Dual Resource Constrained Flexible Job Shop Scheduling Based on Improved Quantum Genetic Algorithm

Abstract

Aiming at solving the problem of dual resource constrained flexible job shop scheduling problem (DRCFJSP) with differences in operating time between operators, an artificial intelligence (AI)-based DRCFJSP optimization model is developed in this paper. This model introduces the differences between the loading and unloading operation time of workers before and after the process. Subsequently, the quantum genetic algorithm (QGA) is used as the carrier; the process is coded through quantum coding; and the niche technology is used to initialize the population, adaptive rotation angle, and quantum mutation strategy to improve the efficiency of the QGA and avoid premature convergence. Lastly, through the Kacem standard calculation example and the reliability analysis of the factory workshop processing process example, performance evaluation is conducted to show that the improved QGA has good convergence and does not fall into premature ability, the improved QGA can solve the problem of reasonable deployment of machines and personnel in the workshop, and the proposed method is more effective for the DRCFJSP than some existing methods. The findings can provide a good theoretical basis for actual production and application.

Mine water cooperative optimal scheduling based on improved genetic algorithm

“…After the final genetic operation, replace the bottom 10% of individuals with the lowest fitness value in the population. This strategy can swiftly eliminate less fit individuals in the population, accelerating the convergence rate and steering the entire population towards a more favorable direction [31] . The roulette selection method is employed, where selection is based on the proportion of an individual's fitness value in the total population.…”

Adaptive Population NSGA-III with Dual Control Strategy for Flexible Job Shop Scheduling Problem with the Consideration of Energy Consumption and Weight

“…Different operations of different jobs have different processing energy consumptions on different machines. The calculation of TPEC is not given in Equation (15).…”

“…In China, manufacturing companies consumed more than 50% of the national electrical energy and generated at least 26% of the total carbon dioxide emission. As the primary industry of the national economy, the manufacturing industry plays a crucial role in developing the national economy [14,15]. Still, it is also the main body of energy consumption and environmental pollution [16,17].…”

Research on Optimization of Dual-Resource Batch Scheduling in Flexible Job Shop

“…Scholars such as Li et al [9] believed that workers in flexible workshops, as flexible production resources like machines, would have a huge impact on the production environment, so they proposed a water wave optimization decisionmaking method with the lowest total energy consumption as the optimization goal, which was verified by a large number of simulation experiments. Zhang et al [10] considered the different loading and unloading times of workers due to individual factors and improved the quantum genetic algorithm through strategies such as niche, adaptive rotation angle, and quantum mutation, which improved the computational efficiency of the algorithm.…”