汽车行业是国家战略规划的重要领域，目前正处于落实碳达峰碳中和战略目标、 由注重汽车销量转型为注重汽车质量的关键时期。在汽车行业蓬勃发展的环境中，如 何提高生产效率、压缩生产成本、降低生产能耗是企业加强市场竞争力的重点问题。 在生产制造过程中，提高生产效率和制定有效的节能措施的意义重大。精益是一种减少浪费、改善流程的系统化方法。智能调度通过算法将生产任务合理分配，达到优化多个生产目标的目的。本文的研究目标是将精益生产和智能调度两种方法结合来改进生产流程，提高生产效率，并运用仿真工具验证方法的有效性。

首先，运用精益生产方法识别生产线整体性问题并改善。识别主要存在问题的产品族，从生产线收集实时数据，并绘制价值流图。从生产时间、增值时间、增值能耗、 瓶颈工序、库存情况、现场作业规范情况等角度识别生产线存在的浪费。针对在制品库存过高的问题，设计可视化看板拉动方案，形成标准化物流配送体系，有效减少在制品的积压。针对车间现场作业不规范的问题，引入 5S 管理工具，加强现场作业规范和员工素养培训，使员工更高效标准的作业，提高生产效率。

其次，针对瓶颈工序和工件排产计划，提出考虑能耗和并行机时间负荷平衡的混合流水车间调度问题，建立以能源消耗、并行机平衡为目标的数学模型，提出改进自适应遗传算法求解车间调度模型。①算法中应用三种初始化方法，在提高种群多样性的同时确保能产生更优初始解；②采用整数编码表达设备选择与工件加工顺序情况；③利用自适应交叉、变异算子更新种群，避免算法早熟收敛、陷入局部优化等问题。

最后，运用生产仿真工具，加入缓存区大小和设备故障率的实际生产情况，对改进前和改进后的生产线分别建模并分析仿真结果，以验证改进方法在实际生产中的正确性与可行性。通过仿真实验对比，生产线的时间增值率提高到 70.94%，能耗增值率提高到 81.05%，平衡率提高到 72.61%，各个工序的平均利用率提高到 67.87%，等待率降低到 20.32%。

结果表明，精益生产和价值流图能发现公司整体生产问题，并能有效降低在制品库存，增强员工现场操作的规范性。对发现的瓶颈工序和排产计划问题运用智能调度算法，能有效的优化工件排产顺序和并行机选择的策略。精益生产与智能调度算法的结合兼顾了生产线设备与人工优化，通过计算数据和仿真工具都证明了该方法对生产线优化的切实有效。

The automobile industry is an important area of national strategic planning. It is currently in the critical period of implementing the carbon neutrality strategic goal of carbon peak and transforming from focusing on automobile sales to focusing on automobile quality.

In the booming environment of the automobile industry, how to improve production efficiency, reduce production costs and reduce production energy consumption is the key issue for enterprises to strengthen market competitiveness. In the manufacturing process, it is of great significance to improve production efficiency and formulate effective energy-saving measures. Lean is a systematic approach to reduce waste and improve processes. Intelligent scheduling allocates production tasks reasonably through algorithms to achieve the purpose of optimizing multiple production goals. The research goal of this paper is to combine lean production and intelligent scheduling to improve the production process, improve production efficiency, and use simulation tools to verify the effectiveness of the method.

Firstly, the lean production method is used to identify the integrity of the production line. Identify product families with major problems, collect real-time data from the workshop, and draw a value stream map. The waste of the production line is identified from the perspectives of total production time, value-added time, value-added energy consumption, bottleneck process, inventory situation and on-site operation specification. Aiming at the problem of excessive inventory of products in process, the supermarket and visual Kanban pulling scheme are designed to form a standardized logistics distribution system, which can effectively reduce the backlog of products in process. Aiming at the problem of non-standard on-site operation in the workshop, 5S management tools are introduced to strengthen on-site operation norms and employee literacy training, so that employees can work more efficiently and improve production efficiency.

