A two-stage stochastic optimization model for port infrastructure planning
Gespeichert in:
| Autor*in: |
Bhurtyal, Sanjeev [verfasserIn] Hernandez, Sarah [verfasserIn] Eksioglu, Sandra [verfasserIn] Yves, Manzi [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
Benders decomposition algorithm |
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| Übergeordnetes Werk: |
Enthalten in: Maritime economics & logistics - [London] : Palgrave Macmillan, 2003, 26(2024), 2 vom: Juni, Seite 185-211 |
|---|---|
| Übergeordnetes Werk: |
volume:26 ; year:2024 ; number:2 ; month:06 ; pages:185-211 |
| Links: |
|---|
| DOI / URN: |
10.1057/s41278-023-00262-0 |
|---|
| Katalog-ID: |
1892250284 |
|---|
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| 982 | |2 26 |1 00 |x DE-206 |b This paper investigates inland port infrastructure investment planning under uncertain commodity (such as coal, petroleum, manufactured products, nonmetallic minerals) demand conditions. A two-stage stochastic optimization is developed to model the impact of demand uncertainty on infrastructure planning and transportation decisions. The model minimizes expected total costs, including capacity expansion costs, associated with handling equipment and storage infrastructure, and the expected transportation costs. To solve the problem, an accelerated Benders decomposition algorithm is implemented. The use of a stochastic approach is justified by comparing the value of stochastic solution with its corresponding deterministic solution. For demonstration, the model is applied to the Arkansas section of the McClellan-Kerr Arkansas River Navigation System (MKARNS). Given data availability, the model is generalizable to other regions. Results show that as investment in port capacities (handling equipment and storage infrastructure) increases by $8 million, the percent of commodity volumes that moves via waterways (in ton-miles) increases by 1%. For the Arkansas application, the model determines nonmetallic minerals as the most affected commodity by investment, and it identifies a cluster of ports at Little Rock where the investment would have the most significant impact. The contribution of the paper is in introducing a stochastic modeling framework to quantify mode shift dependencies on inland waterways port infrastructure (handling equipment and storage). Comparison of a stochastic approach to the state-of-the-literature deterministic approaches, shows that a failure to use a stochastic modeling to capture uncertainty in commodity demand could cost up to $21 M per year. The model serves as a decision-making tool for optimal, distributed allocation of monetary investments, that encourages mode shift to inland waterways. | ||
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10.1057/s41278-023-00262-0 doi (DE-627)1892250284 (DE-599)KXP1892250284 DE-627 ger DE-627 rda eng Bhurtyal, Sanjeev verfasserin aut A two-stage stochastic optimization model for port infrastructure planning Sanjeev Bhurtyal, Sarah Hernandez, Sandra Eksioglu, Manzi Yves 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Benders decomposition algorithm (dpeaa)DE-206 Inland waterway port (dpeaa)DE-206 Port infrastructure investment (dpeaa)DE-206 Stochastic programming (dpeaa)DE-206 Hernandez, Sarah verfasserin (DE-588)1334099138 (DE-627)1892251566 aut Eksioglu, Sandra verfasserin aut Yves, Manzi verfasserin aut Enthalten in Maritime economics & logistics [London] : Palgrave Macmillan, 2003 26(2024), 2 vom: Juni, Seite 185-211 Online-Ressource (DE-627)329557645 (DE-600)2047902-5 (DE-576)375631976 1479-294X nnns volume:26 year:2024 number:2 month:06 pages:185-211 https://link.springer.com/content/pdf/10.1057/s41278-023-00262-0.pdf Verlag lizenzpflichtig https://doi.org/10.1057/s41278-023-00262-0 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_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_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 26 2024 2 6 185-211 26 01 0206 4541928043 x1z 26-06-24 26 00 DE-206 This paper investigates inland port infrastructure investment planning under uncertain commodity (such as coal, petroleum, manufactured products, nonmetallic minerals) demand conditions. A two-stage stochastic optimization is developed to model the impact of demand uncertainty on infrastructure planning and transportation decisions. The model minimizes expected total costs, including capacity expansion costs, associated with handling equipment and storage infrastructure, and the expected transportation costs. To solve the problem, an accelerated Benders decomposition algorithm is implemented. The use of a stochastic approach is justified by comparing the value of stochastic solution with its corresponding deterministic solution. For demonstration, the model is applied to the Arkansas section of the McClellan-Kerr Arkansas River Navigation System (MKARNS). Given data availability, the model is generalizable to other regions. Results show that as investment in port capacities (handling equipment and storage infrastructure) increases by $8 million, the percent of commodity volumes that moves via waterways (in ton-miles) increases by 1%. For the Arkansas application, the model determines nonmetallic minerals as the most affected commodity by investment, and it identifies a cluster of ports at Little Rock where the investment would have the most significant impact. The contribution of the paper is in introducing a stochastic modeling framework to quantify mode shift dependencies on inland waterways port infrastructure (handling equipment and storage). Comparison of a stochastic approach to the state-of-the-literature deterministic approaches, shows that a failure to use a stochastic modeling to capture uncertainty in commodity demand could cost up to $21 M per year. The model serves as a decision-making tool for optimal, distributed allocation of monetary investments, that encourages mode shift to inland waterways. |
