An effective two-stage iterated greedy algorithm for distributed flowshop group scheduling problem with setup time

The distributed flow shop group scheduling problem (DFGSP) has wide industrial applications. Due to the strong coupling of DFGSP, three issues should be solved, such as assigning groups to factories, arranging the sequence of groups in each factory and scheduling the sequence of jobs in each group....
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Wang, Yuhang [verfasserIn] Han, Yuyan [verfasserIn] Wang, Yuting [verfasserIn] Li, Junqing [verfasserIn] Gao, Kaizhou [verfasserIn] Liu, Yiping [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Group scheduling Distributed flow shop Iterated greedy algorithm Makespan

Übergeordnetes Werk:	Enthalten in: Expert systems with applications - Amsterdam [u.a.] : Elsevier Science, 1990, 233
Übergeordnetes Werk:	volume:233

DOI / URN:	10.1016/j.eswa.2023.120909

Katalog-ID:	ELV06247698X

Internformat


LEADER	01000caa a22002652 4500
001	ELV06247698X
003	DE-627
005	20231210093216.0
007	cr uuu---uuuuu
008	230901s2023 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1016/j.eswa.2023.120909 \|2 doi
035			\|a (DE-627)ELV06247698X
035			\|a (ELSEVIER)S0957-4174(23)01411-2
040			\|a DE-627 \|b ger \|c DE-627 \|e rda
041			\|a eng
082	0	4	\|a 004 \|q VZ
084			\|a 54.72 \|2 bkl
100	1		\|a Wang, Yuhang \|e verfasserin \|0 (orcid)0000-0003-1131-9167 \|4 aut
245	1	0	\|a An effective two-stage iterated greedy algorithm for distributed flowshop group scheduling problem with setup time
264		1	\|c 2023
336			\|a nicht spezifiziert \|b zzz \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a The distributed flow shop group scheduling problem (DFGSP) has wide industrial applications. Due to the strong coupling of DFGSP, three issues should be solved, such as assigning groups to factories, arranging the sequence of groups in each factory and scheduling the sequence of jobs in each group. Meanwhile, the calculation for the objective has a high time complexity. To solve the above problems, we first build a mixed-integer linear programming model of DFGSP and verify its correctness by using the Gurobi solver. By exploring the implicit characteristics of the problem, two rapid evaluation methods based on group insertion and job insertion are designed to accelerate the evaluation of the objective. Then, an effective two-stage iterated greedy algorithm (tIGA) is proposed to solve the above three coupled subproblem. In the proposed tIGA, two intra-factory and inter-factory cooperative neighborhood search strategies and two intra-group and inter-group enhanced search strategies are proposed, respectively, to improve the search breadth and depth. The results of comprehensive statistical experiments on 810 test instances show that the proposed algorithm significantly outperforms the compared ones in terms of objective values and relative percentage increase values, and demonstrates the effectiveness of the proposed tIGA.
650		4	\|a Group scheduling
650		4	\|a Distributed flow shop
650		4	\|a Iterated greedy algorithm
650		4	\|a Makespan
700	1		\|a Han, Yuyan \|e verfasserin \|0 (orcid)0000-0001-8963-5421 \|4 aut
700	1		\|a Wang, Yuting \|e verfasserin \|0 (orcid)0000-0002-1562-2764 \|4 aut
700	1		\|a Li, Junqing \|e verfasserin \|0 (orcid)0000-0002-3617-6708 \|4 aut
700	1		\|a Gao, Kaizhou \|e verfasserin \|0 (orcid)0000-0002-9252-6928 \|4 aut
700	1		\|a Liu, Yiping \|e verfasserin \|0 (orcid)0000-0001-7340-2551 \|4 aut
773	0	8	\|i Enthalten in \|t Expert systems with applications \|d Amsterdam [u.a.] : Elsevier Science, 1990 \|g 233 \|h Online-Ressource \|w (DE-627)320577961 \|w (DE-600)2017237-0 \|w (DE-576)11481807X \|7 nnns
773	1	8	\|g volume:233
912			\|a GBV_USEFLAG_U
912			\|a GBV_ELV
912			\|a SYSFLAG_U
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_32
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_101
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
912			\|a GBV_ILN_4700
936	b	k	\|a 54.72 \|j Künstliche Intelligenz \|q VZ
951			\|a AR
952			\|d 233

