基于混合决策机制的自适应生产调度优化与仿真

doi:10.16182/j.issn1004731x.joss.25-0452

系统仿真学报 ›› 2025, Vol. 37 ›› Issue (7): 1791-1803.doi: 10.16182/j.issn1004731x.joss.25-0452

• 特约论文 • 上一篇

基于混合决策机制的自适应生产调度优化与仿真

纪志成¹^,², 全震¹^,², 王艳¹^,²

^1.江南大学物联网技术应用教育部工程研究中心，江苏无锡 214122
^2.江南大学物联网工程学院，江苏无锡 214122

收稿日期:2025-05-20 修回日期:2025-06-09 出版日期:2025-07-18 发布日期:2025-07-30
通讯作者: 全震
第一作者简介:纪志成 1959年生，江南大学物联网工程学院教授，博士生导师，国家“万人计划”教学名师，中国仿真学会会士。原江南大学副校长、教育部高等学校学科创新引智计划(简称“111 计划”)基地负责人。兼任第七届教育部科学技术委员会信息学部副主任、第十届国家督学、教育部高等学校自动化类专业教学指导委员会副主任(2013-2023)、“十三五”国家重点研发计划物联网与智慧城市专项责任专家、《系统仿真学报》顾问。长期从事智能工厂、生产优化与智能运行系统方向的研究。以第一完成人获2011年和2016年教育部科学技术进步一等奖2项(1/10)、获2018年国家教学成果一等奖1项(1/15)。
纪志成(1959-)，男，教授，博士，研究方向为制造物联集成与优化。
基金资助:
长三角科技创新共同体联合攻关项目(2023CSJGG1700)

Optimization and Simulation of Adaptive Production Scheduling Based on Hybrid Decision-making Mechanism

Ji Zhicheng¹^,², Quan Zhen¹^,², Wang Yan¹^,²

^1.Engineering Research Center of Internet of Things Technology Applications (Ministry of Education), Jiangnan University, Wuxi 214122, China
^2.School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China

Received:2025-05-20 Revised:2025-06-09 Online:2025-07-18 Published:2025-07-30
Contact: Quan Zhen

摘要/Abstract

摘要：

为求解以最大完工时间、平均延迟时间、瓶颈机器加工负载率为目标的柔性生产调度优化问题，针对机器分配与任务排序的决策复杂度与约束特征，提出了以二维染色体编码机器分配、启发式规则评估任务排序优先级的混合决策机制调度算法，以增强对决策优化的适应水平。为了进一步改善所提调度方法的性能，制定了以等待调度任务所需加工时长分布为依据的自适应规则策略，实现决策对调度场景的动态适应性，提升全局优化的综合优势水平。实验结果表明：该方法提高了调度问题求解的寻优效率，提供更加占据主导地位的非支配解集。

关键词: 柔性生产调度, 混合决策机制, 自适应规则策略, 进化算法, 遗传规划超启发式

Abstract:

To optimize flexible production scheduling with the objectives of the longest makespan, mean tardiness, and bottleneck machine processing load rate, a hybrid decision-making mechanism scheduling algorithm was proposed based on the decision complexity and constraint characteristics of machine assignment and task sequencing. The algorithm adopted a two-dimensional chromosome to encode machine assignment and a heuristic rule to evaluate task sequencing priority, enhancing the adaptability of the method to decision-making optimization. In order to further improve the performance of the proposed scheduling method, an adaptive rule strategy was designed based on the distribution of processing time required for waiting scheduling tasks to achieve dynamic adaptability of the decision-making to scheduling scenarios, thereby improving the comprehensive advantage level of global optimization. Experimental tests show that the proposed method helps to improve the optimization efficiency of scheduling and provides non-dominated solution sets occupying higher dominant positions.

Key words: flexible production scheduling, hybrid decision-making mechanism, adaptive rule strategy, evolutionary algorithm, genetic programming hyper-heuristics

中图分类号:

TP18

纪志成,全震,王艳 . 基于混合决策机制的自适应生产调度优化与仿真[J]. 系统仿真学报, 2025, 37(7): 1791-1803.

Ji Zhicheng,Quan Zhen,Wang Yan . Optimization and Simulation of Adaptive Production Scheduling Based on Hybrid Decision-making Mechanism[J]. Journal of System Simulation, 2025, 37(7): 1791-1803.

