Uncertain mean-variance model for dynamic project portfolio selection problem with divisibility
Abstract The project portfolio selection problem considering divisibility is a new research problem rising in recent years. However, two deficiencies are discovered in current divisible project portfolio selection research, one is that researchers always ignore the already started exiting projects w...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Li, Xingmei [verfasserIn] Wang, Yaxian [verfasserIn] Yan, Qingyou [verfasserIn] Zhao, Xinchao [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
Project portfolio selection problem |
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| Übergeordnetes Werk: |
Enthalten in: Fuzzy optimization and decision making - Dordrecht [u.a.] : Springer Science + Business Media B.V., 2002, 18(2018), 1 vom: 06. Feb., Seite 37-56 |
|---|---|
| Übergeordnetes Werk: |
volume:18 ; year:2018 ; number:1 ; day:06 ; month:02 ; pages:37-56 |
| Links: |
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| DOI / URN: |
10.1007/s10700-018-9283-6 |
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| Katalog-ID: |
SPR01267267X |
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| 520 | |a Abstract The project portfolio selection problem considering divisibility is a new research problem rising in recent years. However, two deficiencies are discovered in current divisible project portfolio selection research, one is that researchers always ignore the already started exiting projects when selecting a project portfolio, and the other is that the project parameters are all considered as exact values which are not consistent with practice situation. Under this circumstance, the paper first discusses the dynamic project portfolio selection problem with project divisibility. Meanwhile, due to the lack of correlative historical data, some project parameters are given by experts’ estimates and are treated as uncertain variables. Therefore, a mean-variance mixed integer nonlinear optimal selection model is first developed in this paper to deal with the uncertain dynamic project portfolio selection problem with divisibility. For the convenience of computations, an equivalent mixed integer linear programming representation is proposed. Numerical examples with two scenarios are presented to shed light on the characteristics of the proposed model. | ||
| 650 | 4 | |a Project portfolio selection problem |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Project divisibility |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Existing project adjustment (i.e. dynamic) |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Uncertainty theory |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Wang, Yaxian |e verfasserin |4 aut | |
| 700 | 1 | |a Yan, Qingyou |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Xinchao |e verfasserin |4 aut | |
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10.1007/s10700-018-9283-6 doi (DE-627)SPR01267267X (SPR)s10700-018-9283-6-e DE-627 ger DE-627 rakwb eng 004 ASE 54.72 bkl 31.80 bkl 85.03 bkl Li, Xingmei verfasserin aut Uncertain mean-variance model for dynamic project portfolio selection problem with divisibility 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The project portfolio selection problem considering divisibility is a new research problem rising in recent years. However, two deficiencies are discovered in current divisible project portfolio selection research, one is that researchers always ignore the already started exiting projects when selecting a project portfolio, and the other is that the project parameters are all considered as exact values which are not consistent with practice situation. Under this circumstance, the paper first discusses the dynamic project portfolio selection problem with project divisibility. Meanwhile, due to the lack of correlative historical data, some project parameters are given by experts’ estimates and are treated as uncertain variables. Therefore, a mean-variance mixed integer nonlinear optimal selection model is first developed in this paper to deal with the uncertain dynamic project portfolio selection problem with divisibility. For the convenience of computations, an equivalent mixed integer linear programming representation is proposed. Numerical examples with two scenarios are presented to shed light on the characteristics of the proposed model. Project portfolio selection problem (dpeaa)DE-He213 Project divisibility (dpeaa)DE-He213 Existing project adjustment (i.e. dynamic) (dpeaa)DE-He213 Uncertainty theory (dpeaa)DE-He213 Wang, Yaxian verfasserin aut Yan, Qingyou verfasserin aut Zhao, Xinchao verfasserin aut Enthalten in Fuzzy optimization and decision making Dordrecht [u.a.] : Springer Science + Business Media B.V., 2002 18(2018), 1 vom: 06. Feb., Seite 37-56 (DE-627)34087192X (DE-600)2065595-2 1573-2908 nnns volume:18 year:2018 number:1 day:06 month:02 pages:37-56 https://dx.doi.org/10.1007/s10700-018-9283-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE 31.80 ASE 85.03 ASE AR 18 2018 1 06 02 37-56 |
| spelling |
