Multi-objective Hybrid Flowline Energy-saving Scheduling with Combined Buffer Constraints
[1]XUAN Hua,GENG Zhuxin,LI Bing.Multi-objective Hybrid Flowline Energy-saving Scheduling with Combined Buffer Constraints[J].Journal of Zhengzhou University (Engineering Science),2025,46(01):17-25.[doi:10.13705/j.issn.1671-6833.2024.04.009]
Copy
Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
46
Number of periods:
2025 01
Page number:
17-25
Column:
Public date:
2024-12-23
- Title:
- Multi-objective Hybrid Flowline Energy-saving Scheduling with Combined Buffer Constraints
- Keywords:
- hybrid flowline; improved multi-objective memetic algorithm; combined buffer constraints; unrelated parallel machine; multi-objective optimization; energy-saving scheduling
- CLC:
- TB491
- Abstract:
- To solve the hybrid flowline energy-saving scheduling problem with two intermediate buffer constraints, infinite buffer and blocking, between production stages, a mathematical model was formulated by considering the uncorrelated parallel machines and multiple time constraints. Taking into account the characteristics of the problem, an improved multi-objective memetic algorithm (IMOMA) was proposed to minimize simultaneously makespan and total energy consumption of the machines. The algorithm adopted a matrix encoding method based on uncorrelated machine assignment. Using a hybrid initialization strategy based on Tent chaotic map to generate the initial cell array, an non-dominated sorting genetic algorithm improved by parameter-based adaptive genetic strategy was applied for the global optimization operator, and a search strategy integrating adaptive selection neighborhood search and multi-objective simulated annealing was designed for the locally enhanced search operator to improve the algorithm′s search capability. The effectiveness and superiority of the proposed algorithm were verified through case experiments with 24 problem scales. The experimental results showed that the average IGD value of 47.89 and the average SP value of 857.25 obtained by IMOMA within the average running time of 241.26 s were lower than the other three comparison algorithms. So the solution set obtained by IMOMA had better convergence, diversity and distributivity.
- References:
-
[1]RIAHI V, KHORRAMIZADEH M, HAKIM NEWTON M A, et al. Scatter search for mixed blocking flowshop scheduling[J]. Expert Systems with Applications, 2017, 79: 20-32. [2]王君妍, 王薛苑, 轩华. 带批处理机的多阶段柔性流水车间调度优化[J]. 郑州大学学报(工学版), 2017, 38(5): 86-90.WANG J Y, WANG X Y, XUAN H. Multi-stage flexible flowshop scheduling with batching machines[J]. Journal of Zhengzhou University (Engineering Science), 2017, 38(5): 86-90.[3]QIN H X, HAN Y Y, WANG Y T, et al. Intelligent optimization under blocking constraints: a novel iterated greedy algorithm for the hybrid flow shop group scheduling problem[J]. Knowledge-Based Systems, 2022, 258: 109962.[4]MISSAOUI A, BOUJELBENE Y. An effective iterated greedy algorithm for blocking hybrid flow shop problem with due date window[J]. RAIRO-Operations Research, 2021, 55(3): 1603-1616.[5]胡蓉, 董钰明, 钱斌. 基于探路者算法的绿色有限缓冲区流水线调度[J]. 系统仿真学报, 2021, 33(6): 1384-1396.HU R, DONG Y M, QIAN B. Pathfinder algorithm for green pipeline scheduling with limited buffers[J]. Journal of System Simulation, 2021, 33(6): 1384-1396.[6]LIN C C, LIU W Y, CHEN Y H. Considering stockers in reentrant hybrid flow shop scheduling with limited buffer capacity[J]. Computers & Industrial Engineering, 2020, 139: 106154.[7]裴小兵, 李依臻. 新型混合改进遗传算法求解零等待流水车间调度问题[J]. 计算机集成制造系统, 2021, 27(3): 815-827.PEI X B, LI Y Z. New hybrid improved genetic algorithm for solving no-wait flow shop scheduling problem[J]. Computer Integrated Manufacturing Systems, 2021, 27 (3): 815-827.[8]AZAIEZ M N, GHARBI A, KACEM I, et al. Two-stage no-wait hybrid flow shop with inter-stage flexibility for operating room scheduling[J]. Computers & Industrial Engineering, 2022, 168: 108040.[9]WANG Y M, LI X P, RUIZ R, et al. An iterated greedy heuristic for mixed no-wait flowshop problems[J]. IEEE Transactions on Cybernetics, 2018, 48(5): 1553-1566.[10]钟祾充, 李文锋, 贺利军, 等. 集装箱码头混合零空闲柔性流水作业调度优化[J]. 计算机集成制造系统, 2022, 28(11): 3421-3432.ZHONG L C, LI W F, HE L J, et al. Optimization of mixed no-idle flexible flow scheduling in container terminal[J]. Computer Integrated Manufacturing Systems, 2022, 28(11): 3421-3432.[11]轩华, 付鑫博, 李冰. 基于混合离散人工蜂群算法的混合零等待柔性流水车间优化研究[J]. 工业工程与管理, 2023, 28(1): 170-180.XUAN H, FU X B, LI B. Research on optimization of mixed zero-wait flexible flowshop based on hybrid discrete artificial bee colony algorithm[J]. Industrial Engineering and Management, 2023, 28(1): 170-180.[12] ZHUANG Z L, ZHANG Z L, TENG H, et al. Optimization for integrated scheduling of intelligent handling equipment with bidirectional flows and limited buffers at automated container terminals[J]. Computers and Operations Research, 2022, 145: 105863.[13]姜一啸, 吉卫喜, 何鑫, 等. 基于改进非支配排序遗传算法的多目标柔性作业车间低碳调度[J]. 中国机械工程, 2022, 33(21): 2564-2577.JIANG Y X, JI W X, HE X, et al. Low-carbon scheduling of multi-objective flexible job-shop based on improved NSGA-Ⅱ[J]. China Mechanical Engineering, 2022, 33 (21): 2564-2577.[14]黄辉, 李梦想, 严永. 考虑序列设置时间的混合流水车间多目标调度研究[J]. 运筹与管理, 2020, 29 (12): 215-221.HUANG H, LI M X, YAN Y. Research on multi-objective scheduling of hybrid flow production shop considering sequence setting time[J]. Operations Research and Management Science, 2020, 29(12): 215-221.[15]张洪亮, 徐公杰, 张金春. 带有学习效应的多阶段混合流水车间调度问题研究[J]. 重庆师范大学学报(自然科学版), 2021, 38(1): 87-95.ZHANG H L, XU G J, ZHANG J C. Research on multistage hybrid flow shop scheduling problem with learning effect[J]. Journal of Chongqing Normal University (Natural Science), 2021, 38(1): 87-95.[16]王丽萍, 任宇, 邱启仓, 等. 多目标进化算法性能评价指标研究综述[J]. 计算机学报, 2021, 44(8): 1590-1619.WANG L P, REN Y, QIU Q C, et al. Survey on performance indicators for multi-objective evolutionary algorithms[J]. Chinese Journal of Computers, 2021, 44 (8): 1590-1619.
- Similar References:
Memo
-
Last Update:
2024-12-30