图/表 12

表1

图1

图2

图3

图4

图5

表2

调度解集的HV度量结果

实例	规模	$μ i$ 值区间	HDMSA-AR	HDMSA-DR1	HDMSA-DR2	NSGA-II	MOEA/D
MK01	$10 × 6$	$[1.0,2.5]$	0.67(0.01)	0.50(0.01)( $-$ )	0.56(0.05)( $-$ )	0.68(0.01)( $+$ )	0.69(0.01)( $+$ )
MK02	$10 × 6$	$[1.0,2.5]$	0.66(0.02)	0.48(0.02)( $-$ )	0.56(0.05)( $-$ )	0.64(0.01)( $≈$ )	0.65(0.02)( $≈$ )
MK03	$15 × 8$	$[1.0,2.5]$	0.67(0.01)	0.43(0.01)( $-$ )	0.26(0.04)( $-$ )	0.56(0.01)( $-$ )	0.60(0.01)( $-$ )
MK04	$15 × 8$	$[1.0,2.5]$	0.66(0.01)	0.50(0.01)( $-$ )	0.36(0.07)( $-$ )	0.61(0.01)( $-$ )	0.65(0.02)( $≈$ )
MK05	$15 × 4$	$[1.0,5.0]$	0.58(0.01)	0.27(0.01)( $-$ )	0.24(0.02)( $-$ )	0.45(0.02)( $-$ )	0.51(0.01)( $-$ )
MK06	$10 × 10$	$[1.0,3.0]$	0.68(0.02)	0.50(0.04)( $-$ )	0.53(0.06)( $-$ )	0.63(0.02)( $-$ )	0.63(0.02)( $-$ )
MK07	$20 × 5$	$[1.5,3.0]$	0.56(0.02)	0.35(0.01)( $-$ )	0.34(0.02)( $-$ )	0.52(0.01)( $-$ )	0.54(0.01)( $-$ )
MK08	$20 × 10$	$[1.5,3.0]$	0.50(0.01)	0.26(0.01)( $-$ )	0.14(0.01)( $-$ )	0.46(0.01)( $-$ )	0.52(0.01)( $≈$ )
MK09	$20 × 10$	$[1.0,4.0]$	0.65(0.01)	0.34(0.04)( $-$ )	0.17(0.02)( $-$ )	0.52(0.01)( $-$ )	0.55(0.02)( $-$ )
MK10	$20 × 15$	$[1.0,3.0]$	0.61(0.01)	0.38(0.03)( $-$ )	0.10(0.01)( $-$ )	0.53(0.01)( $-$ )	0.56(0.02)( $-$ )
MK11	$30 × 5$	$[1.0,8.0]$	0.47(0.01)	0.20(0.02)( $-$ )	0.13(0.02)( $-$ )	0.43(0.01)( $-$ )	0.47(0.01)( $≈$ )
MK12	$30 × 10$	$[1.5,4.0]$	0.55(0.01)	0.26(0.01)( $-$ )	0.20(0.02)( $-$ )	0.51(0.01)( $-$ )	0.56(0.01)( $≈$ )
MK13	$30 × 10$	$[1.0,5.0]$	0.62(0.01)	0.33(0.02)( $-$ )	0.18(0.02)( $-$ )	0.53(0.01)( $-$ )	0.57(0.01)( $-$ )
MK14	$30 × 15$	$[1.0,5.0]$	0.56(0.01)	0.21(0.03)( $-$ )	0.18(0.03)( $-$ )	0.46(0.01)( $-$ )	0.52(0.02)( $-$ )
MK15	$30 × 15$	$[1.0,4.0]$	0.66(0.01)	0.34(0.03)( $-$ )	0.14(0.02)( $-$ )	0.57(0.02)( $-$ )	0.61(0.01)( $-$ )