Indexfelder

author_variant	y w yw y h yh y w yw j l jl k g kg y l yl
matchkey_str	wangyuhanghanyuyanwangyutinglijunqinggao:2023----:nfetvtotgieaegedagrtmodsrbtdlwhprush
hierarchy_sort_str	2023
bklnumber	54.72
publishDate	2023
allfields	10.1016/j.eswa.2023.120909 doi (DE-627)ELV06247698X (ELSEVIER)S0957-4174(23)01411-2 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Wang, Yuhang verfasserin (orcid)0000-0003-1131-9167 aut An effective two-stage iterated greedy algorithm for distributed flowshop group scheduling problem with setup time 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The distributed flow shop group scheduling problem (DFGSP) has wide industrial applications. Due to the strong coupling of DFGSP, three issues should be solved, such as assigning groups to factories, arranging the sequence of groups in each factory and scheduling the sequence of jobs in each group. Meanwhile, the calculation for the objective has a high time complexity. To solve the above problems, we first build a mixed-integer linear programming model of DFGSP and verify its correctness by using the Gurobi solver. By exploring the implicit characteristics of the problem, two rapid evaluation methods based on group insertion and job insertion are designed to accelerate the evaluation of the objective. Then, an effective two-stage iterated greedy algorithm (tIGA) is proposed to solve the above three coupled subproblem. In the proposed tIGA, two intra-factory and inter-factory cooperative neighborhood search strategies and two intra-group and inter-group enhanced search strategies are proposed, respectively, to improve the search breadth and depth. The results of comprehensive statistical experiments on 810 test instances show that the proposed algorithm significantly outperforms the compared ones in terms of objective values and relative percentage increase values, and demonstrates the effectiveness of the proposed tIGA. Group scheduling Distributed flow shop Iterated greedy algorithm Makespan Han, Yuyan verfasserin (orcid)0000-0001-8963-5421 aut Wang, Yuting verfasserin (orcid)0000-0002-1562-2764 aut Li, Junqing verfasserin (orcid)0000-0002-3617-6708 aut Gao, Kaizhou verfasserin (orcid)0000-0002-9252-6928 aut Liu, Yiping verfasserin (orcid)0000-0001-7340-2551 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 233 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:233 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 233
spelling	10.1016/j.eswa.2023.120909 doi (DE-627)ELV06247698X (ELSEVIER)S0957-4174(23)01411-2 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Wang, Yuhang verfasserin (orcid)0000-0003-1131-9167 aut An effective two-stage iterated greedy algorithm for distributed flowshop group scheduling problem with setup time 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The distributed flow shop group scheduling problem (DFGSP) has wide industrial applications. Due to the strong coupling of DFGSP, three issues should be solved, such as assigning groups to factories, arranging the sequence of groups in each factory and scheduling the sequence of jobs in each group. Meanwhile, the calculation for the objective has a high time complexity. To solve the above problems, we first build a mixed-integer linear programming model of DFGSP and verify its correctness by using the Gurobi solver. By exploring the implicit characteristics of the problem, two rapid evaluation methods based on group insertion and job insertion are designed to accelerate the evaluation of the objective. Then, an effective two-stage iterated greedy algorithm (tIGA) is proposed to solve the above three coupled subproblem. In the proposed tIGA, two intra-factory and inter-factory cooperative neighborhood search strategies and two intra-group and inter-group enhanced search strategies are proposed, respectively, to improve the search breadth and depth. The results of comprehensive statistical experiments on 810 test instances show that the proposed algorithm significantly outperforms the compared ones in terms of objective values and relative percentage increase values, and demonstrates the effectiveness of the proposed tIGA. Group scheduling Distributed flow shop Iterated greedy algorithm Makespan Han, Yuyan verfasserin (orcid)0000-0001-8963-5421 aut Wang, Yuting verfasserin (orcid)0000-0002-1562-2764 aut Li, Junqing verfasserin (orcid)0000-0002-3617-6708 aut Gao, Kaizhou verfasserin (orcid)0000-0002-9252-6928 aut Liu, Yiping verfasserin (orcid)0000-0001-7340-2551 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 233 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:233 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 233
