Constraint programming and logic-based Benders decomposition for the integrated process planning and scheduling problem

The integrated process planning and scheduling (IPPS) problem is of critical importance in achieving desirable performance for complex manufacturing systems. The IPPS problem is often categorized into two types, i.e., Type-I and Type-II, depending on how the process plan is represented. In recent ye...
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Autor*in:	Zhu, Xuedong [verfasserIn] Son, Junbo [verfasserIn] Zhang, Xi [verfasserIn] Wu, Jianguo [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Integrated process planning and scheduling Constraint programming Logic-based Benders decomposition Forbidden intervals

Übergeordnetes Werk:	Enthalten in: Omega - Oxford [u.a.] : Elsevier, 1973, 117
Übergeordnetes Werk:	volume:117

DOI / URN:	10.1016/j.omega.2022.102823

Katalog-ID:	ELV064796361

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520			\|a The integrated process planning and scheduling (IPPS) problem is of critical importance in achieving desirable performance for complex manufacturing systems. The IPPS problem is often categorized into two types, i.e., Type-I and Type-II, depending on how the process plan is represented. In recent years, several approaches have been proposed to solve the IPPS problem in the literature. However, due to the complexity of the problem, optimal solutions of some benchmark datasets still cannot be obtained in a reasonable time, and few of them can be used to simultaneously address both types of IPPS problem. To this end, this study constructs a constraint programming (CP) model considering both types of IPPS problem, and proposes two basic logic-based Benders decomposition (LBBD) algorithms: one for each type of IPPS problem. In order to ensure computational efficiency, an enhanced LBBD algorithm is designed for both types of IPPS problem with three effective enhancement strategies. The performance of proposed methods is rigorously evaluated and compared with the existing approaches in the literature based on thirteen datasets. The results show that our methods significantly outperform these approaches.
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allfields	10.1016/j.omega.2022.102823 doi (DE-627)ELV064796361 (ELSEVIER)S0305-0483(22)00229-8 DE-627 ger DE-627 rda eng 650 VZ 85.06 bkl Zhu, Xuedong verfasserin aut Constraint programming and logic-based Benders decomposition for the integrated process planning and scheduling problem 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The integrated process planning and scheduling (IPPS) problem is of critical importance in achieving desirable performance for complex manufacturing systems. The IPPS problem is often categorized into two types, i.e., Type-I and Type-II, depending on how the process plan is represented. In recent years, several approaches have been proposed to solve the IPPS problem in the literature. However, due to the complexity of the problem, optimal solutions of some benchmark datasets still cannot be obtained in a reasonable time, and few of them can be used to simultaneously address both types of IPPS problem. To this end, this study constructs a constraint programming (CP) model considering both types of IPPS problem, and proposes two basic logic-based Benders decomposition (LBBD) algorithms: one for each type of IPPS problem. In order to ensure computational efficiency, an enhanced LBBD algorithm is designed for both types of IPPS problem with three effective enhancement strategies. The performance of proposed methods is rigorously evaluated and compared with the existing approaches in the literature based on thirteen datasets. The results show that our methods significantly outperform these approaches. Integrated process planning and scheduling Constraint programming Logic-based Benders decomposition Forbidden intervals Son, Junbo verfasserin aut Zhang, Xi verfasserin aut Wu, Jianguo verfasserin (orcid)0000-0002-2885-8725 aut Enthalten in Omega Oxford [u.a.] : Elsevier, 1973 117 Online-Ressource (DE-627)302466169 (DE-600)1491111-5 (DE-576)079719333 1873-5274 nnns volume:117 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_63 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_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 85.06 Unternehmensführung VZ AR 117
spelling	10.1016/j.omega.2022.102823 doi (DE-627)ELV064796361 (ELSEVIER)S0305-0483(22)00229-8 DE-627 ger DE-627 rda eng 650 VZ 85.06 bkl Zhu, Xuedong verfasserin aut Constraint programming and logic-based Benders decomposition for the integrated process planning and scheduling problem 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The integrated process planning and scheduling (IPPS) problem is of critical importance in achieving desirable performance for complex manufacturing systems. The IPPS problem is often categorized into two types, i.e., Type-I and Type-II, depending on how the process plan is represented. In recent years, several approaches have been proposed to solve the IPPS problem in the literature. However, due to the complexity of the problem, optimal solutions of some benchmark datasets still cannot be obtained in a reasonable time, and few of them can be used to simultaneously address both types of IPPS problem. To this end, this study constructs a constraint programming (CP) model considering both types of IPPS problem, and proposes two basic logic-based Benders decomposition (LBBD) algorithms: one for each type of IPPS problem. In order to ensure computational efficiency, an enhanced LBBD algorithm is designed for both types of IPPS problem with three effective enhancement strategies. The performance of proposed methods is rigorously evaluated and compared with the existing approaches in the literature based on thirteen datasets. The results show that our methods significantly outperform these approaches. Integrated process planning and scheduling Constraint programming Logic-based Benders decomposition Forbidden intervals Son, Junbo verfasserin aut Zhang, Xi verfasserin aut Wu, Jianguo verfasserin (orcid)0000-0002-2885-8725 aut Enthalten in Omega Oxford [u.a.] : Elsevier, 1973 117 Online-Ressource (DE-627)302466169 (DE-600)1491111-5 (DE-576)079719333 1873-5274 nnns volume:117 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_63 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_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 85.06 Unternehmensführung VZ AR 117