Secondly, aiming at the scheduling of bottleneck processes and workpieces, a hybrid flow shop scheduling problem considering energy consumption and parallel machine time load balance is proposed. A mathematical model aiming at energy consumption and parallel machine balance is established, and an improved adaptive genetic algorithm is proposed to solve the shop scheduling model. Three initialization methods are applied in the algorithm :

①Ensure better initial solution while improving population diversity ; ②Using integer coding to express equipment selection and workpiece processing sequence ; ③the adaptive crossover and mutation operators are used to update the population to avoid premature convergence and local optimization.

Finally, using the production simulation tool, the actual production situation of buffer size and equipment failure rate is added, and the simulation results of the production line before and after improvement are modeled and analyzed respectively to verify the correctness and feasibility of the improved method in actual production. Through simulation comparison, the time increment rate of the production line is increased to 76.55%, the energy increment rate is increased to 81.05%, the balance rate is increased to 72.61%, the average utilization rate of each process is increased to 67.87%, and the waiting rate is reduced to 20.32%.

The results show that lean production and value stream mapping can find the company's overall production problems, effectively reduce WIP inventory, and enhance the standardization of employees ' on-site operations. The intelligent scheduling algorithm is used to find the bottleneck process and scheduling problem, which can effectively optimize the job scheduling order and parallel machine selection strategy. The combination of lean production and intelligent scheduling algorithm takes into account the production line equipment and manual optimization. The calculation data and simulation tools prove that the method is effective for production line optimization