allfields_unstemmed	10.1016/j.eswa.2023.120909 doi (DE-627)ELV06247698X (ELSEVIER)S0957-4174(23)01411-2 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Wang, Yuhang verfasserin (orcid)0000-0003-1131-9167 aut An effective two-stage iterated greedy algorithm for distributed flowshop group scheduling problem with setup time 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The distributed flow shop group scheduling problem (DFGSP) has wide industrial applications. Due to the strong coupling of DFGSP, three issues should be solved, such as assigning groups to factories, arranging the sequence of groups in each factory and scheduling the sequence of jobs in each group. Meanwhile, the calculation for the objective has a high time complexity. To solve the above problems, we first build a mixed-integer linear programming model of DFGSP and verify its correctness by using the Gurobi solver. By exploring the implicit characteristics of the problem, two rapid evaluation methods based on group insertion and job insertion are designed to accelerate the evaluation of the objective. Then, an effective two-stage iterated greedy algorithm (tIGA) is proposed to solve the above three coupled subproblem. In the proposed tIGA, two intra-factory and inter-factory cooperative neighborhood search strategies and two intra-group and inter-group enhanced search strategies are proposed, respectively, to improve the search breadth and depth. The results of comprehensive statistical experiments on 810 test instances show that the proposed algorithm significantly outperforms the compared ones in terms of objective values and relative percentage increase values, and demonstrates the effectiveness of the proposed tIGA. Group scheduling Distributed flow shop Iterated greedy algorithm Makespan Han, Yuyan verfasserin (orcid)0000-0001-8963-5421 aut Wang, Yuting verfasserin (orcid)0000-0002-1562-2764 aut Li, Junqing verfasserin (orcid)0000-0002-3617-6708 aut Gao, Kaizhou verfasserin (orcid)0000-0002-9252-6928 aut Liu, Yiping verfasserin (orcid)0000-0001-7340-2551 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 233 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:233 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 233
allfieldsGer	10.1016/j.eswa.2023.120909 doi (DE-627)ELV06247698X (ELSEVIER)S0957-4174(23)01411-2 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Wang, Yuhang verfasserin (orcid)0000-0003-1131-9167 aut An effective two-stage iterated greedy algorithm for distributed flowshop group scheduling problem with setup time 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The distributed flow shop group scheduling problem (DFGSP) has wide industrial applications. Due to the strong coupling of DFGSP, three issues should be solved, such as assigning groups to factories, arranging the sequence of groups in each factory and scheduling the sequence of jobs in each group. Meanwhile, the calculation for the objective has a high time complexity. To solve the above problems, we first build a mixed-integer linear programming model of DFGSP and verify its correctness by using the Gurobi solver. By exploring the implicit characteristics of the problem, two rapid evaluation methods based on group insertion and job insertion are designed to accelerate the evaluation of the objective. Then, an effective two-stage iterated greedy algorithm (tIGA) is proposed to solve the above three coupled subproblem. In the proposed tIGA, two intra-factory and inter-factory cooperative neighborhood search strategies and two intra-group and inter-group enhanced search strategies are proposed, respectively, to improve the search breadth and depth. The results of comprehensive statistical experiments on 810 test instances show that the proposed algorithm significantly outperforms the compared ones in terms of objective values and relative percentage increase values, and demonstrates the effectiveness of the proposed tIGA. Group scheduling Distributed flow shop Iterated greedy algorithm Makespan Han, Yuyan verfasserin (orcid)0000-0001-8963-5421 aut Wang, Yuting verfasserin (orcid)0000-0002-1562-2764 aut Li, Junqing verfasserin (orcid)0000-0002-3617-6708 aut Gao, Kaizhou verfasserin (orcid)0000-0002-9252-6928 aut Liu, Yiping verfasserin (orcid)0000-0001-7340-2551 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 233 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:233 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 233
allfieldsSound	10.1016/j.eswa.2023.120909 doi (DE-627)ELV06247698X (ELSEVIER)S0957-4174(23)01411-2 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Wang, Yuhang verfasserin (orcid)0000-0003-1131-9167 aut An effective two-stage iterated greedy algorithm for distributed flowshop group scheduling problem with setup time 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The distributed flow shop group scheduling problem (DFGSP) has wide industrial applications. Due to the strong coupling of DFGSP, three issues should be solved, such as assigning groups to factories, arranging the sequence of groups in each factory and scheduling the sequence of jobs in each group. Meanwhile, the calculation for the objective has a high time complexity. To solve the above problems, we first build a mixed-integer linear programming model of DFGSP and verify its correctness by using the Gurobi solver. By exploring the implicit characteristics of the problem, two rapid evaluation methods based on group insertion and job insertion are designed to accelerate the evaluation of the objective. Then, an effective two-stage iterated greedy algorithm (tIGA) is proposed to solve the above three coupled subproblem. In the proposed tIGA, two intra-factory and inter-factory cooperative neighborhood search strategies and two intra-group and inter-group enhanced search strategies are proposed, respectively, to improve the search breadth and depth. The results of comprehensive statistical experiments on 810 test instances show that the proposed algorithm significantly outperforms the compared ones in terms of objective values and relative percentage increase values, and demonstrates the effectiveness of the proposed tIGA. Group scheduling Distributed flow shop Iterated greedy algorithm Makespan Han, Yuyan verfasserin (orcid)0000-0001-8963-5421 aut Wang, Yuting verfasserin (orcid)0000-0002-1562-2764 aut Li, Junqing verfasserin (orcid)0000-0002-3617-6708 aut Gao, Kaizhou verfasserin (orcid)0000-0002-9252-6928 aut Liu, Yiping verfasserin (orcid)0000-0001-7340-2551 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 233 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:233 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 233
