Research on Layout Optimization of Robot Packaging Production Line Based on NSGA-II Algorithm

A Probabilistic One-Step Approach to the Optimal Product Line Design Problem Using Conjoint and Cost Data

Simulation is the key technology to enhance the product and production engineering processes. This paper presents an example of application of the simulation to the virtual design and optimization of a industrial production line.

Simulation and optimization techniques are the pillars of for the Virtual Commissioning of modern digital factories. In particular, industrial production lines are complex systems formed by a set of machines that have several cross-dependencies (i. e., the production efficiency of a machine impact deeply performance figures of the others and affects the whole line productivity). The simulation and optimization of production lines is an integrated approach that allows finding efficiently optimized production parameters. This paper presents an example of application of the simulation to the virtual design and optimization of an industrial production line.

Purpose The purpose of this paper is to investigate the process optimization of a precast concrete component production line by using value stream mapping. Design/methodology/approach This paper is an empirical focused on of lean production theory and value stream mapping. The data in the case study were collected in real time on-site for each process during the production process of a prefabricated exterior wall. Findings The results of the current value stream map indicate that the main problems of the current production process are related to equipment, technology and organization. The equipment problems include simple demolding and cleaning tools and the lack of professional transfer channels. The technology problems include the lack of a marking mechanism and pipeline exit mechanism. There is a lack of standard operating procedures and incomplete process convergence. A comparison and analysis of the current value stream and the future value flow indicate that optimizations of the process flow, the production line layout, and the standard operating procedures have shortened the delivery cycle, reduced the number of workers, improved the operator’s operating level and balanced the production line. Practical implications The results of this study provide practitioners with a clear understanding of the optimization of the precast concrete component production and represent a method and basis for the process optimization of a factory production line; the approach is suitable for process optimization in other areas. Originality/value This research represents an innovative application of lean production theory and value stream mapping in a complex production line of precast concrete components and thereby fills the gap between the theory and practice of the optimization of a precast concrete component production line.

Aiming at the problem of reasonable scheduling of maintainer for railway freight trains, aiming at the maximum maintenance completion time and the work intensity of maintainer. An improved NSGA-II algorithm is proposed based on parallel machine scheduling. By using the ways of improving the initialization method, enlarging the population size, and employing pseudo-fitness function into the elite retention strategy solve the problems that the traditional NSGA-II algorithm is easy to fall into the local optimum and the convergence speed is slow. Finally, taking the actual maintenance of railway carriage of a depot as the research object, the improved NSGA-II algorithm and the traditional NSGA-II algorithm were respectively used to solve the model. The effectiveness of the algorithm is verified by comparing the optimization results with the actual production data.

In many cases industrial companies’ efficiency is limited by bad man-machine collaboration. In the time of intelligent manufacturing, more data can be collected and help companies to improve their efficiency. Therefore, a production line optimization method based on Man-Machine collaboration with the help of big data tools and sensors is introduced. At first, the production line will be analyzed and main data as machine cycle time, personnel operation time, task distribution etc. will be collected. Then based on these data, a simulation model will be made in plant simulation. After that, the performance of the production line will be evaluated considering the man-machine collaboration, work task distribution etc. and the optimization will be made. Afterwords, the machine cycle time, operator motion track and labor operation time in real production situation will be collected to compare and validate the optimization model.

Prefabricated components production line optimization is critical for improving industrialized building construction efficiency; however, few studies focus on the production line optimization problem in context of industrialized building construction. In order to optimize the large random orders in the prefabricated components production process, this research proposes a model to minimize variance of the production capacity utilization of prefabricated components in the production cycle, and the ant colony optimization algorithm is introduced to solve the mixed production line sequencing optimization problem. By optimizing the sequence, the production capacity of the component production is balanced, and the capacity utilization rate in the industrialized building construction process is improved. Finally, the effectiveness of the method is verified through a real case of fabricated building components production. The results show that the variance of daily production capacity utilization rate of the optimized hybrid component production line has reduced to 0.53%, which is significantly lower than the 2.45% before optimization. The proposed model could effectively achieve the production capacity balance of prefabricated components production line.