10.1007/s10700-018-9283-6 doi (DE-627)SPR01267267X (SPR)s10700-018-9283-6-e DE-627 ger DE-627 rakwb eng 004 ASE 54.72 bkl 31.80 bkl 85.03 bkl Li, Xingmei verfasserin aut Uncertain mean-variance model for dynamic project portfolio selection problem with divisibility 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The project portfolio selection problem considering divisibility is a new research problem rising in recent years. However, two deficiencies are discovered in current divisible project portfolio selection research, one is that researchers always ignore the already started exiting projects when selecting a project portfolio, and the other is that the project parameters are all considered as exact values which are not consistent with practice situation. Under this circumstance, the paper first discusses the dynamic project portfolio selection problem with project divisibility. Meanwhile, due to the lack of correlative historical data, some project parameters are given by experts’ estimates and are treated as uncertain variables. Therefore, a mean-variance mixed integer nonlinear optimal selection model is first developed in this paper to deal with the uncertain dynamic project portfolio selection problem with divisibility. For the convenience of computations, an equivalent mixed integer linear programming representation is proposed. Numerical examples with two scenarios are presented to shed light on the characteristics of the proposed model. Project portfolio selection problem (dpeaa)DE-He213 Project divisibility (dpeaa)DE-He213 Existing project adjustment (i.e. dynamic) (dpeaa)DE-He213 Uncertainty theory (dpeaa)DE-He213 Wang, Yaxian verfasserin aut Yan, Qingyou verfasserin aut Zhao, Xinchao verfasserin aut Enthalten in Fuzzy optimization and decision making Dordrecht [u.a.] : Springer Science + Business Media B.V., 2002 18(2018), 1 vom: 06. Feb., Seite 37-56 (DE-627)34087192X (DE-600)2065595-2 1573-2908 nnns volume:18 year:2018 number:1 day:06 month:02 pages:37-56 https://dx.doi.org/10.1007/s10700-018-9283-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE 31.80 ASE 85.03 ASE AR 18 2018 1 06 02 37-56 |
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10.1007/s10700-018-9283-6 doi (DE-627)SPR01267267X (SPR)s10700-018-9283-6-e DE-627 ger DE-627 rakwb eng 004 ASE 54.72 bkl 31.80 bkl 85.03 bkl Li, Xingmei verfasserin aut Uncertain mean-variance model for dynamic project portfolio selection problem with divisibility 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The project portfolio selection problem considering divisibility is a new research problem rising in recent years. However, two deficiencies are discovered in current divisible project portfolio selection research, one is that researchers always ignore the already started exiting projects when selecting a project portfolio, and the other is that the project parameters are all considered as exact values which are not consistent with practice situation. Under this circumstance, the paper first discusses the dynamic project portfolio selection problem with project divisibility. Meanwhile, due to the lack of correlative historical data, some project parameters are given by experts’ estimates and are treated as uncertain variables. Therefore, a mean-variance mixed integer nonlinear optimal selection model is first developed in this paper to deal with the uncertain dynamic project portfolio selection problem with divisibility. For the convenience of computations, an equivalent mixed integer linear programming representation is proposed. Numerical examples with two scenarios are presented to shed light on the characteristics of the proposed model. Project portfolio selection problem (dpeaa)DE-He213 Project divisibility (dpeaa)DE-He213 Existing project adjustment (i.e. dynamic) (dpeaa)DE-He213 Uncertainty theory (dpeaa)DE-He213 Wang, Yaxian verfasserin aut Yan, Qingyou verfasserin aut Zhao, Xinchao verfasserin aut Enthalten in Fuzzy optimization and decision making Dordrecht [u.a.] : Springer Science + Business Media B.V., 2002 18(2018), 1 vom: 06. Feb., Seite 37-56 (DE-627)34087192X (DE-600)2065595-2 1573-2908 nnns volume:18 year:2018 number:1 day:06 month:02 pages:37-56 https://dx.doi.org/10.1007/s10700-018-9283-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE 31.80 ASE 85.03 ASE AR 18 2018 1 06 02 37-56 |
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10.1007/s10700-018-9283-6 doi (DE-627)SPR01267267X (SPR)s10700-018-9283-6-e DE-627 ger DE-627 rakwb eng 004 ASE 54.72 bkl 31.80 bkl 85.03 bkl Li, Xingmei verfasserin aut Uncertain mean-variance model for dynamic project portfolio selection problem with divisibility 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The project portfolio selection problem considering divisibility is a new research problem rising in recent years. However, two deficiencies are discovered in current divisible project portfolio selection research, one is that researchers always ignore the already started exiting projects when selecting a project portfolio, and the other is that the project parameters are all considered as exact values which are not consistent with practice situation. Under this circumstance, the paper first discusses the dynamic project portfolio selection problem with project divisibility. Meanwhile, due to the lack of correlative historical data, some project parameters are given by experts’ estimates and are treated as uncertain variables. Therefore, a mean-variance mixed integer nonlinear optimal selection model is first developed in this paper to deal with the uncertain dynamic project portfolio selection problem with divisibility. For the convenience of computations, an equivalent mixed integer linear programming representation is proposed. Numerical examples with two scenarios are presented to shed light on the characteristics of the proposed model. Project portfolio selection problem (dpeaa)DE-He213 Project divisibility (dpeaa)DE-He213 Existing project adjustment (i.e. dynamic) (dpeaa)DE-He213 Uncertainty theory (dpeaa)DE-He213 Wang, Yaxian verfasserin aut Yan, Qingyou verfasserin aut Zhao, Xinchao verfasserin aut Enthalten in Fuzzy optimization and decision making Dordrecht [u.a.] : Springer Science + Business Media B.V., 2002 18(2018), 1 vom: 06. Feb., Seite 37-56 (DE-627)34087192X (DE-600)2065595-2 1573-2908 nnns volume:18 year:2018 number:1 day:06 month:02 pages:37-56 https://dx.doi.org/10.1007/s10700-018-9283-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE 31.80 ASE 85.03 ASE AR 18 2018 1 06 02 37-56 |