language	English
source	Enthalten in Expert systems with applications 233 volume:233
sourceStr	Enthalten in Expert systems with applications 233 volume:233
format_phy_str_mv	Article
bklname	Künstliche Intelligenz
institution	findex.gbv.de
topic_facet	Group scheduling Distributed flow shop Iterated greedy algorithm Makespan
dewey-raw	004
isfreeaccess_bool	false
container_title	Expert systems with applications
authorswithroles_txt_mv	Wang, Yuhang @@aut@@ Han, Yuyan @@aut@@ Wang, Yuting @@aut@@ Li, Junqing @@aut@@ Gao, Kaizhou @@aut@@ Liu, Yiping @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	320577961
dewey-sort	14
id	ELV06247698X
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV06247698X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231210093216.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230901s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.eswa.2023.120909</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV06247698X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0957-4174(23)01411-2</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.72</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wang, Yuhang</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-1131-9167</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">An effective two-stage iterated greedy algorithm for distributed flowshop group scheduling problem with setup time</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The distributed flow shop group scheduling problem (DFGSP) has wide industrial applications. Due to the strong coupling of DFGSP, three issues should be solved, such as assigning groups to factories, arranging the sequence of groups in each factory and scheduling the sequence of jobs in each group. Meanwhile, the calculation for the objective has a high time complexity. To solve the above problems, we first build a mixed-integer linear programming model of DFGSP and verify its correctness by using the Gurobi solver. By exploring the implicit characteristics of the problem, two rapid evaluation methods based on group insertion and job insertion are designed to accelerate the evaluation of the objective. Then, an effective two-stage iterated greedy algorithm (tIGA) is proposed to solve the above three coupled subproblem. In the proposed tIGA, two intra-factory and inter-factory cooperative neighborhood search strategies and two intra-group and inter-group enhanced search strategies are proposed, respectively, to improve the search breadth and depth. The results of comprehensive statistical experiments on 810 test instances show that the proposed algorithm significantly outperforms the compared ones in terms of objective values and relative percentage increase values, and demonstrates the effectiveness of the proposed tIGA.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Group scheduling</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Distributed flow shop</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Iterated greedy algorithm</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Makespan</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Han, Yuyan</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0001-8963-5421</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Yuting</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-1562-2764</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Junqing</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-3617-6708</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gao, Kaizhou</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-9252-6928</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Yiping</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0001-7340-2551</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Expert systems with applications</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier Science, 1990</subfield><subfield code="g">233</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)320577961</subfield><subfield code="w">(DE-600)2017237-0</subfield><subfield code="w">(DE-576)11481807X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:233</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_101</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.72</subfield><subfield code="j">Künstliche Intelligenz</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">233</subfield></datafield></record></collection>
author	Wang, Yuhang
spellingShingle	Wang, Yuhang ddc 004 bkl 54.72 misc Group scheduling misc Distributed flow shop misc Iterated greedy algorithm misc Makespan An effective two-stage iterated greedy algorithm for distributed flowshop group scheduling problem with setup time
authorStr	Wang, Yuhang
ppnlink_with_tag_str_mv	@@773@@(DE-627)320577961
format	electronic Article
dewey-ones	004 - Data processing & computer science
delete_txt_mv	keep
author_role	aut aut aut aut aut aut
collection	elsevier
remote_str	true
illustrated	Not Illustrated
