Formulation of a nonlinear water distribution problem with water rationing: an optimisation approach
Abstract A mixed-integer nonlinear programming water distribution problem that incorporates water rationing is presented. The city of Bulawayo water distribution problem is implemented and solved using max–min ant system, genetic, tabu search, and simulated annealing algorithms with 100 runs perform...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Chagwiza, Godfrey [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer International Publishing Switzerland 2016 |
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| Übergeordnetes Werk: |
Enthalten in: Sustainable Water Resources Management - Cham : Springer International Publishers, 2015, 2(2016), 4 vom: 13. Sept., Seite 379-385 |
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| Übergeordnetes Werk: |
volume:2 ; year:2016 ; number:4 ; day:13 ; month:09 ; pages:379-385 |
| Links: |
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| DOI / URN: |
10.1007/s40899-016-0066-3 |
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| Katalog-ID: |
SPR037977466 |
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| 520 | |a Abstract A mixed-integer nonlinear programming water distribution problem that incorporates water rationing is presented. The city of Bulawayo water distribution problem is implemented and solved using max–min ant system, genetic, tabu search, and simulated annealing algorithms with 100 runs performed for each algorithm. The results show that the city of Bulawayo can save $3158 a day. The max–min ant system produced the best optimal costs compared with the other algorithms. The least run time is obtained by implementing the tabu search algorithm. Water lost through hoarding during water-rationing periods contributes significantly to the total operational costs. Statistical analysis of the results obtained by different algorithms shows that the optimal costs obtained by tabu search, and simulated annealing algorithms are insignificantly different. Future research may be directed toward incorporating priority among water users and formulating a hybrid algorithm that uses both the max–min ant system and tabu search algorithms to solve such problems. | ||
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10.1007/s40899-016-0066-3 doi (DE-627)SPR037977466 (SPR)s40899-016-0066-3-e DE-627 ger DE-627 rakwb eng Chagwiza, Godfrey verfasserin aut Formulation of a nonlinear water distribution problem with water rationing: an optimisation approach 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer International Publishing Switzerland 2016 Abstract A mixed-integer nonlinear programming water distribution problem that incorporates water rationing is presented. The city of Bulawayo water distribution problem is implemented and solved using max–min ant system, genetic, tabu search, and simulated annealing algorithms with 100 runs performed for each algorithm. The results show that the city of Bulawayo can save $3158 a day. The max–min ant system produced the best optimal costs compared with the other algorithms. The least run time is obtained by implementing the tabu search algorithm. Water lost through hoarding during water-rationing periods contributes significantly to the total operational costs. Statistical analysis of the results obtained by different algorithms shows that the optimal costs obtained by tabu search, and simulated annealing algorithms are insignificantly different. Future research may be directed toward incorporating priority among water users and formulating a hybrid algorithm that uses both the max–min ant system and tabu search algorithms to solve such problems. Mixed-integer nonlinear programming (dpeaa)DE-He213 Water rationing (dpeaa)DE-He213 Optimisation (dpeaa)DE-He213 Enthalten in Sustainable Water Resources Management Cham : Springer International Publishers, 2015 2(2016), 4 vom: 13. Sept., Seite 379-385 (DE-627)827029845 (DE-600)2823488-1 2363-5045 nnns volume:2 year:2016 number:4 day:13 month:09 pages:379-385 https://dx.doi.org/10.1007/s40899-016-0066-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 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_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 AR 2 2016 4 13 09 379-385 |
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10.1007/s40899-016-0066-3 doi (DE-627)SPR037977466 (SPR)s40899-016-0066-3-e DE-627 ger DE-627 rakwb eng Chagwiza, Godfrey verfasserin aut Formulation of a nonlinear water distribution problem with water rationing: an optimisation approach 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer International Publishing Switzerland 2016 Abstract A mixed-integer nonlinear programming water distribution problem that incorporates water rationing is presented. The city of Bulawayo water distribution problem is implemented and solved using max–min ant system, genetic, tabu search, and simulated annealing algorithms with 100 runs performed for each algorithm. The results show that the city of Bulawayo can save $3158 a day. The max–min ant system produced the best optimal costs compared with the other algorithms. The least run time is obtained by implementing the tabu search algorithm. Water lost through hoarding during water-rationing periods contributes significantly to the total operational costs. Statistical analysis of the results obtained by different algorithms shows that the optimal costs obtained by tabu search, and simulated annealing algorithms are insignificantly different. Future research may be directed toward incorporating priority among water users and formulating a hybrid algorithm that uses both the max–min ant system and tabu search algorithms to solve such problems. Mixed-integer nonlinear programming (dpeaa)DE-He213 Water rationing (dpeaa)DE-He213 Optimisation (dpeaa)DE-He213 Enthalten in Sustainable Water Resources Management Cham : Springer International Publishers, 2015 2(2016), 4 vom: 13. Sept., Seite 379-385 (DE-627)827029845 (DE-600)2823488-1 2363-5045 nnns volume:2 year:2016 number:4 day:13 month:09 pages:379-385 https://dx.doi.org/10.1007/s40899-016-0066-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 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_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 AR 2 2016 4 13 09 379-385 |
