Table of Content

    10 November 2022, Volume 33 Issue 21
    Industrial Engineering and Lean Management for Smart Manufacturing
    QI Ershi, HUO Yanfang, LIU Hongwei
    2022, 33(21):  2521-2530.  DOI: 10.3969/j.issn.1004-132X.2022.21.001
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    This paper reviewed the development routes of developed countries such as the United States, Japan, and Germany since the emergence of industrial engineering for more than 100 years, discussed the regular characteristics of enterprise management innovation, and drew the conclusion that smart manufacturing also needed IE/LM to provide management support. According to the smart manufacturing project cycle, the functions of IE/LM in smart manufacturing engineering were analyzed from the perspectives of basic preparation, scheme selection, and integration development. The framework of lean smart management system for smart manufacturing transformation of Chinese enterprises was given, and the improvement ideas and methods of smart-lean integration were explained based on a case. Finally, some key technologies of smart manufacturing management facing transformation and upgrading of Chinese enterprises were presented based on the actual needs of China. 
    Dual Center Logistics Path Planning of Blending Workshops
    YU Hanlin, LUO Yabo
    2022, 33(21):  2531-2537.  DOI: 10.3969/j.issn.1004-132X.2022.21.002
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     The method of dual center logistics path parallel real-time planning problem for logistics planning of blending workshops focused on the reliability of scheduling with low degree of path optimization. To solve this problem, based on experience and practice, considering load balance an adjacent three-point clustering method was proposed with consideration of load balance and path optimization to give priority on ensuringthe scheduling reliability. Benchmarking experiments and real case study show that the solution with higher comprehensive satisfaction is stably obtained by the proposed strategies and methods on the premise of ensuring scheduling reliability. 
    Job Integrated Optimization of Automated Storage/retrieval Systems Based on Two-stage Wolf Pack Algorithm
    HE Li, TAO Yifei, LUO Junbin, XUN Hongkai
    2022, 33(21):  2538-2546.  DOI: 10.3969/j.issn.1004-132X.2022.21.003
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     In order to improve the loading/unloading operation efficiency of single-shuttle AS/RS under dynamic working conditions, a simulation optimization model was established for the job integrated optimization problem of AS/RS. The model considered the weight of goods and the frequency of entering and leaving the warehouse to divide the rack area. To minimize the running time of single-shuttle stacker in the instruction, a two-stage wolf pack algorithm was designed. The algorithm used the wolf pack algorithm to optimize the storage location assignment and job scheduling, and the solution processes reflected the mutual connection and feedback between the two optimization problems. The experimental results show that under different order sizes, the two-stage wolf pack algorithm may obtain satisfactory solutions and effectively shorten the operation time of AS/RS, compared with other optimization methods. 
    Energy Footprint Modeling and Parameter Optimization in Workshop Manufacturing Processes
    TIAN Ying, SHAO Wenting, WANG Taiyong
    2022, 33(21):  2547-2553.  DOI: 10.3969/j.issn.1004-132X.2022.21.004
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     To reduce the energy consumption during the parts manufacturing processes, an energy-saving focused multi-equipment machining parameter collaborative optimization method was proposed. Taking the workshop manufacturing processes with machines and robots as target, the energy footprint models considering cutting tool degradation processes were setup for the workshop system. Considering the cost index function of tool life and stability of robotic transportation, a multi-objective collaborative optimization model of machining parameters with multi-equipment systems was established. Taking the processing time as constraint, the artificial bee colony algorithm was used to obtain the global optimal parameters. Experimental results show that the optimized parameters may reduce the energy consumption of CNC machines by 17.97%, and reduce the energy consumption of the robotic transportation by 18.13%. 
    Distributed Flexible Job Shop Green Scheduling with Transportation Time
    ZHANG Hongliang, XU Gongjie, BAO Qiang, PAN Ruilin
    2022, 33(21):  2554-2563,2645.  DOI: 10.3969/j.issn.1004-132X.2022.21.005
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     Aiming at the distributed flexible job shop green scheduling problem with transportation time, a mixed-integer programming model for minimizing makespan and total energy consumption was established, and an INSGA-Ⅱwas proposed. A double-layer encoding scheme was adopted based on operation and machine, and a greedy insertion decoding method was designed considering transportation time. Considering processing time and energy consumption, an initialization method was designed to improve the quality of the population, and the multi-parent crossover and new mutation operations were used to update the population. A variable neighborhood search strategy was embedded to improve the quality of Pareto front. The effectiveness of the proposed method was verified by a series of experiments. 
    Low-carbon Scheduling of Multi-objective Flexible Job-shop Based on Improved NSGA-Ⅱ
    JIANG Yixiao , JI Weixi, HE Xin, SU Xuan
    2022, 33(21):  2564-2577.  DOI: 10.3969/j.issn.1004-132X.2022.21.006
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     To solve the low-carbon scheduling problems of multi-objective flexible job-shops taking equipment energy consumption, tool wear and cutting fluid consumption as carbon emission sources and energy consumption and labor cost as processing cost, a low-carbon scheduling model was formulated to minimize carbon emission, makespan and processing cost, and an improved elitist NSGA-Ⅱ was proposed to solve the problem. Firstly, the chromosome composition was dynamically adjusted by encoding based on Tent chaotic map and greedy decoding based on analytic hierarchy process to improve the quality of the initial population. Then, an adaptive genetic strategy was proposed based on genetic parameters, which adjusted the crossover and mutation rates according to the population evolution stage and the population non dominated state dynamically. Finally, based on external archives an improved elite retention strategy was designed to improve the population diversity in the later stages of the algorithm and retain high-quality individuals in the evolution processes. The effectiveness of the improved algorithm was verified by standard scheduling examples and a practical case. 