[1] 国家统计局. 中国统计年鉴[M]. 北京:中国统计出版社, 2020.
[2] TORTORELLA G L, FETTERMANN D. Implementation of Industry 4.0 and lean production in Brazilian manufacturing companies[J]. International Journal of Production Research, 2017, 56(8): 2975-2987.
[3] RAJESH R. Flexible business strategies to enhance resilience in manufacturing supply chains: An empirical study - ScienceDirect[J]. Journal of Manufacturing Systems, 2020, 60, 903-919.
[4] TREVILLE S D. Could Lean Production job Design be Intrinsically Motivating Contextual, Configurational, and Levels of Analysis Issues.[J]. Journal of Operations Management, 2006, 24 (2): 99–123.
[5] SAWHNEY R, TEPARAKUL P, BAGCHI A, et al. En-Lean: a framework to align lean and green manufacturing in the metal cutting supply chain[J]. International Journal of Enterprise Network Management, 2007, 1, 238–260 .
[6] FARIAS L S , SANTOS L C , GOHR C F , et al. Criteria and practices for lean and green performance assessment: Systematic review and conceptual framework[J]. Journal of Cleaner Production, 2019, 218(MAY 1):746-762..
[7] Dües C M, Tan K H, Lim M. Green as the new Lean: how to use Lean practices as a catalyst to greening your supply chain[J]. Journal of cleaner production, 2013, 40: 93-100.
[8] GHOBAKHLOO, MORTEZA. The future of manufacturing industry: a strategic roadmap toward Industry 4.0[J]. Journal of Manufacturing Technology Management, 2018, 29(6):910-936.
[9] SONY. Industry 4.0 and lean management: a proposed integration model and research propositions[J]. Production & Manufacturing Research, 2018, 6, 416–43.
[10] BUER S V, STRANDHAGEN J O, CHAN F T S. The link between Industry 4.0 and lean manufacturing: mapping current research and establishing a research agenda[J]. International journal of production research, 2018, 56(8): 2924-2940.
[11] SHAHIN M, F F CHEN, HAMED BOUZARY, et al. Integration of Lean Practices and Industry 4.0 Technologies[J]. International Journal of Advanced Manufacturing Technology 2020, 107 (5-6): 2927–2936.
[12] KAMBLE S, GUNASEKARAN A, Dhone N C. Industry 4.0 and lean manufacturing practices for sustainable organisational performance in Indian manufacturing companies[J]. International Journal of Production Research, 2020, 58, 1319–1337
[13] BROWN A, AMUNDSON J, BADURDEEN F. Sustainable value stream mapping (Sus-VSM) in different manufacturing system configurations: application case studies[J]. Journal of Cleaner Production, 2014, 85: 164-179.
[14] SUNK A, KUHLANG P, EDTMAYR T, et al. Developments of traditional value stream mapping to enhance personal and organisational system and methods competencies[J]. International Journal of Production Research, 2016, 55(13): 3732-3746.
[15] RODRÍGUEZ CORNEJO V, CERVERA PAZ Á, LÓPEZ MOLINA L, et al. Lean Thinking to Foster the Transition from Traditional Logistics to the Physical Internet[J]. Sustainability, 2020, 12(15): 1-17.
[16] 张方哲, 贾纯洁. 基于价值流的飞机蒙皮族零件生产精益改善[J]. 工业工程, 2019,22(06):110-117.
[17] 郭洪飞, 陈敏诗, 张瑜等. 基于价值流图及仿真技术的驾驶室焊装生产线改善[J]. 计算机集成制造系统, 2020, 26(04):920-929.
[18] CHEN W, WANG X, PENG N, et al. Evaluation of the Green Innovation Efficiency of Chinese Industrial Enterprises: Research Based on the Three-Stage Chain Network SBM Model[J]. Mathematical Problems in Engineering, 2020, 2020: 1-11.
[19] SETH D, SETH N, DHARIWAL P. Application of value stream mapping (VSM) for lean and cycle time reduction in complex production environments: a case study[J]. Production Planning & Control, 2017, 28(5): 398-419.
[20] SWARNAKAR V, SINGH A R, ANTONY J, et al. Development of a conceptual method for sustainability assessment in manufacturing[J]. Computers & Industrial Engineering, 2021, 158(2):107403.
[21] ROH P, KUNZ A, WEGENER K. Information stream mapping: Mapping, analysing and improving the efficiency of information streams in manufacturing value streams[J]. CIRP Journal of Manufacturing Science and Technology, 2019, 25: 1-13.
[22] KESKINTURK T, YILDIRIM M B, BARUT M. An ant colony optimization algorithm for load balancing in parallel machines with sequence-dependent setup time[J]. Computers and OperationsResearch, 2012, 39(6):1225-1235.
[23] GUO W, JIANG P, XU L, et al. Integration of value stream mapping with DMAIC for concurrent Lean-Kaizen: A case study on an air-conditioner assembly line[J]. Advances in Mechanical Engineering, 2019, 11(2): 1-17.