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10.1007/s10700-018-9283-6 doi (DE-627)SPR01267267X (SPR)s10700-018-9283-6-e DE-627 ger DE-627 rakwb eng 004 ASE 54.72 bkl 31.80 bkl 85.03 bkl Li, Xingmei verfasserin aut Uncertain mean-variance model for dynamic project portfolio selection problem with divisibility 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The project portfolio selection problem considering divisibility is a new research problem rising in recent years. However, two deficiencies are discovered in current divisible project portfolio selection research, one is that researchers always ignore the already started exiting projects when selecting a project portfolio, and the other is that the project parameters are all considered as exact values which are not consistent with practice situation. Under this circumstance, the paper first discusses the dynamic project portfolio selection problem with project divisibility. Meanwhile, due to the lack of correlative historical data, some project parameters are given by experts’ estimates and are treated as uncertain variables. Therefore, a mean-variance mixed integer nonlinear optimal selection model is first developed in this paper to deal with the uncertain dynamic project portfolio selection problem with divisibility. For the convenience of computations, an equivalent mixed integer linear programming representation is proposed. Numerical examples with two scenarios are presented to shed light on the characteristics of the proposed model. Project portfolio selection problem (dpeaa)DE-He213 Project divisibility (dpeaa)DE-He213 Existing project adjustment (i.e. dynamic) (dpeaa)DE-He213 Uncertainty theory (dpeaa)DE-He213 Wang, Yaxian verfasserin aut Yan, Qingyou verfasserin aut Zhao, Xinchao verfasserin aut Enthalten in Fuzzy optimization and decision making Dordrecht [u.a.] : Springer Science + Business Media B.V., 2002 18(2018), 1 vom: 06. Feb., Seite 37-56 (DE-627)34087192X (DE-600)2065595-2 1573-2908 nnns volume:18 year:2018 number:1 day:06 month:02 pages:37-56 https://dx.doi.org/10.1007/s10700-018-9283-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE 31.80 ASE 85.03 ASE AR 18 2018 1 06 02 37-56 |
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Enthalten in Fuzzy optimization and decision making 18(2018), 1 vom: 06. Feb., Seite 37-56 volume:18 year:2018 number:1 day:06 month:02 pages:37-56 |
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Project portfolio selection problem Project divisibility Existing project adjustment (i.e. dynamic) Uncertainty theory |
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Li, Xingmei @@aut@@ Wang, Yaxian @@aut@@ Yan, Qingyou @@aut@@ Zhao, Xinchao @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR01267267X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220110235022.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201005s2018 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10700-018-9283-6</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR01267267X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s10700-018-9283-6-e</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">rakwb</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">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.72</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">31.80</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">85.03</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Li, Xingmei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Uncertain mean-variance model for dynamic project portfolio selection problem with divisibility</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</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">Abstract The project portfolio selection problem considering divisibility is a new research problem rising in recent years. However, two deficiencies are discovered in current divisible project portfolio selection research, one is that researchers always ignore the already started exiting projects when selecting a project portfolio, and the other is that the project parameters are all considered as exact values which are not consistent with practice situation. Under this circumstance, the paper first discusses the dynamic project portfolio selection problem with project divisibility. Meanwhile, due to the lack of correlative historical data, some project parameters are given by experts’ estimates and are treated as uncertain variables. Therefore, a mean-variance mixed integer nonlinear optimal selection model is first developed in this paper to deal with the uncertain dynamic project portfolio selection problem with divisibility. For the convenience of computations, an equivalent mixed integer linear programming representation is proposed. Numerical examples with two scenarios are presented to shed light on the characteristics of the proposed model.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Project portfolio selection problem</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Project divisibility</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Existing project adjustment (i.e. dynamic)</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Uncertainty theory</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Yaxian</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yan, Qingyou</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhao, Xinchao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Fuzzy optimization and decision making</subfield><subfield code="d">Dordrecht [u.a.] : Springer Science + Business Media B.V., 2002</subfield><subfield code="g">18(2018), 1 vom: 06. 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Li, Xingmei |
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Li, Xingmei ddc 004 bkl 54.72 bkl 31.80 bkl 85.03 misc Project portfolio selection problem misc Project divisibility misc Existing project adjustment (i.e. dynamic) misc Uncertainty theory Uncertain mean-variance model for dynamic project portfolio selection problem with divisibility |