| spelling |
10.1057/s41278-023-00262-0 doi (DE-627)1892250284 (DE-599)KXP1892250284 DE-627 ger DE-627 rda eng Bhurtyal, Sanjeev verfasserin aut A two-stage stochastic optimization model for port infrastructure planning Sanjeev Bhurtyal, Sarah Hernandez, Sandra Eksioglu, Manzi Yves 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Benders decomposition algorithm (dpeaa)DE-206 Inland waterway port (dpeaa)DE-206 Port infrastructure investment (dpeaa)DE-206 Stochastic programming (dpeaa)DE-206 Hernandez, Sarah verfasserin (DE-588)1334099138 (DE-627)1892251566 aut Eksioglu, Sandra verfasserin aut Yves, Manzi verfasserin aut Enthalten in Maritime economics & logistics [London] : Palgrave Macmillan, 2003 26(2024), 2 vom: Juni, Seite 185-211 Online-Ressource (DE-627)329557645 (DE-600)2047902-5 (DE-576)375631976 1479-294X nnns volume:26 year:2024 number:2 month:06 pages:185-211 https://link.springer.com/content/pdf/10.1057/s41278-023-00262-0.pdf Verlag lizenzpflichtig https://doi.org/10.1057/s41278-023-00262-0 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_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_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 26 2024 2 6 185-211 26 01 0206 4541928043 x1z 26-06-24 26 00 DE-206 This paper investigates inland port infrastructure investment planning under uncertain commodity (such as coal, petroleum, manufactured products, nonmetallic minerals) demand conditions. A two-stage stochastic optimization is developed to model the impact of demand uncertainty on infrastructure planning and transportation decisions. The model minimizes expected total costs, including capacity expansion costs, associated with handling equipment and storage infrastructure, and the expected transportation costs. To solve the problem, an accelerated Benders decomposition algorithm is implemented. The use of a stochastic approach is justified by comparing the value of stochastic solution with its corresponding deterministic solution. For demonstration, the model is applied to the Arkansas section of the McClellan-Kerr Arkansas River Navigation System (MKARNS). Given data availability, the model is generalizable to other regions. Results show that as investment in port capacities (handling equipment and storage infrastructure) increases by $8 million, the percent of commodity volumes that moves via waterways (in ton-miles) increases by 1%. For the Arkansas application, the model determines nonmetallic minerals as the most affected commodity by investment, and it identifies a cluster of ports at Little Rock where the investment would have the most significant impact. The contribution of the paper is in introducing a stochastic modeling framework to quantify mode shift dependencies on inland waterways port infrastructure (handling equipment and storage). Comparison of a stochastic approach to the state-of-the-literature deterministic approaches, shows that a failure to use a stochastic modeling to capture uncertainty in commodity demand could cost up to $21 M per year. The model serves as a decision-making tool for optimal, distributed allocation of monetary investments, that encourages mode shift to inland waterways. |
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10.1057/s41278-023-00262-0 doi (DE-627)1892250284 (DE-599)KXP1892250284 DE-627 ger DE-627 rda eng Bhurtyal, Sanjeev verfasserin aut A two-stage stochastic optimization model for port infrastructure planning Sanjeev Bhurtyal, Sarah Hernandez, Sandra Eksioglu, Manzi Yves 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Benders decomposition algorithm (dpeaa)DE-206 Inland waterway port (dpeaa)DE-206 Port infrastructure investment (dpeaa)DE-206 Stochastic programming (dpeaa)DE-206 Hernandez, Sarah verfasserin (DE-588)1334099138 (DE-627)1892251566 aut Eksioglu, Sandra verfasserin aut Yves, Manzi verfasserin aut Enthalten in Maritime economics & logistics [London] : Palgrave Macmillan, 2003 26(2024), 2 vom: Juni, Seite 185-211 Online-Ressource (DE-627)329557645 (DE-600)2047902-5 (DE-576)375631976 1479-294X nnns volume:26 year:2024 number:2 month:06 pages:185-211 https://link.springer.com/content/pdf/10.1057/s41278-023-00262-0.pdf Verlag lizenzpflichtig https://doi.org/10.1057/s41278-023-00262-0 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_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_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 26 2024 2 6 185-211 26 01 0206 4541928043 x1z 26-06-24 26 00 DE-206 This paper investigates inland port infrastructure investment planning under uncertain commodity (such as coal, petroleum, manufactured products, nonmetallic minerals) demand conditions. A two-stage stochastic optimization is developed to model the impact of demand uncertainty on infrastructure planning and transportation decisions. The