表2

表3

调度解集的MID度量结果

实例	规模	$μ i$ 值区间	HDMSA-AR	HDMSA-DR1	HDMSA-DR2	NSGA-II	MOEA/D
MK01	$10 × 6$	$[1.0,2.5]$	0.50(0.01)	0.69(0.01)( $+$ )	0.81(0.03)( $+$ )	0.57(0.02)( $+$ )	0.64(0.02)( $+$ )
MK02	$10 × 6$	$[1.0,2.5]$	0.52(0.02)	0.73(0.02)( $+$ )	0.81(0.07)( $+$ )	0.58(0.02)( $+$ )	0.65(0.04)( $+$ )
MK03	$15 × 8$	$[1.0,2.5]$	0.49(0.01)	0.75(0.01)( $+$ )	1.04(0.01)( $+$ )	0.60(0.01)( $+$ )	0.58(0.01)( $+$ )
MK04	$15 × 8$	$[1.0,2.5]$	0.50(0.03)	0.69(0.03)( $+$ )	0.93(0.05)( $+$ )	0.57(0.03)( $+$ )	0.60(0.03)( $+$ )
MK05	$15 × 4$	$[1.0,5.0]$	0.57(0.01)	0.86(0.01)( $+$ )	1.03(0.02)( $+$ )	0.74(0.01)( $+$ )	0.75(0.01)( $+$ )
MK06	$10 × 10$	$[1.0,3.0]$	0.49(0.02)	0.69(0.05)( $+$ )	0.83(0.03)( $+$ )	0.52(0.02)( $+$ )	0.60(0.01)( $+$ )
MK07	$20 × 5$	$[1.5,3.0]$	0.61(0.01)	0.81(0.01)( $+$ )	1.00(0.05)( $+$ )	0.67(0.01)( $+$ )	0.77(0.04)( $+$ )
MK08	$20 × 10$	$[1.5,3.0]$	0.64(0.01)	0.86(0.01)( $+$ )	1.14(0.01)( $+$ )	0.69(0.01)( $+$ )	0.67(0.01)( $+$ )
MK09	$20 × 10$	$[1.0,4.0]$	0.45(0.01)	0.80(0.03)( $+$ )	1.13(0.01)( $+$ )	0.57(0.02)( $+$ )	0.61(0.02)( $+$ )
MK10	$20 × 15$	$[1.0,3.0]$	0.44(0.01)	0.79(0.02)( $+$ )	1.15(0.03)( $+$ )	0.56(0.02)( $+$ )	0.58(0.03)( $+$ )
MK11	$30 × 5$	$[1.0,8.0]$	0.66(0.01)	0.90(0.01)( $+$ )	1.13(0.04)( $+$ )	0.72(0.01)( $+$ )	0.73(0.01)( $+$ )
MK12	$30 × 10$	$[1.5,4.0]$	0.56(0.01)	0.85(0.01)( $+$ )	1.07(0.02)( $+$ )	0.65(0.01)( $+$ )	0.64(0.01)( $+$ )
MK13	$30 × 10$	$[1.0,5.0]$	0.48(0.01)	0.82(0.01)( $+$ )	1.07(0.02)( $+$ )	0.58(0.01)( $+$ )	0.61(0.01)( $+$ )
MK14	$30 × 15$	$[1.0,5.0]$	0.54(0.01)	0.90(0.01)( $+$ )	1.11(0.02)( $+$ )	0.68(0.02)( $+$ )	0.67(0.01)( $+$ )
MK15	$30 × 15$	$[1.0,4.0]$	0.42(0.02)	0.81(0.02)( $+$ )	1.10(0.04)( $+$ )	0.53(0.02)( $+$ )	0.54(0.02)( $+$ )

表3

图6

图7

表4

MK01工件完工期限信息

工件	$d i$	工件	$d i$	工件	$d i$	工件	$d i$	工件	$d i$
$J 1$	44.0	$J 3$	23.0	$J 5$	30.0	$J 7$	22.0	$J 9$	54.0
$J 2$	37.0	$J 4$	32.0	$J 6$	44.0	$J 8$	55.0	$J 10$	28.0

表4

图8

参考文献 22

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属性	特征	描述
任务特征	OPT	某工序所需加工时长
	APT	某工件所有工序所需加工时长总和
	LPT	某工序所属工件的上一工序完工时间
	EST	某工序的最早可开始加工时间
	NPT	某工序所属工件的下一工序所需加工时长
	RON	某工件剩余任务数量
	ROT	某工件剩余任务所需加工时长总和
	DRT	某工件的完工期限之前剩余时间
机器特征	MIT	某机器成为空闲机器的时刻
	WPT	等待在某机器上排序的任务所需加工时长总和
	NIT	在某机器上调度下一工序将产生的机器新增空载运行时长

基于混合决策机制的自适应生产调度优化与仿真

Optimization and Simulation of Adaptive Production Scheduling Based on Hybrid Decision-making Mechanism

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