The two-sided disassembly line is popular for its high-efficiency disassembly of large-volume end-of-life products. However, in the process of two-sided disassembly, some parts and components need to be disassembled in parallel, and the uncertainty of disassembly time lacks certain research. This paper constructs a fuzzy multiobjective two-sided disassembly line balance problem model based on parallel operation constraint, which aims to reduce the balance loss rate, smoothness index, and energy consumption of disassembly activities. A multiobjective flatworm algorithm based on the Pareto-dominance relationship is developed. To increase the diversity of feasible solutions in the evolution process and accelerate the convergence of Pareto-optimal solutions to prevent the random search of solution space, growth, splitting and regeneration mechanisms are embedded in the algorithm. The working mechanism and efficiency of the multiobjective flatworm algorithm are proved on a series of two-sided disassembly cases, and the excellent performance of the proposed model and algorithm are demonstrated by an actual automobile two-sided disassembly line.

The disassembly industry is still labor-intensive, and for the disassembly of products with similar assembly structures, achieving mixed disassembly of these products on the same disassembly line is a more economical solution and very popular in disassembly companies. When large-volume end-of-life (EOL) products are disassembled on the two-sided disassembly line, there is uncertainty in the disassembly time during the disassembly process due to the differences in the recovery quality of EOL products. Therefore, a stochastic mixed-integer programming model is formulated that considers workstation activation cost, workload smoothness index between workstations, and total hazard index considering the order of hazardous task removal. Then, a multi-objective genetic flatworm algorithm is developed in which two different mechanisms for new individual generation are embedded, namely the flatworm genetic operations and the regeneration operations. The effectiveness of the model and the efficiency of the algorithm are verified by the disassembly cases of the printer and the classical mixed-model on the two-sided disassembly line. Finally, the proposed model and algorithm are applied to the disassembly of multiple types of cars, which proves their practical industrial application value.

Precast concrete components have attracted a lot of attention due to their efficient production on off-site production lines. However, in the precast component production process, unreasonable production sequence and mold layout will reduce production efficiency and affect the workload balance between each process. Due to the multi-species and small-lot production characteristics of precast concrete components, the number of molds corresponding to each precast concrete component is generally limited. In this paper, a production sequence and layout optimization model for assembling precast concrete components under a limited number of molds is proposed, aiming to improve the comprehensive utilization efficiency of the mold tables and balance the workload between each production process of precast components. In order to obtain a better production sequence and a richer combination of mold layout schemes, a multi-objective teaching-learning-based optimization algorithm based on the Pareto dominance relation is developed, and an enhancement mechanism is embedded in the proposed algorithm. To verify the superior performance of the enhanced teaching-learning-based optimization algorithm in improving the comprehensive utilization efficiency of the mold tables and balancing the workload between various processes, three different sizes of precast concrete component production cases are designed. The research results show that the proposed model and optimization algorithm can help production managers to efficiently formulate more reasonable precast component production sequence and layout schemes, especially for those enterprises that are struggling to improve the efficiency of precast concrete component production.

Precast concrete components, as one of the important structural systems in prefabricated buildings, have received widespread attention due to their efficient manufacturing characteristics on the production line. Their production sequence and layout on the mold table have a crucial impact on production energy consumption. However, a critical constraint is often overlooked in the first step of precast concrete manufacturing: the production sequence and layout of molds are planned without considering the limited availability of molds for each component type. Therefore, this article proposes a mixed-integer programming model for the production sequence and layout of precast concrete components under a limited number of molds, aiming to simultaneously minimize production energy consumption, fluctuation coefficients of mold table utilization, and mold switching time. To obtain high-quality solutions for production sequence and mold layout, a multi-objective genetic flatworm algorithm with a Tabu mapping mechanism is developed to efficiently determine the production sequence and the positions of molds on the mold tables. Through three production cases of precast concrete components with different scales, the proposed model and algorithm have been demonstrated to be highly effective in assisting decision-makers in quickly formulating the optimal production sequence and layout schemes for precast concrete components.

Product manufacturing quality is influenced by various factors of the production process, so the key to improve product manufacturing quality is improving the combination of the relevant parameters and processing methods in the manufacturing process. Aiming at this issue, a manufacturing quality improvement method using gene recombination and editing mechanism is proposed. In this method, an optimization model is established and described by formulas, in which three optimization objectives including production quality, costs, and time are involved. In the model, the quality indicator is measured by a comprehensive evaluation approach of product gene. To address the model, an improved genetic algorithm (GA) and artificial bee colony algorithm (ABC) with hybrid encoding scheme (H-IGA-IABC) is designed by considering the different types of gene elements. Fifteen comparison experiments with different scales are performed to test the model and H-IGA-IABC. According to the data obtained by different components and algorithms, the search ability, speed of convergence of H-IGA-IABC are better than that of other components and algorithms, especially in solving large-scale problems. Compared with the solution before optimization, the quality evaluation results and other indicators of the solutions after optimization are significantly better. Therefore, the proposed method is effective and performs well.

With the rise of assembled buildings, precast concrete components have become the focus of the industry, and their production scheduling as a key link to the healthy and green development of the industry plays a crucial role. Nowadays, with the diverse demands of the construction market, the production of components is characterised by multiple varieties and small batches, and covers multiple closely related processes from raw material supply to maintenance and demoulding, facing complex scheduling dilemmas, and the problems of rationally allocating the processing sequence of the workpieces, improving the utilisation rate of the equipment, and shortening the production cycle still exist. This study shows that on the premise of ensuring that the proposed production scheduling model meets the needs of the assembly construction site, using the calculation results of the simulated annealing method, the rational planning of the production sequence of the components and the allocation of resources can significantly reduce the completion time of the prefabricated components and improve their production efficiency.