allfields_unstemmed	10.1016/j.omega.2022.102823 doi (DE-627)ELV064796361 (ELSEVIER)S0305-0483(22)00229-8 DE-627 ger DE-627 rda eng 650 VZ 85.06 bkl Zhu, Xuedong verfasserin aut Constraint programming and logic-based Benders decomposition for the integrated process planning and scheduling problem 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The integrated process planning and scheduling (IPPS) problem is of critical importance in achieving desirable performance for complex manufacturing systems. The IPPS problem is often categorized into two types, i.e., Type-I and Type-II, depending on how the process plan is represented. In recent years, several approaches have been proposed to solve the IPPS problem in the literature. However, due to the complexity of the problem, optimal solutions of some benchmark datasets still cannot be obtained in a reasonable time, and few of them can be used to simultaneously address both types of IPPS problem. To this end, this study constructs a constraint programming (CP) model considering both types of IPPS problem, and proposes two basic logic-based Benders decomposition (LBBD) algorithms: one for each type of IPPS problem. In order to ensure computational efficiency, an enhanced LBBD algorithm is designed for both types of IPPS problem with three effective enhancement strategies. The performance of proposed methods is rigorously evaluated and compared with the existing approaches in the literature based on thirteen datasets. The results show that our methods significantly outperform these approaches. Integrated process planning and scheduling Constraint programming Logic-based Benders decomposition Forbidden intervals Son, Junbo verfasserin aut Zhang, Xi verfasserin aut Wu, Jianguo verfasserin (orcid)0000-0002-2885-8725 aut Enthalten in Omega Oxford [u.a.] : Elsevier, 1973 117 Online-Ressource (DE-627)302466169 (DE-600)1491111-5 (DE-576)079719333 1873-5274 nnns volume:117 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_63 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_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 85.06 Unternehmensführung VZ AR 117
allfieldsGer	10.1016/j.omega.2022.102823 doi (DE-627)ELV064796361 (ELSEVIER)S0305-0483(22)00229-8 DE-627 ger DE-627 rda eng 650 VZ 85.06 bkl Zhu, Xuedong verfasserin aut Constraint programming and logic-based Benders decomposition for the integrated process planning and scheduling problem 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The integrated process planning and scheduling (IPPS) problem is of critical importance in achieving desirable performance for complex manufacturing systems. The IPPS problem is often categorized into two types, i.e., Type-I and Type-II, depending on how the process plan is represented. In recent years, several approaches have been proposed to solve the IPPS problem in the literature. However, due to the complexity of the problem, optimal solutions of some benchmark datasets still cannot be obtained in a reasonable time, and few of them can be used to simultaneously address both types of IPPS problem. To this end, this study constructs a constraint programming (CP) model considering both types of IPPS problem, and proposes two basic logic-based Benders decomposition (LBBD) algorithms: one for each type of IPPS problem. In order to ensure computational efficiency, an enhanced LBBD algorithm is designed for both types of IPPS problem with three effective enhancement strategies. The performance of proposed methods is rigorously evaluated and compared with the existing approaches in the literature based on thirteen datasets. The results show that our methods significantly outperform these approaches. Integrated process planning and scheduling Constraint programming Logic-based Benders decomposition Forbidden intervals Son, Junbo verfasserin aut Zhang, Xi verfasserin aut Wu, Jianguo verfasserin (orcid)0000-0002-2885-8725 aut Enthalten in Omega Oxford [u.a.] : Elsevier, 1973 117 Online-Ressource (DE-627)302466169 (DE-600)1491111-5 (DE-576)079719333 1873-5274 nnns volume:117 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_63 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_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 85.06 Unternehmensführung VZ AR 117
allfieldsSound	10.1016/j.omega.2022.102823 doi (DE-627)ELV064796361 (ELSEVIER)S0305-0483(22)00229-8 DE-627 ger DE-627 rda eng 650 VZ 85.06 bkl Zhu, Xuedong verfasserin aut Constraint programming and logic-based Benders decomposition for the integrated process planning and scheduling problem 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The integrated process planning and scheduling (IPPS) problem is of critical importance in achieving desirable performance for complex manufacturing systems. The IPPS problem is often categorized into two types, i.e., Type-I and Type-II, depending on how the process plan is represented. In recent years, several approaches have been proposed to solve the IPPS problem in the literature. However, due to the complexity of the problem, optimal solutions of some benchmark datasets still cannot be obtained in a reasonable time, and few of them can be used to simultaneously address both types of IPPS problem. To this end, this study constructs a constraint programming (CP) model considering both types of IPPS problem, and proposes two basic logic-based Benders decomposition (LBBD) algorithms: one for each type of IPPS problem. In order to ensure computational efficiency, an enhanced LBBD algorithm is designed for both types of IPPS problem with three effective enhancement strategies. The performance of proposed methods is rigorously evaluated and compared with the existing approaches in the literature based on thirteen datasets. The results show that our methods significantly outperform these approaches. Integrated process planning and scheduling Constraint programming Logic-based Benders decomposition Forbidden intervals Son, Junbo verfasserin aut Zhang, Xi verfasserin aut Wu, Jianguo verfasserin (orcid)0000-0002-2885-8725 aut Enthalten in Omega Oxford [u.a.] : Elsevier, 1973 117 Online-Ressource (DE-627)302466169 (DE-600)1491111-5 (DE-576)079719333 1873-5274 nnns volume:117 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_63 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_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 85.06 Unternehmensführung VZ AR 117
