Job shop scheduling always plays an important role in the manufacturing process and is one of the decisive factors influencing manufacturing efficiency. In the actual process of production scheduling, there exist some uncertain events, information asymmetry and abnormal disturbance, which would cause the execution deviation and affect the efficiency and quality of scheduling execution. Traditional scheduling methods are not sufficient to solve the challenges well. Due to the rise of digital twin, which has the characters of virtual reality interaction, real time mapping and symbiotic evolution, a new job shop scheduling method based on digital twin is proposed to reduce the scheduling deviation. In this paper, the architecture and working principle of the new job shop scheduling mode are introduced. Then, scheduling resource parameter updating methods and dynamic interactive scheduling strategies are proposed to achieve real-time and precise scheduling. Finally, a prototype system is designed to verify the validity of this new job shop scheduling mode.

This paper considers the design and balancing of mixed-model disassembly lines with multi-robotic workstations under uncertainty. Tasks of different models are performed simultaneously by the robots which have different capacities for disassembly. The robots have unidentical task times and energy consumption respectively. Task precedence diagrams are used to model the precedence relations among tasks. Considering uncertainties in disassembly process, the task processing times are assumed to be interval numbers. A mixed-integer mathematical programming model is proposed to minimise the cycle time, peak workstation energy consumption, and total energy consumption. This model has a significant managerial implication in real-life disassembly line systems. Since the studied problem is known as NP-hard, a metaheuristic approach based on an evolutionary simulated annealing algorithm is developed. Computational experiments are conducted and the results demonstrate the proposed algorithm outperforms other multi-objective algorithms on optimisation quality and computational efficiency.

The objective of disassembly lines is to disassemble End-of-Life products in a remanufacturing field. The disassembly line balancing problem (DLBP) considers how to allocate disassembly operations to operators on the disassembly line to optimize predetermined goals, such as cycle time. In practice, various environmental uncertainties (e.g., uncertain product quality) exist in the disassembly line. These uncertainties entail DLBP essentially a dynamic multi-objective optimization problem. This study presents a dynamic DLBP (D-DLB) to model the effect of environmental uncertainties on the assignment of disassembly operations. Furthermore, a prediction-based dynamic optimization algorithm, termed domain generalization-based dynamic multi-objective evolutionary algorithm (DG-DMOEA), combining meta-learning with multi-objective optimization, is proposed to solve D-DLB. In DG-DMOEA, a meta-learning algorithm is employed to learn the parameters of a solution-generative model from the Pareto-optimal sets in all historical environments. Subsequently, the solution-generative model is applied to generate a high-quality initial population that can assist multi-objective optimization algorithms in finding the Pareto-optimal set in the new environment faster. Since no information in the new environment is required, learning can begin before the new environment arrives, significantly reducing computational time. Moreover, different solution-generative models can be designed for different DMOPs. Therefore, DG-DMOEA can thoroughly combine real-world problem properties to represent knowledge. The experimental results show that, compared with state-of-the-art methods, DG-DMOEA can considerably improve the quality of solutions and significantly enhance the ability to react quickly to environmental changes.