A teaching-learning-based optimization with feedback for L-R fuzzy flexible assembly job shop scheduling problem with batch splitting

Batch splitting is an effective means to improve production efficiency. This paper integrates batch splitting into flexible assembly job shop scheduling problem. Complex manufacturing flexible system has high uncertainty. A L-R fuzzy number with nonlinear membership function is used to describe the...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Sun, Mengke [verfasserIn] Cai, Zongyan [verfasserIn] Zhang, Haonan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Flexible assembly job shop scheduling problem Batch splitting L-R fuzzy number Teaching-learning-based optimization with feedback

Übergeordnetes Werk:	Enthalten in: Expert systems with applications - Amsterdam [u.a.] : Elsevier Science, 1990, 224
Übergeordnetes Werk:	volume:224

DOI / URN:	10.1016/j.eswa.2023.120043

Katalog-ID:	ELV009789952

Internformat


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245	1	0	\|a A teaching-learning-based optimization with feedback for L-R fuzzy flexible assembly job shop scheduling problem with batch splitting
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520			\|a Batch splitting is an effective means to improve production efficiency. This paper integrates batch splitting into flexible assembly job shop scheduling problem. Complex manufacturing flexible system has high uncertainty. A L-R fuzzy number with nonlinear membership function is used to describe the time uncertainty of scheduling problem. Therefore, this paper proposes the L-R fuzzy flexible assembly job shop scheduling problem with batch splitting (BFFAJSP). BFFAJSP is a two-stage scheduling problem, namely the processing stage and the assembly stage. A multi-objective optimization model is formulated to optimize makespan, energy consumption and quality simultaneously. A teaching-learning-based optimization with feedback (FTLBO) is proposed to solve the proposed BFFAJSP. First, three initialization heuristics are designed to improve the quality and diversity of solutions. Next, the classes are divided based on non-dominated sorting and crowding distance. Then, the quality of the solution is improved based on feedback teaching. In addition, the communication between classes further improves the convergence speed of the algorithm. Finally, in the self-learning stage of teachers, the integrated five local search strategies improve the exploration and exploitation ability of the algorithm. The results of extensive numerical tests show the effectiveness of the proposed algorithm for solving BFFAJSP.
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matchkey_str	sunmengkecaizongyanzhanghaonan:2023----:tahnlannbsdpiiainihedakolfzylxbesebyososhd
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allfields	10.1016/j.eswa.2023.120043 doi (DE-627)ELV009789952 (ELSEVIER)S0957-4174(23)00545-6 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Sun, Mengke verfasserin aut A teaching-learning-based optimization with feedback for L-R fuzzy flexible assembly job shop scheduling problem with batch splitting 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Batch splitting is an effective means to improve production efficiency. This paper integrates batch splitting into flexible assembly job shop scheduling problem. Complex manufacturing flexible system has high uncertainty. A L-R fuzzy number with nonlinear membership function is used to describe the time uncertainty of scheduling problem. Therefore, this paper proposes the L-R fuzzy flexible assembly job shop scheduling problem with batch splitting (BFFAJSP). BFFAJSP is a two-stage scheduling problem, namely the processing stage and the assembly stage. A multi-objective optimization model is formulated to optimize makespan, energy consumption and quality simultaneously. A teaching-learning-based optimization with feedback (FTLBO) is proposed to solve the proposed BFFAJSP. First, three initialization heuristics are designed to improve the quality and diversity of solutions. Next, the classes are divided based on non-dominated sorting and crowding distance. Then, the quality of the solution is improved based on feedback teaching. In addition, the communication between classes further improves the convergence speed of the algorithm. Finally, in the self-learning stage of teachers, the integrated five local search strategies improve the exploration and exploitation ability of the algorithm. The results of extensive numerical tests show the effectiveness of the proposed algorithm for solving BFFAJSP. Flexible assembly job shop scheduling problem Batch splitting L-R fuzzy number Teaching-learning-based optimization with feedback Cai, Zongyan verfasserin (orcid)0000-0001-9738-2428 aut Zhang, Haonan verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 224 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:224 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 224