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10.1007/s40899-016-0066-3 doi (DE-627)SPR037977466 (SPR)s40899-016-0066-3-e DE-627 ger DE-627 rakwb eng Chagwiza, Godfrey verfasserin aut Formulation of a nonlinear water distribution problem with water rationing: an optimisation approach 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer International Publishing Switzerland 2016 Abstract A mixed-integer nonlinear programming water distribution problem that incorporates water rationing is presented. The city of Bulawayo water distribution problem is implemented and solved using max–min ant system, genetic, tabu search, and simulated annealing algorithms with 100 runs performed for each algorithm. The results show that the city of Bulawayo can save $3158 a day. The max–min ant system produced the best optimal costs compared with the other algorithms. The least run time is obtained by implementing the tabu search algorithm. Water lost through hoarding during water-rationing periods contributes significantly to the total operational costs. Statistical analysis of the results obtained by different algorithms shows that the optimal costs obtained by tabu search, and simulated annealing algorithms are insignificantly different. Future research may be directed toward incorporating priority among water users and formulating a hybrid algorithm that uses both the max–min ant system and tabu search algorithms to solve such problems. Mixed-integer nonlinear programming (dpeaa)DE-He213 Water rationing (dpeaa)DE-He213 Optimisation (dpeaa)DE-He213 Enthalten in Sustainable Water Resources Management Cham : Springer International Publishers, 2015 2(2016), 4 vom: 13. Sept., Seite 379-385 (DE-627)827029845 (DE-600)2823488-1 2363-5045 nnns volume:2 year:2016 number:4 day:13 month:09 pages:379-385 https://dx.doi.org/10.1007/s40899-016-0066-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 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_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 AR 2 2016 4 13 09 379-385 |
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10.1007/s40899-016-0066-3 doi (DE-627)SPR037977466 (SPR)s40899-016-0066-3-e DE-627 ger DE-627 rakwb eng Chagwiza, Godfrey verfasserin aut Formulation of a nonlinear water distribution problem with water rationing: an optimisation approach 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer International Publishing Switzerland 2016 Abstract A mixed-integer nonlinear programming water distribution problem that incorporates water rationing is presented. The city of Bulawayo water distribution problem is implemented and solved using max–min ant system, genetic, tabu search, and simulated annealing algorithms with 100 runs performed for each algorithm. The results show that the city of Bulawayo can save $3158 a day. The max–min ant system produced the best optimal costs compared with the other algorithms. The least run time is obtained by implementing the tabu search algorithm. Water lost through hoarding during water-rationing periods contributes significantly to the total operational costs. Statistical analysis of the results obtained by different algorithms shows that the optimal costs obtained by tabu search, and simulated annealing algorithms are insignificantly different. Future research may be directed toward incorporating priority among water users and formulating a hybrid algorithm that uses both the max–min ant system and tabu search algorithms to solve such problems. Mixed-integer nonlinear programming (dpeaa)DE-He213 Water rationing (dpeaa)DE-He213 Optimisation (dpeaa)DE-He213 Enthalten in Sustainable Water Resources Management Cham : Springer International Publishers, 2015 2(2016), 4 vom: 13. Sept., Seite 379-385 (DE-627)827029845 (DE-600)2823488-1 2363-5045 nnns volume:2 year:2016 number:4 day:13 month:09 pages:379-385 https://dx.doi.org/10.1007/s40899-016-0066-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 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_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 AR 2 2016 4 13 09 379-385 |