model minimizes expected total costs, including capacity expansion costs, associated with handling equipment and storage infrastructure, and the expected transportation costs. To solve the problem, an accelerated Benders decomposition algorithm is implemented. The use of a stochastic approach is justified by comparing the value of stochastic solution with its corresponding deterministic solution. For demonstration, the model is applied to the Arkansas section of the McClellan-Kerr Arkansas River Navigation System (MKARNS). Given data availability, the model is generalizable to other regions. Results show that as investment in port capacities (handling equipment and storage infrastructure) increases by $8 million, the percent of commodity volumes that moves via waterways (in ton-miles) increases by 1%. For the Arkansas application, the model determines nonmetallic minerals as the most affected commodity by investment, and it identifies a cluster of ports at Little Rock where the investment would have the most significant impact. The contribution of the paper is in introducing a stochastic modeling framework to quantify mode shift dependencies on inland waterways port infrastructure (handling equipment and storage). Comparison of a stochastic approach to the state-of-the-literature deterministic approaches, shows that a failure to use a stochastic modeling to capture uncertainty in commodity demand could cost up to $21 M per year. The model serves as a decision-making tool for optimal, distributed allocation of monetary investments, that encourages mode shift to inland waterways. |
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10.1057/s41278-023-00262-0 doi (DE-627)1892250284 (DE-599)KXP1892250284 DE-627 ger DE-627 rda eng Bhurtyal, Sanjeev verfasserin aut A two-stage stochastic optimization model for port infrastructure planning Sanjeev Bhurtyal, Sarah Hernandez, Sandra Eksioglu, Manzi Yves 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Benders decomposition algorithm (dpeaa)DE-206 Inland waterway port (dpeaa)DE-206 Port infrastructure investment (dpeaa)DE-206 Stochastic programming (dpeaa)DE-206 Hernandez, Sarah verfasserin (DE-588)1334099138 (DE-627)1892251566 aut Eksioglu, Sandra verfasserin aut Yves, Manzi verfasserin aut Enthalten in Maritime economics & logistics [London] : Palgrave Macmillan, 2003 26(2024), 2 vom: Juni, Seite 185-211 Online-Ressource (DE-627)329557645 (DE-600)2047902-5 (DE-576)375631976 1479-294X nnns volume:26 year:2024 number:2 month:06 pages:185-211 https://link.springer.com/content/pdf/10.1057/s41278-023-00262-0.pdf Verlag lizenzpflichtig https://doi.org/10.1057/s41278-023-00262-0 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_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_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 26 2024 2 6 185-211 26 01 0206 4541928043 x1z 26-06-24 26 00 DE-206 This paper investigates inland port infrastructure investment planning under uncertain commodity (such as coal, petroleum, manufactured products, nonmetallic minerals) demand conditions. A two-stage stochastic optimization is developed to model the impact of demand uncertainty on infrastructure planning and transportation decisions. The model minimizes expected total costs, including capacity expansion costs, associated with handling equipment and storage infrastructure, and the expected transportation costs. To solve the problem, an accelerated Benders decomposition algorithm is implemented. The use of a stochastic approach is justified by comparing the value of stochastic solution with its corresponding deterministic solution. For demonstration, the model is applied to the Arkansas section of the McClellan-Kerr Arkansas River Navigation System (MKARNS). Given data availability, the model is generalizable to other regions. Results show that as investment in port capacities (handling equipment and storage infrastructure) increases by $8 million, the percent of commodity volumes that moves via waterways (in ton-miles) increases by 1%. For the Arkansas application, the model determines nonmetallic minerals as the most affected commodity by investment, and it identifies a cluster of ports at Little Rock where the investment would have the most significant impact. The contribution of the paper is in introducing a stochastic modeling framework to quantify mode shift dependencies on inland waterways port infrastructure (handling equipment and storage). Comparison of a stochastic approach to the state-of-the-literature deterministic approaches, shows that a failure to use a stochastic modeling to capture uncertainty in commodity demand could cost up to $21 M per year. The model serves as a decision-making tool for optimal, distributed allocation of monetary investments, that encourages mode shift to inland waterways. |
