machine interactions, have been widely and efficiently applied in the manufacturing industry, leading to a continuous increase in energy consumption. Hence, this work focuses on investigating robot-machine integrated energy-efficient scheduling in flexible job shop environments. To address the new problem, an innovative mixed-integer linear programming model and a novel dual-self-learning co-evolutionary algorithm are proposed, aimed at minimizing the total energy consumption and makespan. In the proposed algorithm, a three-dimensional vector is first used to comprehensively express the solution, and then a greedy decoding strategy is designed to reduce the idle time and energy consumption simultaneously. A hybrid initialization method with adaptive random selection and chaos mapping is developed to ensure the diversity and high quality of the initial solutions. A dual-self-learning mechanism, including a self-learning evolutionary mechanism and a self-learning cooperation mechanism, is designed to select suitable evolutionary operators and enhance interactions between populations, respectively. Finally, multiple sets of experiments are conducted to demonstrate the effectiveness of the proposed mathematical model, improved components and algorithm through numerical, statistical, and differential analyses.

Abstract 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. Graphical Abstract Graphical Abstract Problem description and solution process of IPTS-PTCRs.