spelling	10.1016/j.eswa.2023.120043 doi (DE-627)ELV009789952 (ELSEVIER)S0957-4174(23)00545-6 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Sun, Mengke verfasserin aut A teaching-learning-based optimization with feedback for L-R fuzzy flexible assembly job shop scheduling problem with batch splitting 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Batch splitting is an effective means to improve production efficiency. This paper integrates batch splitting into flexible assembly job shop scheduling problem. Complex manufacturing flexible system has high uncertainty. A L-R fuzzy number with nonlinear membership function is used to describe the time uncertainty of scheduling problem. Therefore, this paper proposes the L-R fuzzy flexible assembly job shop scheduling problem with batch splitting (BFFAJSP). BFFAJSP is a two-stage scheduling problem, namely the processing stage and the assembly stage. A multi-objective optimization model is formulated to optimize makespan, energy consumption and quality simultaneously. A teaching-learning-based optimization with feedback (FTLBO) is proposed to solve the proposed BFFAJSP. First, three initialization heuristics are designed to improve the quality and diversity of solutions. Next, the classes are divided based on non-dominated sorting and crowding distance. Then, the quality of the solution is improved based on feedback teaching. In addition, the communication between classes further improves the convergence speed of the algorithm. Finally, in the self-learning stage of teachers, the integrated five local search strategies improve the exploration and exploitation ability of the algorithm. The results of extensive numerical tests show the effectiveness of the proposed algorithm for solving BFFAJSP. Flexible assembly job shop scheduling problem Batch splitting L-R fuzzy number Teaching-learning-based optimization with feedback Cai, Zongyan verfasserin (orcid)0000-0001-9738-2428 aut Zhang, Haonan verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 224 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:224 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 224
allfields_unstemmed	10.1016/j.eswa.2023.120043 doi (DE-627)ELV009789952 (ELSEVIER)S0957-4174(23)00545-6 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Sun, Mengke verfasserin aut A teaching-learning-based optimization with feedback for L-R fuzzy flexible assembly job shop scheduling problem with batch splitting 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Batch splitting is an effective means to improve production efficiency. This paper integrates batch splitting into flexible assembly job shop scheduling problem. Complex manufacturing flexible system has high uncertainty. A L-R fuzzy number with nonlinear membership function is used to describe the time uncertainty of scheduling problem. Therefore, this paper proposes the L-R fuzzy flexible assembly job shop scheduling problem with batch splitting (BFFAJSP). BFFAJSP is a two-stage scheduling problem, namely the processing stage and the assembly stage. A multi-objective optimization model is formulated to optimize makespan, energy consumption and quality simultaneously. A teaching-learning-based optimization with feedback (FTLBO) is proposed to solve the proposed BFFAJSP. First, three initialization heuristics are designed to improve the quality and diversity of solutions. Next, the classes are divided based on non-dominated sorting and crowding distance. Then, the quality of the solution is improved based on feedback teaching. In addition, the communication between classes further improves the convergence speed of the algorithm. Finally, in the self-learning stage of teachers, the integrated five local search strategies improve the exploration and exploitation ability of the algorithm. The results of extensive numerical tests show the effectiveness of the proposed algorithm for solving BFFAJSP. Flexible assembly job shop scheduling problem Batch splitting L-R fuzzy number Teaching-learning-based optimization with feedback Cai, Zongyan verfasserin (orcid)0000-0001-9738-2428 aut Zhang, Haonan verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 224 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:224 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 224
allfieldsGer	10.1016/j.eswa.2023.120043 doi (DE-627)ELV009789952 (ELSEVIER)S0957-4174(23)00545-6 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Sun, Mengke verfasserin aut A teaching-learning-based optimization with feedback for L-R fuzzy flexible assembly job shop scheduling problem with batch splitting 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Batch splitting is an effective means to improve production efficiency. This paper integrates batch splitting into flexible assembly job shop scheduling problem. Complex manufacturing flexible system has high uncertainty. A L-R fuzzy number with nonlinear membership function is used to describe the time uncertainty of scheduling problem. Therefore, this paper proposes the L-R fuzzy flexible assembly job shop scheduling problem with batch splitting (BFFAJSP). BFFAJSP is a two-stage scheduling problem, namely the processing stage and the assembly stage. A multi-objective optimization model is formulated to optimize makespan, energy consumption and quality simultaneously. A teaching-learning-based optimization with feedback (FTLBO) is proposed