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10.1057/s41278-023-00262-0 doi (DE-627)1892250284 (DE-599)KXP1892250284 DE-627 ger DE-627 rda eng Bhurtyal, Sanjeev verfasserin aut A two-stage stochastic optimization model for port infrastructure planning Sanjeev Bhurtyal, Sarah Hernandez, Sandra Eksioglu, Manzi Yves 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Benders decomposition algorithm (dpeaa)DE-206 Inland waterway port (dpeaa)DE-206 Port infrastructure investment (dpeaa)DE-206 Stochastic programming (dpeaa)DE-206 Hernandez, Sarah verfasserin (DE-588)1334099138 (DE-627)1892251566 aut Eksioglu, Sandra verfasserin aut Yves, Manzi verfasserin aut Enthalten in Maritime economics & logistics [London] : Palgrave Macmillan, 2003 26(2024), 2 vom: Juni, Seite 185-211 Online-Ressource (DE-627)329557645 (DE-600)2047902-5 (DE-576)375631976 1479-294X nnns volume:26 year:2024 number:2 month:06 pages:185-211 https://link.springer.com/content/pdf/10.1057/s41278-023-00262-0.pdf Verlag lizenzpflichtig https://doi.org/10.1057/s41278-023-00262-0 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_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_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 26 2024 2 6 185-211 26 01 0206 4541928043 x1z 26-06-24 26 00 DE-206 This paper investigates inland port infrastructure investment planning under uncertain commodity (such as coal, petroleum, manufactured products, nonmetallic minerals) demand conditions. A two-stage stochastic optimization is developed to model the impact of demand uncertainty on infrastructure planning and transportation decisions. The model minimizes expected total costs, including capacity expansion costs, associated with handling equipment and storage infrastructure, and the expected transportation costs. To solve the problem, an accelerated Benders decomposition algorithm is implemented. The use of a stochastic approach is justified by comparing the value of stochastic solution with its corresponding deterministic solution. For demonstration, the model is applied to the Arkansas section of the McClellan-Kerr Arkansas River Navigation System (MKARNS). Given data availability, the model is generalizable to other regions. Results show that as investment in port capacities (handling equipment and storage infrastructure) increases by $8 million, the percent of commodity volumes that moves via waterways (in ton-miles) increases by 1%. For the Arkansas application, the model determines nonmetallic minerals as the most affected commodity by investment, and it identifies a cluster of ports at Little Rock where the investment would have the most significant impact. The contribution of the paper is in introducing a stochastic modeling framework to quantify mode shift dependencies on inland waterways port infrastructure (handling equipment and storage). Comparison of a stochastic approach to the state-of-the-literature deterministic approaches, shows that a failure to use a stochastic modeling to capture uncertainty in commodity demand could cost up to $21 M per year. The model serves as a decision-making tool for optimal, distributed allocation of monetary investments, that encourages mode shift to inland waterways. |
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Enthalten in Maritime economics & logistics 26(2024), 2 vom: Juni, Seite 185-211 volume:26 year:2024 number:2 month:06 pages:185-211 |
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Bhurtyal, Sanjeev @@aut@@ Hernandez, Sarah @@aut@@ Eksioglu, Sandra @@aut@@ Yves, Manzi @@aut@@ |
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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="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">26</subfield><subfield code="j">2024</subfield><subfield code="e">2</subfield><subfield code="c">6</subfield><subfield code="h">185-211</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">01</subfield><subfield code="x">0206</subfield><subfield code="b">4541928043</subfield><subfield code="y">x1z</subfield><subfield code="z">26-06-24</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="b">This paper investigates inland port infrastructure investment planning under uncertain commodity (such as coal, petroleum, manufactured products, nonmetallic minerals) demand conditions. A two-stage stochastic optimization is developed to model the impact of demand uncertainty on infrastructure planning and transportation decisions. The model minimizes expected total costs, including capacity expansion costs, associated with handling equipment and storage infrastructure, and the expected transportation costs. To solve the problem, an accelerated Benders decomposition algorithm is implemented. The use of a stochastic approach is justified by comparing the value of stochastic solution with its corresponding deterministic solution. For demonstration, the model is applied to the Arkansas section of the McClellan-Kerr Arkansas River Navigation System (MKARNS). Given data availability, the model is generalizable to other regions. Results show that as investment in port capacities (handling equipment and storage infrastructure) increases by $8 million, the percent of commodity volumes that moves via waterways (in ton-miles) increases by 1%. For the Arkansas application, the model determines nonmetallic minerals as the most affected commodity by investment, and it identifies a cluster of ports at Little Rock where the investment would have the most significant impact. The contribution of the paper is in introducing a stochastic modeling framework to quantify mode shift dependencies on inland waterways port infrastructure (handling equipment and storage). Comparison of a stochastic approach to the state-of-the-literature deterministic approaches, shows that a failure to use a stochastic modeling to capture uncertainty in commodity demand could cost up to $21 M per year. The model serves as a decision-making tool for optimal, distributed allocation of monetary investments, that encourages mode shift to inland waterways.</subfield></datafield></record></collection>