topic_title	004 VZ 54.72 bkl An effective two-stage iterated greedy algorithm for distributed flowshop group scheduling problem with setup time Group scheduling Distributed flow shop Iterated greedy algorithm Makespan
topic	ddc 004 bkl 54.72 misc Group scheduling misc Distributed flow shop misc Iterated greedy algorithm misc Makespan
topic_unstemmed	ddc 004 bkl 54.72 misc Group scheduling misc Distributed flow shop misc Iterated greedy algorithm misc Makespan
topic_browse	ddc 004 bkl 54.72 misc Group scheduling misc Distributed flow shop misc Iterated greedy algorithm misc Makespan
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Expert systems with applications
hierarchy_parent_id	320577961
dewey-tens	000 - Computer science, knowledge & systems
hierarchy_top_title	Expert systems with applications
isfreeaccess_txt	false
familylinks_str_mv	(DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X
title	An effective two-stage iterated greedy algorithm for distributed flowshop group scheduling problem with setup time
ctrlnum	(DE-627)ELV06247698X (ELSEVIER)S0957-4174(23)01411-2
title_full	An effective two-stage iterated greedy algorithm for distributed flowshop group scheduling problem with setup time
author_sort	Wang, Yuhang
journal	Expert systems with applications
journalStr	Expert systems with applications
lang_code	eng
isOA_bool	false
dewey-hundreds	000 - Computer science, information & general works
recordtype	marc
publishDateSort	2023
contenttype_str_mv	zzz
author_browse	Wang, Yuhang Han, Yuyan Wang, Yuting Li, Junqing Gao, Kaizhou Liu, Yiping
container_volume	233
class	004 VZ 54.72 bkl
format_se	Elektronische Aufsätze
author-letter	Wang, Yuhang
doi_str_mv	10.1016/j.eswa.2023.120909
normlink	(ORCID)0000-0003-1131-9167 (ORCID)0000-0001-8963-5421 (ORCID)0000-0002-1562-2764 (ORCID)0000-0002-3617-6708 (ORCID)0000-0002-9252-6928 (ORCID)0000-0001-7340-2551
normlink_prefix_str_mv	(orcid)0000-0003-1131-9167 (orcid)0000-0001-8963-5421 (orcid)0000-0002-1562-2764 (orcid)0000-0002-3617-6708 (orcid)0000-0002-9252-6928 (orcid)0000-0001-7340-2551
dewey-full	004
author2-role	verfasserin
title_sort	an effective two-stage iterated greedy algorithm for distributed flowshop group scheduling problem with setup time
title_auth	An effective two-stage iterated greedy algorithm for distributed flowshop group scheduling problem with setup time
abstract	The distributed flow shop group scheduling problem (DFGSP) has wide industrial applications. Due to the strong coupling of DFGSP, three issues should be solved, such as assigning groups to factories, arranging the sequence of groups in each factory and scheduling the sequence of jobs in each group. Meanwhile, the calculation for the objective has a high time complexity. To solve the above problems, we first build a mixed-integer linear programming model of DFGSP and verify its correctness by using the Gurobi solver. By exploring the implicit characteristics of the problem, two rapid evaluation methods based on group insertion and job insertion are designed to accelerate the evaluation of the objective. Then, an effective two-stage iterated greedy algorithm (tIGA) is proposed to solve the above three coupled subproblem. In the proposed tIGA, two intra-factory and inter-factory cooperative neighborhood search strategies and two intra-group and inter-group enhanced search strategies are proposed, respectively, to improve the search breadth and depth. The results of comprehensive statistical experiments on 810 test instances show that the proposed algorithm significantly outperforms the compared ones in terms of objective values and relative percentage increase values, and demonstrates the effectiveness of the proposed tIGA.
abstractGer	The distributed flow shop group scheduling problem (DFGSP) has wide industrial applications. Due to the strong coupling of DFGSP, three issues should be solved, such as assigning groups to factories, arranging the sequence of groups in each factory and scheduling the sequence of jobs in each group. Meanwhile, the calculation for the objective has a high time complexity. To solve the above problems, we first build a mixed-integer linear programming model of DFGSP and verify its correctness by using the Gurobi solver. By exploring the implicit characteristics of the problem, two rapid evaluation methods based on group insertion and job insertion are designed to accelerate the evaluation of the objective. Then, an effective two-stage iterated greedy algorithm (tIGA) is proposed to solve the above three coupled subproblem. In the proposed tIGA, two intra-factory and inter-factory cooperative neighborhood search strategies and two intra-group and inter-group enhanced search strategies are proposed, respectively, to improve the search breadth and depth. The results of comprehensive statistical experiments on 810 test instances show that the proposed algorithm significantly outperforms the compared ones in terms of objective values and relative percentage increase values, and demonstrates the effectiveness of the proposed tIGA.