[24] GUPTA J N D. Two-stage, hybrid flowshop scheduling problem[J]. Journal of the Operational Research Society, 1988, 39(4):359-364.
[25] MOUZON G, YILDIRIM, M.B. And Twomey, J. Operational Methods for Minimization of Energy Consumption of Manufacturing Equipment[J]. International Journal of Production Research, 2007, 45, 4247-4271.
[26] KAYVANFAR V K G M, AALAEI A, et al. Minimizing total tardiness and earliness on unrelated parallel machines with controllable processing times[J]. Computers & Operations Research, 2014, 41(jan.): 31-43.
[27] CHEN G, ZHANG L, ARINEZ J, et al. Energy-Efficient Production Systems Through Schedule-Based Operations[J]. IEEE Transactions on Automation Science and Engineering, 2013, 10(1): 27-37.
[28] WU X, CHE A. A memetic differential evolution algorithm for energy-efficient parallel machine scheduling[J]. Omega, 2019, 82: 155-165.
[29] JIN X, ZHANG F, FAN L, et al. Scheduling for energy minimization on restricted parallel processors[J]. Journal of Parallel and Distributed Computing, 2015, 81-82: 36-46.
[30] LEI D, GAO L, ZHENG Y. A novel teaching-learning-based optimization algorithm for energy-efficient scheduling in hybrid flow shop[J]. IEEE Transactions on Engineering Management, 2018, 65(2): 330-340.
[31] MANSOURI S A, AKTAS E, BESIKCI U. Green scheduling of a two-machine flowshop: Trade-off between makespan and energy consumption[J]. European Journal of Operational Research, 2016, 248(3): 772-788.
[32] LI J Q, SANG H Y, HAN Y Y, et al. Efficient multi-objective optimization algorithm for hybrid flow shop scheduling problems with setup energy consumptions[J]. Journal of Cleaner Production, 2018, 181: 584-598.
[33] WANG J J, WANG L. A Cooperative Memetic Algorithm With Learning-Based Agent for Energy-Aware Distributed Hybrid Flow-Shop Scheduling[J]. IEEE Transactions on Evolutionary Computation, 2022, 26(3): 461-475.
[34] ZHANG B, PAN Q K, GAO L, et al. A multiobjective evolutionary algorithm based on decomposition for hybrid flowshop green scheduling problem[J]. Computers & Industrial Engineering, 2019, 136: 325-344.
[35] 包哲人, 徐华. 面向能耗机制的多目标柔性作业车间调度[J]. 计算机应用研究, 2017, 34(12):3617-3622.
[36] 唐红涛, 张缓. 基于绿色生产的混合流水车间调度问题研究[J]. 工业工程, 2022, 25(03):115-123.
[37] 张朝阳, 徐莉萍, 李健等. 基于改进狼群算法的柔性作业车间调度研究[J].系统仿真学报,2023,35(03):534-543.
[38] SALONITIS K, BALL P. Energy Efficient Manufacturing from Machine Tools to Manufacturing Systems[J]. Procedia CIRP, 2013, 7: 634-639.
[39] HUANG R H, YANG C L, CHENG W C. Flexible job shop scheduling with due window—a two-pheromone ant colony approach[J]. International Journal of Production Economics, 2013, 141(2): 685-697.
[40] 柳龙华, 陈晶晶, 姜秀梅等. 基于遗传禁忌算法考虑转运约束的并行机批量调度问题研究[J]. 工业工程与管理, 2023, 28(01):59-66.
[41] 韩忠华, 董晓婷, 史海波等. 改进DE算法求解混合流水车间负荷平衡问题[J]. 计算机集成制造系统, 2016, 22(02):547-557.
[42] LIU Q, YANG H. An Improved Value Stream Mapping to Prioritize Lean Optimization Scenarios Using Simulation and Multiple-Attribute Decision-Making Method[J]. IEEE Access, 2020, 8: 204914-204930.
[43] ZHANG W, HOU L, JIAO R J. Dynamic takt time decisions for paced assembly lines balancing and sequencing considering highly mixed-model production: An improved artificial bee colony optimization approach[J]. Computers & Industrial Engineering, 2021, 161.
[44] ZHAO R, ZOU G, SU Q, et al. Digital Twins-Based Production Line Design and Simulation Optimization of Large-Scale Mobile Phone Assembly Workshop[J]. Machines, 2022, 10(5): 367.
[45] STEFFENBANGSOW. Manufacturing Simulation with Plant Simulation and SimTalk[M]. Springer Berlin Heidelberg, 2010.
[46] KOKAREVA V V , MALYHIN A N , SMELOV V G . Production Processes Management by Simulation in Tecnomatix Plant Simulation[J]. Applied Mechanics & Materials, 2015, 756:604-609.
[47] Amjad M S, Rafique M Z, Khan M A. Leveraging Optimized and Cleaner Production through Industry 4.0[J]. Sustainable Production and Consumption, 2021, 26: 859-871.
[48] 张国辉, 高亮, 李培根等. 改进遗传算法求解柔性作业车间调度问题[J]. 机械工程学报, 2009, 45(07):145-151.
[49] 杨博, 刘树东, 鲁维佳等. 改进遗传算法在机器人路径规划中的应用[J]. 现代制造工程, 2022, No.501(06):9-16.
[50] 李瑞, 龚文引. 改进的基于分解的多目标进化算法求解双目标模糊柔性作业车间调度问题[J]. 控制理论与应用, 2022, 39(01):31-40.