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004 ASE 54.72 bkl 31.80 bkl 85.03 bkl Uncertain mean-variance model for dynamic project portfolio selection problem with divisibility Project portfolio selection problem (dpeaa)DE-He213 Project divisibility (dpeaa)DE-He213 Existing project adjustment (i.e. dynamic) (dpeaa)DE-He213 Uncertainty theory (dpeaa)DE-He213 |
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ddc 004 bkl 54.72 bkl 31.80 bkl 85.03 misc Project portfolio selection problem misc Project divisibility misc Existing project adjustment (i.e. dynamic) misc Uncertainty theory |
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ddc 004 bkl 54.72 bkl 31.80 bkl 85.03 misc Project portfolio selection problem misc Project divisibility misc Existing project adjustment (i.e. dynamic) misc Uncertainty theory |
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uncertain mean-variance model for dynamic project portfolio selection problem with divisibility |
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Uncertain mean-variance model for dynamic project portfolio selection problem with divisibility |
| abstract |
Abstract The project portfolio selection problem considering divisibility is a new research problem rising in recent years. However, two deficiencies are discovered in current divisible project portfolio selection research, one is that researchers always ignore the already started exiting projects when selecting a project portfolio, and the other is that the project parameters are all considered as exact values which are not consistent with practice situation. Under this circumstance, the paper first discusses the dynamic project portfolio selection problem with project divisibility. Meanwhile, due to the lack of correlative historical data, some project parameters are given by experts’ estimates and are treated as uncertain variables. Therefore, a mean-variance mixed integer nonlinear optimal selection model is first developed in this paper to deal with the uncertain dynamic project portfolio selection problem with divisibility. For the convenience of computations, an equivalent mixed integer linear programming representation is proposed. Numerical examples with two scenarios are presented to shed light on the characteristics of the proposed model. |
| abstractGer |
Abstract The project portfolio selection problem considering divisibility is a new research problem rising in recent years. However, two deficiencies are discovered in current divisible project portfolio selection research, one is that researchers always ignore the already started exiting projects when selecting a project portfolio, and the other is that the project parameters are all considered as exact values which are not consistent with practice situation. Under this circumstance, the paper first discusses the dynamic project portfolio selection problem with project divisibility. Meanwhile, due to the lack of correlative historical data, some project parameters are given by experts’ estimates and are treated as uncertain variables. Therefore, a mean-variance mixed integer nonlinear optimal selection model is first developed in this paper to deal with the uncertain dynamic project portfolio selection problem with divisibility. For the convenience of computations, an equivalent mixed integer linear programming representation is proposed. Numerical examples with two scenarios are presented to shed light on the characteristics of the proposed model. |
| abstract_unstemmed |
Abstract The project portfolio selection problem considering divisibility is a new research problem rising in recent years. However, two deficiencies are discovered in current divisible project portfolio selection research, one is that researchers always ignore the already started exiting projects when selecting a project portfolio, and the other is that the project parameters are all considered as exact values which are not consistent with practice situation. Under this circumstance, the paper first discusses the dynamic project portfolio selection problem with project divisibility. Meanwhile, due to the lack of correlative historical data, some project parameters are given by experts’ estimates and are treated as uncertain variables. Therefore, a mean-variance mixed integer nonlinear optimal selection model is first developed in this paper to deal with the uncertain dynamic project portfolio selection problem with divisibility. For the convenience of computations, an equivalent mixed integer linear programming representation is proposed. Numerical examples with two scenarios are presented to shed light on the characteristics of the proposed model. |
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| container_issue |
1 |
| title_short |
Uncertain mean-variance model for dynamic project portfolio selection problem with divisibility |
| url |
https://dx.doi.org/10.1007/s10700-018-9283-6 |
| remote_bool |
true |
| author2 |
Wang, Yaxian Yan, Qingyou Zhao, Xinchao |
| author2Str |
Wang, Yaxian Yan, Qingyou Zhao, Xinchao |
| ppnlink |
34087192X |
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c |
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false |
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false |
| doi_str |
10.1007/s10700-018-9283-6 |
| up_date |
2024-07-03T14:31:58.440Z |
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1803568669990584320 |
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code="a">GBV_ILN_4246</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_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4336</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="q">ASE</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">31.80</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">85.03</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">18</subfield><subfield code="j">2018</subfield><subfield code="e">1</subfield><subfield code="b">06</subfield><subfield code="c">02</subfield><subfield code="h">37-56</subfield></datafield></record></collection>
|
| score |
7.402011 |