In order to solve the problems such as poor diversity and poor convergence ability of the offspring population of the NSGA-II Algorithm in the vehicle production scheduling problem, an improved shop scheduling algorithm based on NSGA-II is proposed. The improved NSGA-ii Algorithm mainly focuses on the crossover and mutation of the traditional NSGA-II Algorithm, and proposes a new improved self-adaptive Crossover and mutation operator. By comparing the individual crowding degree with the average crowding degree of the population, and combining the iterative evolution process of the population, in order to avoid blind orientation and to improve the convergence speed of the population, the genetic probability is correlated with the individuals and the evolution iteration times of the population, and a new uniform evolution strategy is proposed to select the individuals of the population through adaptive hierarchical selection, in order to improve the quality of the solution, the problem of the poor diversity of the offspring population was solved. The improved NSGA-II Algorithm is used to carry out the experimental simulation analysis. The effectiveness of the proposed algorithm is verified by comparing the optimization results before and after the improvement.

The NSGA-II algorithm is widely used in multi-objective optimization problems, but the traditional NSGA-II algorithm has some shortcomings such as large computational cost and poor convergence in some complex practical problems. To solve above defections, an improved NSGA-II algorithm is proposed in this paper. Firstly, the specific crossover and mutation operators are designed. Secondly, a novel elitist strategy is developed as well. Then, the simulations of the standard test functions are carried out, the results illustrate that the improved strategies can effectively enhance the convergence and operation speed of the traditional algorithm. Finally, in order to test the practicality of the algorithm, a multi-objective mathematical model for charge plan of steelmaking is established. Simulation is carried out with real industry data. The results show that the algorithm is practical for charge scheduling.

A profit maximizing mathematical model for pricing and selecting optimal product line

The production process of helicopter composite main blade is complex, with many procedures and large difference in process time. It leads the production line to run unevenly while the production capacity may not meet the delivery requirements. In order to solve this problem, the process of composite main blade is analyzed and the current production data is collected. On this basis, the production line of helicopter composite main blade is modeled and simulated by using simulation technology of production line based on DELMIA/Quest software. Key factors such as capacity, equipment utilization and bottlenecks of the production line are analyzed. In order to solve the problems found in the simulation, the improvement scheme is proposed from the perspectives of production shift, process and equipment configuration and verified by simulation. The simulation results provide the basis for the evaluation and optimization of composite main blade production line.

Research on intelligent workshop resource scheduling method based on improved NSGA-II algorithm

Based on the current LCD (Liquid Crystal Display) module production line type S-S00A(SEL315V3-S00A), this paper studies the production line of liquid-crystal module with the method of industrial engineering and simulation method. Firstly, the process of the production line of the liquid crystal module is analyzed, and the cycle time of the production line is measured in 24 seconds, and the rate of the production line balance is 60.3%. With the witness software simulation platform used, it is found that the working hours are not high and the proportion of idle time is about 40%. The production process of liquid-crystal module production is analyzed by process analysis. Combining the 5W1H (What, Why, Where, When, Who, How) question technology and ECRS (Eliminate, Combine, Rearrange, Simplify) principle to optimize the production process, the processes cancel 3 times handling, cancel 5 times checks, merge several operations, and reduce the five workers. Finally, simulation model of optimized production line of liquid-crystal module is established, and the simulation results show that the workload for each station basic at 80%, improving the utilization rate of hours. The cycle time of the optimized liquid crystal module production line is 18 seconds. The production line balance rate is 85.56%, increasing by 25%.

Product line optimization in the presence of preferences for compromise alternatives

In order to optimize the local search efficiency of multi-objective parameters of flux switching permanent motor based on traditional NSGA-II algorithm, an improved NSGA-II (iNSGA-II) algorithm is proposed, with an anti-redundant mutation operator and forward comparison operation designed for quick identification of non-dominated individuals. In the initial stage of the iNSGA-II algorithm, half of the individual populations were randomly generated, while the other half was generated according to feature distribution information. Taking the flux switching permanent motor stator/rotor gap, permanent magnets width, stator tooth width, rotor tooth width and other parameters as optimization variables, the flux switching permanent motor maximum output shaft torque and minimum torque ripple are taken as optimization objectives, thus a multi-objective optimization model is established. Real number coding was adopted for obtaining the Pareto optimal solution of flux switching permanent motor structure parameters. The results showed that the iNSGA-II algorithm is better than the traditional NSGA-II on convergence. A 1.8L TOYOTA PRIUS model was selected as the prototype vehicle. By using the optimized parameters, a joint optimization simulation model was established by calling ADVISOR’s back-office function. The simulation results showed that the entire vehicle’s 100-km acceleration time is under 8 s and the battery’s SOC value maintains at 0.5–0.7 in the entire cycle, implying that the iNSGA-II algorithm optimizes the flux switching permanent motor design and is suitable for the initial design and optimizing calculation of the flux switching permanent motor.