To meet the increasingly diversified demand of customers, more mixed-flow shops are employed. The flexibility of mixed-flow shops increases the difficulty of scheduling. In this paper, a mixed-flow shop scheduling approach (MFSS) is proposed to minimise the energy consumption and tardiness fine (TF) of production with a special focus on non-processing energy (NPE) reduction. The proposed approach consists of two parts: firstly, a mathematic model is developed to describe how NPE and TF can be determined with a specific schedule; then, a multi-objective evolutionary algorithm with multi-chromosomes (MCEAs) is developed to obtain the optimal solutions considering the NPE-TF trade-offs. A deterministic search method with boundary (DSB) and a non-dominated sorting genetic algorithm (NSGA) are employed to validate the developed MCEA. Finally, a case study on an extrusion die mixed-flow shop is performed to demonstrate the proposed approach in industrial practice. Compared with three traditional scheduling approaches, the better performance of the MFSS in terms of computational time and solution quality could be demonstrated.

The evolution and application of intelligence have been discussed from perspectives of life, control theory and artificial intelligence. However, there has been no consensus on understanding the evolution of intelligence. In this study, we propose a Tri-X Intelligence (TI) model, aimed at providing a comprehensive perspective to understand complex intelligence and the implementation of intelligent systems. In this work, the essence and evolution of intelligent systems (or system intelligentization) are analyzed and discussed from multiple perspectives and at different stages (Type I, Type II and Type III), based on a Tri-X Intelligence model. Elemental intelligence based on scientific effects (e.g., conscious humans, cyber entities and physical objects) is at the primitive level of intelligence (Type I). Integrated intelligence formed by two-element integration (e.g., human-cyber systems and cyber-physical systems) is at the normal level of intelligence (Type II). Complex intelligence formed by ternary-interaction (e.g., a human-cyber-physical system) is at the dynamic level of intelligence (Type III). Representative cases are analyzed to deepen the understanding of intelligent systems and their future implementation, such as in intelligent manufacturing. This work provides a systematic scheme, and technical supports, to understand and develop intelligent systems.