abstract_unstemmed	The distributed flow shop group scheduling problem (DFGSP) has wide industrial applications. Due to the strong coupling of DFGSP, three issues should be solved, such as assigning groups to factories, arranging the sequence of groups in each factory and scheduling the sequence of jobs in each group. Meanwhile, the calculation for the objective has a high time complexity. To solve the above problems, we first build a mixed-integer linear programming model of DFGSP and verify its correctness by using the Gurobi solver. By exploring the implicit characteristics of the problem, two rapid evaluation methods based on group insertion and job insertion are designed to accelerate the evaluation of the objective. Then, an effective two-stage iterated greedy algorithm (tIGA) is proposed to solve the above three coupled subproblem. In the proposed tIGA, two intra-factory and inter-factory cooperative neighborhood search strategies and two intra-group and inter-group enhanced search strategies are proposed, respectively, to improve the search breadth and depth. The results of comprehensive statistical experiments on 810 test instances show that the proposed algorithm significantly outperforms the compared ones in terms of objective values and relative percentage increase values, and demonstrates the effectiveness of the proposed tIGA.
collection_details	GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700
title_short	An effective two-stage iterated greedy algorithm for distributed flowshop group scheduling problem with setup time
remote_bool	true
author2	Han, Yuyan Wang, Yuting Li, Junqing Gao, Kaizhou Liu, Yiping
author2Str	Han, Yuyan Wang, Yuting Li, Junqing Gao, Kaizhou Liu, Yiping
ppnlink	320577961
mediatype_str_mv	c
isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1016/j.eswa.2023.120909
up_date	2024-07-06T18:50:35.361Z
_version_	1803856731555495936
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV06247698X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231210093216.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230901s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.eswa.2023.120909</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV06247698X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0957-4174(23)01411-2</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.72</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wang, Yuhang</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-1131-9167</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">An effective two-stage iterated greedy algorithm for distributed flowshop group scheduling problem with setup time</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The distributed flow shop group scheduling problem (DFGSP) has wide industrial applications. Due to the strong coupling of DFGSP, three issues should be solved, such as assigning groups to factories, arranging the sequence of groups in each factory and scheduling the sequence of jobs in each group. Meanwhile, the calculation for the objective has a high time complexity. To solve the above problems, we first build a mixed-integer linear programming model of DFGSP and verify its correctness by using the Gurobi solver. By exploring the implicit characteristics of the problem, two rapid evaluation methods based on group insertion and job insertion are designed to accelerate the evaluation of the objective. Then, an effective two-stage iterated greedy algorithm (tIGA) is proposed to solve the above three coupled subproblem. In the proposed tIGA, two intra-factory and inter-factory cooperative neighborhood search strategies and two intra-group and inter-group enhanced search strategies are proposed, respectively, to improve the search breadth and depth. The results of comprehensive statistical experiments on 810 test instances show that the proposed algorithm significantly outperforms the compared ones in terms of objective values and relative percentage increase values, and demonstrates the effectiveness of the proposed tIGA.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Group scheduling</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Distributed flow shop</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Iterated greedy algorithm</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Makespan</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Han, Yuyan</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0001-8963-5421</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Yuting</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-1562-2764</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Junqing</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-3617-6708</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gao, Kaizhou</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-9252-6928</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Yiping</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0001-7340-2551</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Expert systems with applications</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier Science, 1990</subfield><subfield code="g">233</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)320577961</subfield><subfield code="w">(DE-600)2017237-0</subfield><subfield code="w">(DE-576)11481807X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:233</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_101</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.72</subfield><subfield code="j">Künstliche Intelligenz</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">233</subfield></datafield></record></collection>
score	7.402915

Nicht das Richtige dabei?

Schreiben Sie uns!

An effective two-stage iterated greedy algorithm for distributed flowshop group scheduling problem with setup time

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?