Improved Hybrid Optimization Algorithm for Multi-objective IPPS Problem

doi:10.16182/j.issn1004731x.joss.24-0031

Abstract

Abstract:

For the problem of multi-objective integrated process planning and scheduling (MOIPPS), an improved hybrid optimization algorithm considering global and local optimum is proposed to optimize two objectives about minimum makespan and energy consumption. A multi-objective problem model and solution framework are established by analyzing the difference and connection between process planning and scheduling in integrated system. A hybrid optimization algorithm is proposed for the two-stage integration problem. In the process planning stage, global search algorithm is employed to provide a variety of process schemes for the integrated system and to ensure the global search performance of the integrated algorithm. With regard to the scheduling stage, an improved tabu search algorithm is proposed, of which, crossover and random sampling operator is aimed at expanding the solution searching region and neighborhood tabu search is promoting the algorithm to converge instantly respectively. Pareto non-dominated sorting is employed to acquire the global optimal solution. Through contrastive analysis on diverse experiment results, the efficiency and consistency of the proposed algorithm is verified in solving multi-objective integrated process planning and scheduling problems.

Key words: integrated system, process planning and scheduling, hybrid algorithm, multi-objective optimal, energy conservation and emission reduction

CLC Number:

TP391.9

Gu Wenbin, Qing Jiexia, Fang Jie, Liu Siqi. Improved Hybrid Optimization Algorithm for Multi-objective IPPS Problem[J]. Journal of System Simulation, 2025, 37(5): 1197-1209.

Figures/Tables 18

Table 1

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Table 2

Table 3

Table 4

Job information in different scale problems

问题	规模(工件数 $n$ ×机器数 $m$ )	工件序号
1	6×5	job1~6
2	6×5	job2~7
3	6×5	job3~8
4	6×5	job9~14
5	6×5	job10~15
6	6×5	job11~16
7	6×5	job1,3,6,8,10,15
8	6×5	job2,4,5,7,11,14
9	6×5	job2,6,9,12,13,16
10	10×5	job1~5,9~13
11	10×5	job4~8,12~16
12	10×5	job1,3,5,7~10,12,14,16
13	10×5	job1~9,13
14	10×5	job7~16
15	15×10	job1~15
16	15×10	job1~14,16
17	15×10	job1~11,13~16
18	16×10	job1~16

Table 4

Fig. 11

Table 5

Table 6

Fig. 12

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符号	描述
N	工件总数
M	机器总数
I	工件索引
J	待加工工序索引
K	机器索引
m_k	第k个机器
O_ij	工件i的第j个加工工序
H_i	工件i总加工工序数
f_i	工件i的加工特征数
p_ix	加工特征f_i 的可选工艺方案数(x = 1, 2, …, f_i )
q_ixy	工艺方案p_ix 所需加工的工序数(y = 1, 2, …, p_ix )
SH_i	工件i的加工工艺路线确认后待加工的工序数
M_ij	工序O_ij 的可加工机器合集
t_ijk	工序O_ij 在机器m_k 上的加工时间
P_ijk	工序O_ij 在机器m_k 上的加工功率
C_ij	工序O_ij 的完工时间
P_f^k	机器m_k 的待机功率
P_a	车间辅助功率
X_ijk	1，如果O_ij 被安排在机器m_k 上；否则，0
Y_ijpq	1，如果O_ij 是O_pq 的上一道工序；0，如果O_ij 是O_pq 的下一道工序

参数名称	取值
集成系统最大迭代次数I_bm	200
工艺阶段迭代次数I_ps	与I_bm同步
工艺阶段种群规模G_p	100
调度阶段最大迭代次数I_s	50
调度阶段种群规模G_s	50
新个体容量V_n	20
禁忌最大迭代次数I_ts	20
禁忌长度L_ts	randi(5,9)
邻域解个数Ca	10

问题	工件数	SEA	GATS	HA	ICA	THA	SMGAVNS	HMS-GA	IHOA
1	6	428	427	483	427	427	427	427	427
2	6	343	343	383	343	343	343	343	301
3	6	347	345	386	345	344	344	344	344
4	6	306	306	328	306	306	306	306	301
5	6	319	319	348	319	319	316	318	304
6	6	438	427	506	435	427	427	427	427
7	6	372	372	386	372	372	372	372	372
8	6	343	343	376	343	348	351	342	320
9	6	428	427	507	427	427	427	427	427
10	9	443	427	504	440	427	427	427	427
11	9	369	369	413	367	365	350	349	344
12	9	328	326	361	327	321	317	321	301
13	9	452	428	505	457	434	427	428	428
14	9	381	380	423	390	385	393	374	372
15	9	434	427	496	432	427	427	427	427
16	12	454	435	521	466	430	437	428	434
17	12	431	423	474	443	418	414	367	362
18	12	379	349	417	384	353	358	335	331
19	12	490	474	550	490	470	462	434	428
20	12	447	432	473	440	431	419	390	386
21	12	477	427	525	466	430	427	427	427
22	15	534	513	560	529	489	476	443	444
23	15	498	470	533	495	453	440	415	415
24	18	587	539	607	577	511	493	475	475

目标值	最大完工时间/min	能耗/kW
最高	358.00	68.73
最低	355.00	66.90
平均	355.97	67.65

优化目标		单目标值
优化目标		完工时间/min	能耗/kW
完工时间最小	最高	358	83.10
	最低	354	83.13
	平均	355.23	82.55
能耗最小	最高	366	69.44
	最低	362	67.72
	平均	365	68.53