Scheduling Method with AGV Resource Based on Production Gantt Chart
[1]ZHANG Fuqiang,BAI Junyan,ZHANG Linpeng.Scheduling Method with AGV Resource Based on Production Gantt Chart[J].Journal of Zhengzhou University (Engineering Science),2022,43(04):23-29.[doi:10. 13705 / j. issn. 1671-6833. 2022. 04. 004]
Copy
Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
43卷
Number of periods:
2022 04
Page number:
23-29
Column:
Public date:
2022-07-03
- Title:
- Scheduling Method with AGV Resource Based on Production Gantt Chart
- Keywords:
- flexible job shop scheduling; whale optimization algorithm; multi-objective optimization; automatic guided vehicle; production Gantt chart
- CLC:
- TH18
- DOI:
- 10. 13705 / j. issn. 1671-6833. 2022. 04. 004
- Abstract:
- At the demands of digital,networked and intelligent transformation and upgrading of manufacturing enterprises,automated guided vehicle (AGV) is widely used in the logistics transportation of production operation.On the basis of workpiece process route planning,it is urgent to plan the transportation in each process to meet the requirements of actual production.Aiming to solve the flexible job shop scheduling problem with limited AGV,a multi-objective scheduling model was established with the optimization functions of maximum completion time,AGV quantity and resource imbalance rate.An improved whale algorithm based on production Gantt chart was proposed to solve the above model.Firstly,the basic principle of whale algorithm was introduced.Secondly,a three-stage real number coding method including AGV quantity,process sequence and AGV number was designed to transform the discrete data into continuous positions in whale individuals.Then the algorithm was improved from three aspects.During initialization,a better initial population was obtained by reverse learning strategy;in the iterative process,adaptive weight and mutation factor were added separately to improve the convergence accuracy and global search ability of the algorithm.Finally,the improved whale algorithm,the basic whale algorithm and the NSGA-II were used separately to solve the scheduling model.The simulation results showed that the improved whale algorithm had higher solution quality and the running time was 21.6% shorter than NSGA-II.The algorithm proposed in the paper had certain practical value in solving the intelligent job shop scheduling problem with limited AGV resources.
- References:
-
[1] 陈敏. 考虑有限车间物流运输能力的 AGV 调度方 法[D] . 广州: 广东工业大学, 2019. CHEN M. AGV scheduling method considering limited logistics transportation capacity of workshop[D]. Guangzhou: Guangdong University of Technology, 2019.[2] 成丽新, 唐秋华, 张利平. 基于基因表达式编程的 单 AGV 加工车间调度规则生成[ J] . 现代制造工 程, 2020(1) : 43-49.CHENG L X, TANG Q H, ZHANG L P. Single AGV job shop dispatching rule generation based on gene expression programming[ J] . Modern manufacturing engineering, 2020(1) : 43-49.[3] 刘二辉, 姚锡凡, 陶韬, 等. 基于改进花授粉算法 的共融 AGV 作业车间调度[ J] . 计算机集成制造 系统, 2019, 25(9) : 2219-2236.LIU E H, YAO X F, TAO T, et al. Improved flower pollinaton algorithm for job shop scheduling problems integrated with AGVs[ J] . Computer integrated manufacturing systems, 2019, 25(9) : 2219-2236.[4] 贺长征, 宋豫川, 雷琦, 等. 柔性作业车间多自动 导引小车和机器的集成调度[ J] . 中国机械工程, 2019, 30(4) : 438-447.HE C Z, SONG Y C, LEI Q, et al. Integrated scheduling of multiple AGVs and machines in flexible job shops [ J] . China mechanical engineering, 2019, 30 (4) : 438-447.[5] 徐云琴, 叶春明, 曹磊. 含有 AGV 的柔性车间调度 优化研究[ J] . 计 算 机 应 用 研 究, 2018, 35 ( 11) : 3271-3275.XU Y Q, YE C M, CAO L. Research on flexible JobShop scheduling problem with AGV constraints [ J ] . Application research of computers, 2018, 35 ( 11 ) : 3271-3275.[6] 刘旭, 楼佩煌, 钱晓明, 等. 基于改进遗传算法的 物料配送多 AGV 调度优化[ J] . 机械设计与制造 工程, 2015, 44(3) : 16-21.LIU X, LOU P H, QIAN X M, et al. Scheduling of automated guided vehicles for material distribution based on improved genetic algorithm[ J] . Machine design and manufacturing engineering, 2015, 44(3) : 16 -21.[7] 王俊英, 颜芬芬, 陈鹏, 等. 基于概率自适应蚁群 算法的云任务调度方法[ J] . 郑州大学学报( 工学 版) , 2017, 38(4) : 51-56.WANG J Y, YAN F F, CHEN P, et al. Task scheduling method based on probability adaptive ant colony optimization in cloud computing[ J] . Journal of Zhengzhou university ( engineering science) , 2017, 38(4) : 51-56.[8] 刘琼, 熊书平, 湛梦梦. 基于改进精英策略的 PCANSGAⅡ的高维目标调度优化[ J] . 计算机集成制 造系统, 2020, 26(9) : 2474-2483.LIU Q, XIONG S P, ZHAN M M. Many-objectives scheduling optimization based on PCA-NSGA Ⅱ with improved elite strategy[ J] . Computer integrated manufacturing systems, 2020, 26(9) : 2474-2483.[9] 龙志伟, 肖松毅, 王晖, 等. 基于粒子群算法的水 资源需 求 预 测 [ J ] . 郑 州 大 学 学 报 ( 工 学 版 ) , 2019, 40(4) : 32-35, 47.LONG Z W, XIAO S Y, WANG H, et al. Water resources demand prediction based on particle swarm optimization[ J] . Journal of Zhengzhou university ( engineering science) , 2019, 40(4) : 32-35, 47.[10] MIRJALILI S, LEWIS A. The whale optimization algorithm[ J] . Advances in engineering software, 2016, 95: 51-67.[11] 张斯琪, 倪静, 郭起轩. 能耗和噪声约束下的柔性 车间调 度 决 策 优 化 [ J] . 小 型 微 型 计 算 机 系 统, 2020, 41(2) : 431-439.ZHANG S Q, NI J, GUO Q X. Flexible shop scheduling decision optimization under the constraint of energy consumption and noise [ J ] . Journal of Chinese computer systems, 2020, 41(2) : 431-439.[12] 杨智飞, 苏春, 胡祥涛, 等. 面向智能生产车间的 多 AGV 系统多目标调度优化[ J] . 东南大学学报 (自然科学版) , 2019, 49(6) : 1033-1040.YANG Z F, SU C, HU X T, et al. Multi-objective scheduling optimization for multi-AGV systems of intelligent jobshop [ J ] . Journal of southeast university ( natural science edition ) , 2019, 49 ( 6 ) : 1033 -1040.[13] RIQUELME N, Von LÜCKEN C, BARAN B. Performance metrics in multi-objective optimization[C] / / 2015 Latin American Computing Conference ( CLEI) . Piscataway: IEEE, 2015: 1-11.
- Similar References:
Memo
-
Last Update:
2022-07-03