Low-carbon Scheduling of Discrete Manufacturing Workshop Driven by Manufacturing Resources Real-time Status Monitoring
-
摘要: 针对离散制造车间低碳调度问题，考虑车间制造资源实时状态，构建实时数据驱动的车间状态监测框架，以机床能耗、刀具磨损和切削液损耗作为碳排放来源，结合最大完工时间，建立一个制造资源实时状态驱动的离散制造车间低碳调度模型，并提出一种融合Tent映射的多目标Jaya优化算法。首先使用Tent混沌序列初始化种群，然后引入基于邻域搜索和模拟退火算法的局部搜索方法，并使用严格的外部档案集保存搜索到的解。最后通过灌装设备生产车间的实例数据对算法和模型进行验证，结果表明所提出的算法和模型能够有效求解离散制造车间低碳调度问题，减少车间异常状态对生产计划的影响，降低车间碳排放量。Abstract: To carry out the low-carbon scheduling of a discrete manufacturing workshop, considering the real-time status of manufacturing resources, a real-time data-driven status monitoring framework was constructed and the optimization model of the real-time status of manufacturing resources driven by the low-carbon scheduling of the discrete manufacturing workshop was established, considering the make-span and energy consumption of machine tool, tool wear and cutting fluid loss as carbon emission sources. An improved multi-objective Jaya optimization algorithm with the Tent chaotic map was proposed. Firstly, the algorithm initialized the population with the Tent chaotic sequence and executed the local search with the neighborhood search and simulated annealing algorithm. Then it preserved the solution with the strict external archival set. Finally, the example data of the filling equipment production workshop is used to verify the validity of the algorithm and the model. The results show that the algorithm and model proposed in the paper can effectively reduce carbon emissions and the impact of abnormal conditions on production plans.
-
表 1 模型符号
符号 含义 m、n、l、g 零件、设备、工序、刀具总数 i、k、j、h 零件、设备、工序、刀具序号 αe、αt、αl 电能、刀具磨耗、切削液损耗的碳排放因子 Pwk、Ppk 机床k的加工功率和准备功率 tijk 工件i工序j在机床k上的加工时间 tij 工件i工序j的工时定额 tijs、tije 工件i工序j的开始和结束时间 Tks、Tke 机床k本次调度的开始时间和最终完工时间 tksu、tkeu 机床k第u个工序的开始和结束时间 qij 工件i工序j单位时间平均刀具磨损量 tijh 工件i工序j刀具h参与的加工时间 qijh 工件i工序j刀具h的单位时间刀具磨损量 mijh 工件i工序j刀具h的质量 TLijh 工件i工序j刀具h的刀具耐用度 Nijh 工件i工序j刀具h的可修磨次数 rk 机床k单位时间切削液损耗量 Vk 机床k使用的切削液体积 TLk 机床k使用的切削液的更换周期 taek、taei 与机床k和工件i有关的扰动的解决时间 
下载: 导出CSV
表 2 零件加工时间表
工件 工序 机床加工时间及刀具磨损 M0 M1 M2 M3 M4 qil J0 O00 8 - 21 16 20 1.49 O01 - 22 - 11 16 0.61 O02 10 - 17 11 19 1.91 O03 19 9 17 - - 1.77 J1 O10 15 14 14 - - 0.82 O11 10 - 15 11 8 0.76 O12 16 19 - 15 17 0.51 J2 O20 25 7 - - 12 1.51 O21 16 - 17 - - 2.08 O22 10 13 - 10 18 0.51 O23 7 11 13 5 - 0.45 J3 O30 9 14 36 - - 0.23 O31 8 - 8 - 7 2.05 O32 - 9 - 9 - 0.77 O33 20 - 6 - - 1.12 J4 O40 14 23 18 - 18 1.48 O41 - 11 9 20 11 0.81 O42 - - 7 - 9 1.35 O43 7 - 11 - 14 1.01 J5 O50 20 17 18 - 7 0.45 O51 16 26 16 - - 1.7 O52 8 12 13 12 36 0.49 J6 O60 14 - 21 - 18 0.18 O61 6 9 - 12 11 1.12 O62 14 12 14 14 9 1.84 O63 - 10 - 14 - 0.52 J7 O70 19 - - 18 - 1.99 O71 14 - 21 10 16 1.09 O72 - 16 8 - 9 0.75 J8 O80 20 21 - 9 - 1.47 O81 16 17 6 - - 0.66 J9 O90 15 - - 8 15 1.33 O91 - 17 15 18 - 1.66 O92 10 - - 13 7 2.06 Pmk 12.5 13.2 13.0 12.9 10.8 - Ppk 1.2 2.3 3.5 3.6 3.7 - rk 5.2 10.4 5.8 4.1 4.6 -
