多目标下航天产品生产车间柔性资源配置与调度集成优化

doi:10.16381/j.cnki.issn1003-207x.2021.1831

Abstract

Abstract:

To cater to the multi-variety and small-batch characteristics of aerospace product manufacturing， flexible allocation and scheduling of resources in production workshops based on production planning is key to maintaining efficiency and response capability. In particular， an integrated optimization problem of resource allocation and scheduling is formulated and solved to minimize production delay， manufacturing costs， and total resources across multiple scenarios with various production resources， in-workshop transportation， and resource availability. A multi-objective programming model is first formulated based on a detailed delineation of our research questions. An improved multi-objective differential evolution （IMODE） algorithm is then put forward based on Pareto optimization to solve such an NP-hard problem efficiently. Upon obtaining a high-quality set of initial chromosomes based on heuristic search， the final solution of a Pareto set is derived from fast non-dominated sorting followed by iterative optimization through a differential evolution algorithm. Based on 11 scenarios with different combinations of job numbers and due dates generated from empirical data， IMODE on average outperforms NSGA-II and MOPSO， two widely-adopted algorithms for multi-objective optimization. Furthermore， our additional analyses identify a ceiling effect of resource quantity on overdue days and manufacturing costs. When there is a large number of jobs with insufficient resources， on the one hand， increasing the number of resources can mitigate production delays and costs. On the other hand， the allocation of more resources is unable to shorten overdue days or bring down costs in the context of a small job number with adequate production resources. In sum， flexible integrated resource allocation and scheduling through IMODE enhance resource utilization and manufacturing efficiency in the face of dynamic production demands.

Key words: flexible resource allocation and scheduling, multi-objective optimization, adaptive differential evolution algorithm, fast non-dominated sorting, heuristic strategy

CLC Number:

F273

Hui Li,Xi Wang,Zhiya Zuo. Multi-objective Integrated Optimization of Flexible Resource Allocation and Scheduling in the Aerospace Production Workshop[J]. Chinese Journal of Management Science, 2024, 32(10): 146-155.

Figures/Tables 9

算例	工序总数	超期时间 $f 1$ (天)	总成本 $f 2$ (千元)	资源投入数量 $f 3$
1	200	16	1911	47
		113	1893	45
		89	1900	36
2	221	5	2823	46
		126	2796	47
		44	2809	37
3	238	4	3339	46
		95	3319	46
		96	3331	35
4	262	0	3881	47
		86	3861	45
		23	3881	38
5	282	0	3901	42
		43	3889	48
		80	3907	37
6	300	0	4229	43
		48	4219	52
		226	4228	39
7	314	0	4308	44
		98	4302	51
		15	4324	37
8	345	0	4470	42
		20	4453	47
		56	4465	40
9	362	0	4536	51
		58	4531	47
		44	4546	40
10	401	0	4950	46
		78	4940	53
		278	4954	39
11	416	0	5471	45
		109	5466	52
		16	5485	41

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算例	工序总数	A: IMODE B: NSGA-Ⅱ		A: IMODE B: MOPSO		A: NSGA-Ⅱ B: MOPSO		IGD
算例	工序总数	C(A,B)	C(B,A)	C(A,B)	C(B,A)	C(A,B)	C(B,A)	IMODE	NSGA-Ⅱ	MOPSO
1	200	0.892768	0.000000	0.485149	0.000000	0.297030	0.446384	0.014751	0.023054	0.032566
2	221	0.927374	0.000000	0.650000	0.000000	0.166667	0.505587	0.013363	0.028650	0.027697
3	238	0.994475	0.000000	0.583333	0.000000	0.250000	0.078729	0.014245	0.044035	0.058596
4	262	0.944112	0.000000	0.413793	0.000000	0.146552	0.057884	0.021410	0.046133	0.041833
5	282	1.000000	0.000000	0.404762	0.000000	0.285714	0.000000	0.017126	0.043978	0.065493
6	300	0.894428	0.000000	0.546763	0.000000	0.287770	0.388563	0.025469	0.037573	0.026460
7	314	1.000000	0.000000	0.848739	0.000000	0.168067	0.292308	0.015110	0.043681	0.058750
8	345	0.995792	0.000000	0.415929	0.000000	0.079646	0.683029	0.016943	0.041294	0.018143
9	362	1.000000	0.000000	0.772358	0.000000	0.333333	0.555556	0.010847	0.027219	0.035677
10	401	1.000000	0.000000	0.464789	0.000000	0.084507	0.432191	0.018521	0.044088	0.018947
11	416	1.000000	0.000000	0.581818	0.000000	0.218182	0.170732	0.034609	0.048620	0.063734

算例	工序总数	运行时间
算例	工序总数	IMODE	NSGA-Ⅱ	MOPSO
1	200	3102.6	5726.8	3408.4
2	221	3350.2	6290.8	3684.5
3	238	3520.7	6894.1	4051.9
4	262	3806.2	7591.0	4271.0
5	282	4119.3	8155.5	4559.7
6	300	4360.1	8513.7	4810.7
7	314	4521.4	8822.7	5009.2
8	345	4890.0	10140.9	5449.7
9	362	5129.1	10745.2	5671.4
10	401	5672.5	11967.3	6244.4
11	416	5780.6	12356.6	6463.6

Multi-objective Integrated Optimization of Flexible Resource Allocation and Scheduling in the Aerospace Production Workshop

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Figures/Tables 9

References 27

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