to solve the proposed BFFAJSP. First, three initialization heuristics are designed to improve the quality and diversity of solutions. Next, the classes are divided based on non-dominated sorting and crowding distance. Then, the quality of the solution is improved based on feedback teaching. In addition, the communication between classes further improves the convergence speed of the algorithm. Finally, in the self-learning stage of teachers, the integrated five local search strategies improve the exploration and exploitation ability of the algorithm. The results of extensive numerical tests show the effectiveness of the proposed algorithm for solving BFFAJSP. Flexible assembly job shop scheduling problem Batch splitting L-R fuzzy number Teaching-learning-based optimization with feedback Cai, Zongyan verfasserin (orcid)0000-0001-9738-2428 aut Zhang, Haonan verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 224 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:224 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 224
allfieldsSound	10.1016/j.eswa.2023.120043 doi (DE-627)ELV009789952 (ELSEVIER)S0957-4174(23)00545-6 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Sun, Mengke verfasserin aut A teaching-learning-based optimization with feedback for L-R fuzzy flexible assembly job shop scheduling problem with batch splitting 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Batch splitting is an effective means to improve production efficiency. This paper integrates batch splitting into flexible assembly job shop scheduling problem. Complex manufacturing flexible system has high uncertainty. A L-R fuzzy number with nonlinear membership function is used to describe the time uncertainty of scheduling problem. Therefore, this paper proposes the L-R fuzzy flexible assembly job shop scheduling problem with batch splitting (BFFAJSP). BFFAJSP is a two-stage scheduling problem, namely the processing stage and the assembly stage. A multi-objective optimization model is formulated to optimize makespan, energy consumption and quality simultaneously. A teaching-learning-based optimization with feedback (FTLBO) is proposed to solve the proposed BFFAJSP. First, three initialization heuristics are designed to improve the quality and diversity of solutions. Next, the classes are divided based on non-dominated sorting and crowding distance. Then, the quality of the solution is improved based on feedback teaching. In addition, the communication between classes further improves the convergence speed of the algorithm. Finally, in the self-learning stage of teachers, the integrated five local search strategies improve the exploration and exploitation ability of the algorithm. The results of extensive numerical tests show the effectiveness of the proposed algorithm for solving BFFAJSP. Flexible assembly job shop scheduling problem Batch splitting L-R fuzzy number Teaching-learning-based optimization with feedback Cai, Zongyan verfasserin (orcid)0000-0001-9738-2428 aut Zhang, Haonan verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 224 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:224 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 224
language	English
source	Enthalten in Expert systems with applications 224 volume:224
sourceStr	Enthalten in Expert systems with applications 224 volume:224
format_phy_str_mv	Article
bklname	Künstliche Intelligenz
institution	findex.gbv.de
topic_facet	Flexible assembly job shop scheduling problem Batch splitting L-R fuzzy number Teaching-learning-based optimization with feedback
dewey-raw	004
isfreeaccess_bool	false
container_title	Expert systems with applications
authorswithroles_txt_mv	Sun, Mengke @@aut@@ Cai, Zongyan @@aut@@ Zhang, Haonan @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	320577961
dewey-sort	14
id	ELV009789952
language_de	englisch
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author	Sun, Mengke
spellingShingle	Sun, Mengke ddc 004 bkl 54.72 misc Flexible assembly job shop scheduling problem misc Batch splitting misc L-R fuzzy number misc Teaching-learning-based optimization with feedback A teaching-learning-based optimization with feedback for L-R fuzzy flexible assembly job shop scheduling problem with batch splitting
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topic_title	004 VZ 54.72 bkl A teaching-learning-based optimization with feedback for L-R fuzzy flexible assembly job shop scheduling problem with batch splitting Flexible assembly job shop scheduling problem Batch splitting L-R fuzzy number Teaching-learning-based optimization with feedback
topic	ddc 004 bkl 54.72 misc Flexible assembly job shop scheduling problem misc Batch splitting misc L-R fuzzy number misc Teaching-learning-based optimization with feedback
topic_unstemmed	ddc 004 bkl 54.72 misc Flexible assembly job shop scheduling problem misc Batch splitting misc L-R fuzzy number misc Teaching-learning-based optimization with feedback
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title	A teaching-learning-based optimization with feedback for L-R fuzzy flexible assembly job shop scheduling problem with batch splitting
