Post-optimality Analysis of Production Levels in a Petrochemical Complex

Abstract This study investigates the effect of uncertainty or variation associated with some model parameters on the production rates in a petrochemical complex. A modified approach to post-optimality analysis is used to assess and evaluate such effects. Frequent and small variations in parameters,...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Al-Shammari, Abdallah [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2015

Schlagwörter:	Petrochemical industry Optimization Uncertainty Post-optimality analysis Stability analysis

Übergeordnetes Werk:	Enthalten in: The Arabian journal for science and engineering - Berlin : Springer, 2011, 41(2015), 7 vom: 23. Okt., Seite 2429-2438
Übergeordnetes Werk:	volume:41 ; year:2015 ; number:7 ; day:23 ; month:10 ; pages:2429-2438

Links:	Volltext

DOI / URN:	10.1007/s13369-015-1863-y

Katalog-ID:	SPR031899641

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allfields	10.1007/s13369-015-1863-y doi (DE-627)SPR031899641 (SPR)s13369-015-1863-y-e DE-627 ger DE-627 rakwb eng 600 500 ASE 31.00 bkl Al-Shammari, Abdallah verfasserin aut Post-optimality Analysis of Production Levels in a Petrochemical Complex 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This study investigates the effect of uncertainty or variation associated with some model parameters on the production rates in a petrochemical complex. A modified approach to post-optimality analysis is used to assess and evaluate such effects. Frequent and small variations in parameters, like product prices, or constraints, like demand and supply, can seriously affect the optimum solution. To overcome this problem, various fuzzy and stochastic programming techniques have been proposed in the literature to determine robust solutions. However, application of post-optimality analysis in the petrochemical industry has received less attention. In this paper, a modified method for post-optimality (stability) analysis is applied to a petrochemical complex model formulated as a linear programming problem. The proposed approach differs from stochastic programming in that it can provide the decision-maker with simple and useful information that enables identification of sensitive parameters and constraints that need better estimation and monitoring. The results present the stability limits within which the assigned production rates remain optimum and sensitivity information like the Lagrange multiplier is still valid. By applying these results, the profit of the petrochemical complex can be increased by between 3.4 and 6.3 % by adjusting some model constraints. Petrochemical industry (dpeaa)DE-He213 Optimization (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 Post-optimality analysis (dpeaa)DE-He213 Stability analysis (dpeaa)DE-He213 Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 41(2015), 7 vom: 23. Okt., Seite 2429-2438 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:41 year:2015 number:7 day:23 month:10 pages:2429-2438 https://dx.doi.org/10.1007/s13369-015-1863-y 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_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_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 31.00 ASE AR 41 2015 7 23 10 2429-2438
spelling	10.1007/s13369-015-1863-y doi (DE-627)SPR031899641 (SPR)s13369-015-1863-y-e DE-627 ger DE-627 rakwb eng 600 500 ASE 31.00 bkl Al-Shammari, Abdallah verfasserin aut Post-optimality Analysis of Production Levels in a Petrochemical Complex 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This study investigates the effect of uncertainty or variation associated with some model parameters on the production rates in a petrochemical complex. A modified approach to post-optimality analysis is used to assess and evaluate such effects. Frequent and small variations in parameters, like product prices, or constraints, like demand and supply, can seriously affect the optimum solution. To overcome this problem, various fuzzy and stochastic programming techniques have been proposed in the literature to determine robust solutions. However, application of post-optimality analysis in the petrochemical industry has received less attention. In this paper, a modified method for post-optimality (stability) analysis is applied to a petrochemical complex model formulated as a linear programming problem. The proposed approach differs from stochastic programming in that it can provide the decision-maker with simple and useful information that enables identification of sensitive parameters and constraints that need better estimation and monitoring. The results present the stability limits within which the assigned production rates remain optimum and sensitivity information like the Lagrange multiplier is still valid. By applying these results, the profit of the petrochemical complex can be increased by between 3.4 and 6.3 % by adjusting some model constraints. Petrochemical industry (dpeaa)DE-He213 Optimization (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 Post-optimality analysis (dpeaa)DE-He213 Stability analysis (dpeaa)DE-He213 Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 41(2015), 7 vom: 23. Okt., Seite 2429-2438 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:41 year:2015 number:7 day:23 month:10 pages:2429-2438 https://dx.doi.org/10.1007/s13369-015-1863-y 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_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_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 31.00 ASE AR 41 2015 7 23 10 2429-2438