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authorswithroles_txt_mv	Zhu, Xuedong @@aut@@ Son, Junbo @@aut@@ Zhang, Xi @@aut@@ Wu, Jianguo @@aut@@
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author	Zhu, Xuedong
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topic_title	650 VZ 85.06 bkl Constraint programming and logic-based Benders decomposition for the integrated process planning and scheduling problem Integrated process planning and scheduling Constraint programming Logic-based Benders decomposition Forbidden intervals
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title	Constraint programming and logic-based Benders decomposition for the integrated process planning and scheduling problem
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title_sort	constraint programming and logic-based benders decomposition for the integrated process planning and scheduling problem
title_auth	Constraint programming and logic-based Benders decomposition for the integrated process planning and scheduling problem
abstract	The integrated process planning and scheduling (IPPS) problem is of critical importance in achieving desirable performance for complex manufacturing systems. The IPPS problem is often categorized into two types, i.e., Type-I and Type-II, depending on how the process plan is represented. In recent years, several approaches have been proposed to solve the IPPS problem in the literature. However, due to the complexity of the problem, optimal solutions of some benchmark datasets still cannot be obtained in a reasonable time, and few of them can be used to simultaneously address both types of IPPS problem. To this end, this study constructs a constraint programming (CP) model considering both types of IPPS problem, and proposes two basic logic-based Benders decomposition (LBBD) algorithms: one for each type of IPPS problem. In order to ensure computational efficiency, an enhanced LBBD algorithm is designed for both types of IPPS problem with three effective enhancement strategies. The performance of proposed methods is rigorously evaluated and compared with the existing approaches in the literature based on thirteen datasets. The results show that our methods significantly outperform these approaches.
abstractGer	The integrated process planning and scheduling (IPPS) problem is of critical importance in achieving desirable performance for complex manufacturing systems. The IPPS problem is often categorized into two types, i.e., Type-I and Type-II, depending on how the process plan is represented. In recent years, several approaches have been proposed to solve the IPPS problem in the literature. However, due to the complexity of the problem, optimal solutions of some benchmark datasets still cannot be obtained in a reasonable time, and few of them can be used to simultaneously address both types of IPPS problem. To this end, this study constructs a constraint programming (CP) model considering both types of IPPS problem, and proposes two basic logic-based Benders decomposition (LBBD) algorithms: one for each type of IPPS problem. In order to ensure computational efficiency, an enhanced LBBD algorithm is designed for both types of IPPS problem with three effective enhancement strategies. The performance of proposed methods is rigorously evaluated and compared with the existing approaches in the literature based on thirteen datasets. The results show that our methods significantly outperform these approaches.
abstract_unstemmed	The integrated process planning and scheduling (IPPS) problem is of critical importance in achieving desirable performance for complex manufacturing systems. The IPPS problem is often categorized into two types, i.e., Type-I and Type-II, depending on how the process plan is represented. In recent years, several approaches have been proposed to solve the IPPS problem in the literature. However, due to the complexity of the problem, optimal solutions of some benchmark datasets still cannot be obtained in a reasonable time, and few of them can be used to simultaneously address both types of IPPS problem. To this end, this study constructs a constraint programming (CP) model considering both types of IPPS problem, and proposes two basic logic-based Benders decomposition (LBBD) algorithms: one for each type of IPPS problem. In order to ensure computational efficiency, an enhanced LBBD algorithm is designed for both types of IPPS problem with three effective enhancement strategies. The performance of proposed methods is rigorously evaluated and compared with the existing approaches in the literature based on thirteen datasets. The results show that our methods significantly outperform these approaches.
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title_short	Constraint programming and logic-based Benders decomposition for the integrated process planning and scheduling problem
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doi_str	10.1016/j.omega.2022.102823
up_date	2024-07-06T20:47:08.732Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">ELV064796361</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230926130843.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230926s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.omega.2022.102823</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV064796361</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0305-0483(22)00229-8</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">650</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">85.06</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zhu, Xuedong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Constraint programming and logic-based Benders decomposition for the integrated process planning and scheduling problem</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</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 integrated process planning and scheduling (IPPS) problem is of critical importance in achieving desirable performance for complex manufacturing systems. 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Constraint programming and logic-based Benders decomposition for the integrated process planning and scheduling problem

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