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title_full	A teaching-learning-based optimization with feedback for L-R fuzzy flexible assembly job shop scheduling problem with batch splitting
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title_sort	a teaching-learning-based optimization with feedback for l-r fuzzy flexible assembly job shop scheduling problem with batch splitting
title_auth	A teaching-learning-based optimization with feedback for L-R fuzzy flexible assembly job shop scheduling problem with batch splitting
abstract	Batch splitting is an effective means to improve production efficiency. This paper integrates batch splitting into flexible assembly job shop scheduling problem. Complex manufacturing flexible system has high uncertainty. A L-R fuzzy number with nonlinear membership function is used to describe the time uncertainty of scheduling problem. Therefore, this paper proposes the L-R fuzzy flexible assembly job shop scheduling problem with batch splitting (BFFAJSP). BFFAJSP is a two-stage scheduling problem, namely the processing stage and the assembly stage. A multi-objective optimization model is formulated to optimize makespan, energy consumption and quality simultaneously. A teaching-learning-based optimization with feedback (FTLBO) is proposed to solve the proposed BFFAJSP. First, three initialization heuristics are designed to improve the quality and diversity of solutions. Next, the classes are divided based on non-dominated sorting and crowding distance. Then, the quality of the solution is improved based on feedback teaching. In addition, the communication between classes further improves the convergence speed of the algorithm. Finally, in the self-learning stage of teachers, the integrated five local search strategies improve the exploration and exploitation ability of the algorithm. The results of extensive numerical tests show the effectiveness of the proposed algorithm for solving BFFAJSP.
abstractGer	Batch splitting is an effective means to improve production efficiency. This paper integrates batch splitting into flexible assembly job shop scheduling problem. Complex manufacturing flexible system has high uncertainty. A L-R fuzzy number with nonlinear membership function is used to describe the time uncertainty of scheduling problem. Therefore, this paper proposes the L-R fuzzy flexible assembly job shop scheduling problem with batch splitting (BFFAJSP). BFFAJSP is a two-stage scheduling problem, namely the processing stage and the assembly stage. A multi-objective optimization model is formulated to optimize makespan, energy consumption and quality simultaneously. A teaching-learning-based optimization with feedback (FTLBO) is proposed to solve the proposed BFFAJSP. First, three initialization heuristics are designed to improve the quality and diversity of solutions. Next, the classes are divided based on non-dominated sorting and crowding distance. Then, the quality of the solution is improved based on feedback teaching. In addition, the communication between classes further improves the convergence speed of the algorithm. Finally, in the self-learning stage of teachers, the integrated five local search strategies improve the exploration and exploitation ability of the algorithm. The results of extensive numerical tests show the effectiveness of the proposed algorithm for solving BFFAJSP.
abstract_unstemmed	Batch splitting is an effective means to improve production efficiency. This paper integrates batch splitting into flexible assembly job shop scheduling problem. Complex manufacturing flexible system has high uncertainty. A L-R fuzzy number with nonlinear membership function is used to describe the time uncertainty of scheduling problem. Therefore, this paper proposes the L-R fuzzy flexible assembly job shop scheduling problem with batch splitting (BFFAJSP). BFFAJSP is a two-stage scheduling problem, namely the processing stage and the assembly stage. A multi-objective optimization model is formulated to optimize makespan, energy consumption and quality simultaneously. A teaching-learning-based optimization with feedback (FTLBO) is proposed to solve the proposed BFFAJSP. First, three initialization heuristics are designed to improve the quality and diversity of solutions. Next, the classes are divided based on non-dominated sorting and crowding distance. Then, the quality of the solution is improved based on feedback teaching. In addition, the communication between classes further improves the convergence speed of the algorithm. Finally, in the self-learning stage of teachers, the integrated five local search strategies improve the exploration and exploitation ability of the algorithm. The results of extensive numerical tests show the effectiveness of the proposed algorithm for solving BFFAJSP.
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title_short	A teaching-learning-based optimization with feedback for L-R fuzzy flexible assembly job shop scheduling problem with batch splitting
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A teaching-learning-based optimization with feedback for L-R fuzzy flexible assembly job shop scheduling problem with batch splitting

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