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10.1007/s40899-016-0066-3 doi (DE-627)SPR037977466 (SPR)s40899-016-0066-3-e DE-627 ger DE-627 rakwb eng Chagwiza, Godfrey verfasserin aut Formulation of a nonlinear water distribution problem with water rationing: an optimisation approach 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer International Publishing Switzerland 2016 Abstract A mixed-integer nonlinear programming water distribution problem that incorporates water rationing is presented. The city of Bulawayo water distribution problem is implemented and solved using max–min ant system, genetic, tabu search, and simulated annealing algorithms with 100 runs performed for each algorithm. The results show that the city of Bulawayo can save $3158 a day. The max–min ant system produced the best optimal costs compared with the other algorithms. The least run time is obtained by implementing the tabu search algorithm. Water lost through hoarding during water-rationing periods contributes significantly to the total operational costs. Statistical analysis of the results obtained by different algorithms shows that the optimal costs obtained by tabu search, and simulated annealing algorithms are insignificantly different. Future research may be directed toward incorporating priority among water users and formulating a hybrid algorithm that uses both the max–min ant system and tabu search algorithms to solve such problems. Mixed-integer nonlinear programming (dpeaa)DE-He213 Water rationing (dpeaa)DE-He213 Optimisation (dpeaa)DE-He213 Enthalten in Sustainable Water Resources Management Cham : Springer International Publishers, 2015 2(2016), 4 vom: 13. Sept., Seite 379-385 (DE-627)827029845 (DE-600)2823488-1 2363-5045 nnns volume:2 year:2016 number:4 day:13 month:09 pages:379-385 https://dx.doi.org/10.1007/s40899-016-0066-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 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_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 AR 2 2016 4 13 09 379-385 |
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Formulation of a nonlinear water distribution problem with water rationing: an optimisation approach Mixed-integer nonlinear programming (dpeaa)DE-He213 Water rationing (dpeaa)DE-He213 Optimisation (dpeaa)DE-He213 |
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formulation of a nonlinear water distribution problem with water rationing: an optimisation approach |
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Formulation of a nonlinear water distribution problem with water rationing: an optimisation approach |
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Abstract A mixed-integer nonlinear programming water distribution problem that incorporates water rationing is presented. The city of Bulawayo water distribution problem is implemented and solved using max–min ant system, genetic, tabu search, and simulated annealing algorithms with 100 runs performed for each algorithm. The results show that the city of Bulawayo can save $3158 a day. The max–min ant system produced the best optimal costs compared with the other algorithms. The least run time is obtained by implementing the tabu search algorithm. Water lost through hoarding during water-rationing periods contributes significantly to the total operational costs. Statistical analysis of the results obtained by different algorithms shows that the optimal costs obtained by tabu search, and simulated annealing algorithms are insignificantly different. Future research may be directed toward incorporating priority among water users and formulating a hybrid algorithm that uses both the max–min ant system and tabu search algorithms to solve such problems. © Springer International Publishing Switzerland 2016 |
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Abstract A mixed-integer nonlinear programming water distribution problem that incorporates water rationing is presented. The city of Bulawayo water distribution problem is implemented and solved using max–min ant system, genetic, tabu search, and simulated annealing algorithms with 100 runs performed for each algorithm. The results show that the city of Bulawayo can save $3158 a day. The max–min ant system produced the best optimal costs compared with the other algorithms. The least run time is obtained by implementing the tabu search algorithm. Water lost through hoarding during water-rationing periods contributes significantly to the total operational costs. Statistical analysis of the results obtained by different algorithms shows that the optimal costs obtained by tabu search, and simulated annealing algorithms are insignificantly different. Future research may be directed toward incorporating priority among water users and formulating a hybrid algorithm that uses both the max–min ant system and tabu search algorithms to solve such problems. © Springer International Publishing Switzerland 2016 |
| abstract_unstemmed |
Abstract A mixed-integer nonlinear programming water distribution problem that incorporates water rationing is presented. The city of Bulawayo water distribution problem is implemented and solved using max–min ant system, genetic, tabu search, and simulated annealing algorithms with 100 runs performed for each algorithm. The results show that the city of Bulawayo can save $3158 a day. The max–min ant system produced the best optimal costs compared with the other algorithms. The least run time is obtained by implementing the tabu search algorithm. Water lost through hoarding during water-rationing periods contributes significantly to the total operational costs. Statistical analysis of the results obtained by different algorithms shows that the optimal costs obtained by tabu search, and simulated annealing algorithms are insignificantly different. Future research may be directed toward incorporating priority among water users and formulating a hybrid algorithm that uses both the max–min ant system and tabu search algorithms to solve such problems. © Springer International Publishing Switzerland 2016 |
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