    Performance Analysis for Information and Part Flow Driven Production Systems with a Repair Loop
    CHEN Wenchong, RUAN Yuanpeng, LI Jianguo, QI Ershi
    2022, 33(21):  2578-2589.  DOI: 10.3969/j.issn.1004-132X.2022.21.007
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    Focus on an information and part flow hybrid driven asynchronous serial production systems(HASPS) with a rework loop, the impacts of processing and information control parameters to the steady-state performance of a complex production system were analyzed with the consideration of random breakdown of manufacturing devices and information control devices. Based on the interactive relationship between part flows and information flows, the HASPS with a rework loop was split into various hybrid driven sub-assembly systems and sub-disassembly systems by overlapping decomposition approach. A forward- and backward recursive algorithm was proposed to estimate system steady-state throughput. Numerical experiments and acase for digital production systems of industrial sewing machines verified the efficiency of the recursive algorithm in identifying the bottlenecks and estimating the steady-state performance. 
    Dual Resource-constrained Flexible Job Shop Scheduling Algorithm Considering Machining Quality of Key Jobs
    SUN Aihong, SONG Yuchuan, YANG Yunfan, LEI Qi
    2022, 33(21):  2590-2600.  DOI: 10.3969/j.issn.1004-132X.2022.21.008
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     In the resource constrained job shops with heterogeneous workers and diverse machine tool types, aiming at the situation that resource preemption made the machining quality tilt to non-key jobs and resulted in the machining quality of key jobs could not be guaranteed, a dual resource(worker/machine) constrained flexible job shop scheduling problem was established, which taken the completion time as main objective, and the machining quality of key parts and the overall machining quality as auxiliary objectives. And then, a two-level nested ant colony algorithm was proposed to solve the problem. Firstly, the candidate sets of jobs and resources were used to generate feasible scheduling solutions to meet the requirements of key jobs processing quality. Secondly, according to the difference of machine types and man-machine time windows, based on time windows an activity scheduling strategy was designed to find a more suitable starting time for the operations, and the local optimization ability of the algorithm was improved. Then, a quality assurance strategy was proposed to improve the quality of key jobs and overall jobs. Finally, the performance of the two-level nesting ant colony algorithm and the quality assurance strategy was verified by a numerical example. 
    Scheduling of Flexible Job Shop Based on High-dimension and Multi-objective Migrating Bird Optimization Algorithm
    WANG Qiulian, DUAN Xinghao
    2022, 33(21):  2601-2612.  DOI: 10.3969/j.issn.1004-132X.2022.21.009
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     Aiming at the problems in flexible job shop scheduling, an improved multi-objective migrating bird optimization(MOMBO)algorithm was proposed to solve the high-dimensional multi-objective scheduling problem with the consideration of maximum completion time, total delay period, total load of machine, and total energy consumption. On the basis of migrating bird optimization algorithm, MOMBO algorithm introduced a selection operator based on Pareto domination and reference point to give bird population selection pressure, and the combined weight method based on attribute hierarchical mode and gray relation analysis was used to select the most suitable solution from the optimal solution sets. The effectiveness and practicability of MOMBO were verified by test instances and case study. 
    Performance Analysis and Optimization of Cylinder Head Production Lines Considering Multiple States of Equipment
    HOU Xiaobo, LI Congbo, YANG Miao, YI Qian
    2022, 33(21):  2613-2622.  DOI: 10.3969/j.issn.1004-132X.2022.21.010
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     To improve the performance and reduce the production cost for engine cylinder head production lines, a performance analysis and buffer allocation optimization method of engine cylinder head production lines  was proposed considering multi-state equipment. Firstly, the Markov model was applied to reveal the relationship between multiple states of the equipment and buffer inventory, and considering the availability of the production lines and buffer capacity a coupling model was constructed. Then, a multi-objective model that considered the availability of the production line and the buffer allocation cost as objectives was developed, and the NSGA-Ⅱ algorithm was applied to optimize the model. Finally, a case study was implemented which verify the feasibility of the proposed method that may improve production performance and reduce the buffer allocation costs. 
    Construction Method of Virtual-real Drive Systems for Robots in Digital Twin Workshops
    LIU Huailan, ZHAO Wenjie, LI Shizhuang, YUE Peng, MA Baorui
    2022, 33(21):  2623-2632.  DOI: 10.3969/j.issn.1004-132X.2022.21.011
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     For the current problems for complex modeling and long development cycle of virtual entities such as industrial robots in digital twin workshop construction, a modular construction method of virtual-real drive systems for industrial robots in digital twin workshops was proposed, which divided the virtual-real drive systems into an interaction layer for setting model parameters and a control layer for designing configurations according to functional requirements, and then abstracted the physical industrial robots, etc. into a simulation model from coupling single functional atomic model. The modular and hierarchical approach to building virtual-reality drive systems may quickly and effectively realize the modeling of digital twin virtual entities such as industrial robots, as well as the simulation of industrial robots operating in virtual space and the simultaneous operation of virtual-reality.
    An Experimental Study on Compliance Willingness of Security Alert Information Presentation Framework
    SUN Linhui, LI Xun, GAO Jie, YUAN Xiaofang
    2022, 33(21):  2633-2645.  DOI: 10.3969/j.issn.1004-132X.2022.21.012
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     In order to study the influences of different information presentation frameworks of security alert in intelligent manufacturing information system on user compliance willingness under different security alert situations, a situational experimental method was used to explore the influences of different security alert information presentation frameworks on user trust level and the moderating effect of security alert situations. The results show that the security alert situation has a moderating effect on the relationship between security alert information presentation framework and trust, and between trust and user compliance willingness. Information presentation framework directly affects users compliance willingness with the intelligent manufacturing information system security alerts, and trust has a mediating effect between information presentation framework and users compliance willingness.