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Bhurtyal, Sanjeev misc Benders decomposition algorithm misc Inland waterway port misc Port infrastructure investment misc Stochastic programming A two-stage stochastic optimization model for port infrastructure planning |
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26 00 DE-206 This paper investigates inland port infrastructure investment planning under uncertain commodity (such as coal, petroleum, manufactured products, nonmetallic minerals) demand conditions. A two-stage stochastic optimization is developed to model the impact of demand uncertainty on infrastructure planning and transportation decisions. The model minimizes expected total costs, including capacity expansion costs, associated with handling equipment and storage infrastructure, and the expected transportation costs. To solve the problem, an accelerated Benders decomposition algorithm is implemented. The use of a stochastic approach is justified by comparing the value of stochastic solution with its corresponding deterministic solution. For demonstration, the model is applied to the Arkansas section of the McClellan-Kerr Arkansas River Navigation System (MKARNS). Given data availability, the model is generalizable to other regions. Results show that as investment in port capacities (handling equipment and storage infrastructure) increases by $8 million, the percent of commodity volumes that moves via waterways (in ton-miles) increases by 1%. For the Arkansas application, the model determines nonmetallic minerals as the most affected commodity by investment, and it identifies a cluster of ports at Little Rock where the investment would have the most significant impact. The contribution of the paper is in introducing a stochastic modeling framework to quantify mode shift dependencies on inland waterways port infrastructure (handling equipment and storage). Comparison of a stochastic approach to the state-of-the-literature deterministic approaches, shows that a failure to use a stochastic modeling to capture uncertainty in commodity demand could cost up to $21 M per year. The model serves as a decision-making tool for optimal, distributed allocation of monetary investments, that encourages mode shift to inland waterways A two-stage stochastic optimization model for port infrastructure planning Sanjeev Bhurtyal, Sarah Hernandez, Sandra Eksioglu, Manzi Yves Benders decomposition algorithm (dpeaa)DE-206 Inland waterway port (dpeaa)DE-206 Port infrastructure investment (dpeaa)DE-206 Stochastic programming (dpeaa)DE-206 |
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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="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">26</subfield><subfield code="j">2024</subfield><subfield code="e">2</subfield><subfield code="c">6</subfield><subfield code="h">185-211</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">01</subfield><subfield code="x">0206</subfield><subfield code="b">4541928043</subfield><subfield code="y">x1z</subfield><subfield code="z">26-06-24</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="b">This paper investigates inland port infrastructure investment planning under uncertain commodity (such as coal, petroleum, manufactured products, nonmetallic minerals) demand conditions. A two-stage stochastic optimization is developed to model the impact of demand uncertainty on infrastructure planning and transportation decisions. The model minimizes expected total costs, including capacity expansion costs, associated with handling equipment and storage infrastructure, and the expected transportation costs. To solve the problem, an accelerated Benders decomposition algorithm is implemented. The use of a stochastic approach is justified by comparing the value of stochastic solution with its corresponding deterministic solution. For demonstration, the model is applied to the Arkansas section of the McClellan-Kerr Arkansas River Navigation System (MKARNS). Given data availability, the model is generalizable to other regions. Results show that as investment in port capacities (handling equipment and storage infrastructure) increases by $8 million, the percent of commodity volumes that moves via waterways (in ton-miles) increases by 1%. For the Arkansas application, the model determines nonmetallic minerals as the most affected commodity by investment, and it identifies a cluster of ports at Little Rock where the investment would have the most significant impact. The contribution of the paper is in introducing a stochastic modeling framework to quantify mode shift dependencies on inland waterways port infrastructure (handling equipment and storage). Comparison of a stochastic approach to the state-of-the-literature deterministic approaches, shows that a failure to use a stochastic modeling to capture uncertainty in commodity demand could cost up to $21 M per year. The model serves as a decision-making tool for optimal, distributed allocation of monetary investments, that encourages mode shift to inland waterways.</subfield></datafield></record></collection>
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| score |
7.3970747 |