下载: 导出CSV
表 4 算法性能指标统计表
算法 名称 FT FC Cov MS NAGS-II 最优值 82 71.858 0.4 0.583 平均值 84.6 72.447 0.048 0.418 方差 1.94 0.11 0.008 0.013 IMOJaya 最优值 79 69.816 1 0.953 平均值 81.25 70.21 0.828 0.8 方差 1.188 0.030 2 0.038 0.007
下载: 导出CSV
-
[1] 吴秀丽, 崔琪.基于NSGA-Ⅱ的柔性流水车间低碳调度问题[J/OL].计算机集成制造系统: 1-28[2019-01-1].http://www.cnki.com.cn/Article/CJFDTOTAL-JSJJ2017121300D.htmWu X L, Cui Q. Multi-objective flexible flow shop scheduling problem with renewable[J]. Computer Integrated Manufacturing Systems: 1-28[2019-01-11]. http://www.cnki.com.cn/Article/CJFDTOTAL-JSJJ2017121300D.htm (in Chinese) [2] Deng J, Wang L. A competitive memetic algorithm for multi-objective distributed permutation flow shop scheduling problem[J]. Swarm and Evolutionary Computation, 2017, 32:121-131 http://cn.bing.com/academic/profile?id=2334ab88b0381e651b3600ae6905f518&encoded=0&v=paper_preview&mkt=zh-cn [3] Shao Z S, Pi D C, Shao W S. A multi-objective discrete invasive weed optimization for multi-objective blocking flow-shop scheduling problem[J]. Expert Systems with Applications, 2018, 113:77-99 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=535f0a63a2d6fa05435e587edc0922fa [4] 雷德明, 潘子肖, 张清勇.多目标低碳并行机调度研究[J].华中科技大学学报, 2018, 46(8):104-109 http://d.old.wanfangdata.com.cn/Periodical/hzlgdxxb201808020Lei D M, Pan Z X, Zhang Q Y. Researches on multi-objective low carbon parallel machines scheduling[J]. Journal of Huazhong University of Science & Technology, 2018, 46(8):104-109(in Chinese) http://d.old.wanfangdata.com.cn/Periodical/hzlgdxxb201808020 [5] 蒋增强, 左乐.低碳策略下的多目标柔性作业车间调度[J].计算机集成制造系统, 2015, 21(4):1023-1031 http://d.old.wanfangdata.com.cn/Periodical/jsjjczzxt201504017Jiang Z Q, Zuo L. Multi-objective flexible job-shop scheduling based on low-carbon strategy[J]. Computer Integrated Manufacturing Systems, 2015, 21(4):1023-1031(in Chinese) http://d.old.wanfangdata.com.cn/Periodical/jsjjczzxt201504017 [6] 姜天华.基于灰狼优化算法的低碳车间调度问题[J].计算机集成制造系统, 2018, 24(10):2428-2435 http://d.old.wanfangdata.com.cn/Periodical/jsjjczzxt201810006Jiang T H. Low-carbon workshop scheduling problem based on grey wolf optimization[J]. Computer Integrated Manufacturing Systems, 2018, 24(10):2428-2435(in Chinese) http://d.old.wanfangdata.com.cn/Periodical/jsjjczzxt201810006 [7] Wu X L, Sun Y J. A green scheduling algorithm for flexible job shop with energy-saving measures[J]. Journal of Cleaner Production, 2018, 172:3249-3264 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=58453a8d74803b11ef1ee2c0c8c53a19 [8] Rao R V. Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems[J]. International Journal of Industrial Engineering Computations, 2016, 7(1):19-34 http://cn.bing.com/academic/profile?id=27a3529b7477526b8f43c67f74f104f8&encoded=0&v=paper_preview&mkt=zh-cn [9] Agarwal N, Pradhan M K, Shrivastava N. A new multi- response Jaya Algorithm for optimisation of EDM process parameters[J]. Materialstoday: Proceedings, 2018, 5(11):23759-23768 http://cn.bing.com/academic/profile?id=13064a1d97d74d2d2ab1b677a7e405c8&encoded=0&v=paper_preview&mkt=zh-cn [10] Venkata Rao R, Rai D P. Optimization of selected casting processes using Jaya algorithm[J]. Materialstoday: Proceedings, 2017, 4(10):11056-11067 http://cn.bing.com/academic/profile?id=a6c8e92fc2fc0085fa3a11816cda1470&encoded=0&v=paper_preview&mkt=zh-cn [11] Degertekin S O, Lamberti L, Ugur I B. Sizing, layout and topology design optimization of truss structures using the Jaya algorithm[J]. Applied Soft Computing, 2018, 70:903-928 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=2d85a5db9c4bfb8a48359eb17361764a [12] Aseem M, Divya S. A TLBO and a Jaya heuristics for permutation flow shop scheduling to minimize the sum of inventory holding and batch delay costs[J]. Computers & Industrial Engineering, 2018, 124:509-522 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=b4ec5fd98637f8d9202f24c2f07be1e6 [13] 李爱平, 古志勇, 朱璟, 等.基于低碳制造的多工步孔加工切削参数优化[J].计算机集成制造系统, 2015, 21(6):1515-1522 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jsjjczzxt201506013Li A P, Gu Z Y, Zhu J, et al. Optimization of cutting parameters for multi-pass hole machining based on low carbon manufacturing[J]. Computer Integrated Manufacturing Systems, 2015, 21(6):1515-1522(in Chinese) http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jsjjczzxt201506013 [14] 匡芳君, 徐蔚鸿, 金忠.自适应Tent混沌搜索的人工蜂群算法[J].控制理论与应用, 2014, 31(11):1502-1509 http://d.old.wanfangdata.com.cn/Periodical/kzllyyy201411009Kuang F J, Xu W H, Jin Z. Artificial bee colony algorithm based on self-adaptive Tent chaos search[J]. Control Theory & Applications, 2014, 31(11):1502-1509(in Chinese) http://d.old.wanfangdata.com.cn/Periodical/kzllyyy201411009 [15] 李聪波, 李鹏宇, 刘飞, 等.面向高效低碳的机械加工工艺路线多目标优化模型[J].机械工程学报, 2014, 50(17):133-141 http://d.old.wanfangdata.com.cn/Periodical/xdzzjsyzb201703020Li C B, Li P Y, Liu F, et al. Multi-objective machining process route optimization model for high efficiency and low carbon[J]. Journal of Mechanical Engineering, 2014, 50(17):133-141(in Chinese) http://d.old.wanfangdata.com.cn/Periodical/xdzzjsyzb201703020 [16] Zitzler E, Thiele L. Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach[J]. IEEE Transactions on Evolutionary Computation, 1999, 3(4):257-271 http://cn.bing.com/academic/profile?id=12932c54d336f5e22f7cb8abcd95db08&encoded=0&v=paper_preview&mkt=zh-cn [17] Tsai S J, Sun T Y, Liu C C, et al. An improved multi-objective particle swarm optimizer for multi-objective problems[J]. Expert Systems with Applications, 2010, 37(8):5872-5886 http://cn.bing.com/academic/profile?id=7c6b12f37e303cd72fd4b6cdb3bc5b1b&encoded=0&v=paper_preview&mkt=zh-cn -
点击查看大图
图(10) / 表(5)
计量
- 文章访问数: 653
- HTML全文浏览量: 161
- PDF下载量: 26
- 被引次数: 0