allfields_unstemmed	10.1007/s13369-015-1863-y doi (DE-627)SPR031899641 (SPR)s13369-015-1863-y-e DE-627 ger DE-627 rakwb eng 600 500 ASE 31.00 bkl Al-Shammari, Abdallah verfasserin aut Post-optimality Analysis of Production Levels in a Petrochemical Complex 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This study investigates the effect of uncertainty or variation associated with some model parameters on the production rates in a petrochemical complex. A modified approach to post-optimality analysis is used to assess and evaluate such effects. Frequent and small variations in parameters, like product prices, or constraints, like demand and supply, can seriously affect the optimum solution. To overcome this problem, various fuzzy and stochastic programming techniques have been proposed in the literature to determine robust solutions. However, application of post-optimality analysis in the petrochemical industry has received less attention. In this paper, a modified method for post-optimality (stability) analysis is applied to a petrochemical complex model formulated as a linear programming problem. The proposed approach differs from stochastic programming in that it can provide the decision-maker with simple and useful information that enables identification of sensitive parameters and constraints that need better estimation and monitoring. The results present the stability limits within which the assigned production rates remain optimum and sensitivity information like the Lagrange multiplier is still valid. By applying these results, the profit of the petrochemical complex can be increased by between 3.4 and 6.3 % by adjusting some model constraints. Petrochemical industry (dpeaa)DE-He213 Optimization (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 Post-optimality analysis (dpeaa)DE-He213 Stability analysis (dpeaa)DE-He213 Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 41(2015), 7 vom: 23. Okt., Seite 2429-2438 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:41 year:2015 number:7 day:23 month:10 pages:2429-2438 https://dx.doi.org/10.1007/s13369-015-1863-y 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_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_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 31.00 ASE AR 41 2015 7 23 10 2429-2438
allfieldsGer	10.1007/s13369-015-1863-y doi (DE-627)SPR031899641 (SPR)s13369-015-1863-y-e DE-627 ger DE-627 rakwb eng 600 500 ASE 31.00 bkl Al-Shammari, Abdallah verfasserin aut Post-optimality Analysis of Production Levels in a Petrochemical Complex 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This study investigates the effect of uncertainty or variation associated with some model parameters on the production rates in a petrochemical complex. A modified approach to post-optimality analysis is used to assess and evaluate such effects. Frequent and small variations in parameters, like product prices, or constraints, like demand and supply, can seriously affect the optimum solution. To overcome this problem, various fuzzy and stochastic programming techniques have been proposed in the literature to determine robust solutions. However, application of post-optimality analysis in the petrochemical industry has received less attention. In this paper, a modified method for post-optimality (stability) analysis is applied to a petrochemical complex model formulated as a linear programming problem. The proposed approach differs from stochastic programming in that it can provide the decision-maker with simple and useful information that enables identification of sensitive parameters and constraints that need better estimation and monitoring. The results present the stability limits within which the assigned production rates remain optimum and sensitivity information like the Lagrange multiplier is still valid. By applying these results, the profit of the petrochemical complex can be increased by between 3.4 and 6.3 % by adjusting some model constraints. Petrochemical industry (dpeaa)DE-He213 Optimization (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 Post-optimality analysis (dpeaa)DE-He213 Stability analysis (dpeaa)DE-He213 Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 41(2015), 7 vom: 23. Okt., Seite 2429-2438 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:41 year:2015 number:7 day:23 month:10 pages:2429-2438 https://dx.doi.org/10.1007/s13369-015-1863-y 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_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_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 31.00 ASE AR 41 2015 7 23 10 2429-2438
allfieldsSound	10.1007/s13369-015-1863-y doi (DE-627)SPR031899641 (SPR)s13369-015-1863-y-e DE-627 ger DE-627 rakwb eng 600 500 ASE 31.00 bkl Al-Shammari, Abdallah verfasserin aut Post-optimality Analysis of Production Levels in a Petrochemical Complex 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This study investigates the effect of uncertainty or variation associated with some model parameters on the production rates in a petrochemical complex. A modified approach to post-optimality analysis is used to assess and evaluate such effects. Frequent and small variations in parameters, like product prices, or constraints, like demand and supply, can seriously affect the optimum solution. To overcome this problem, various fuzzy and stochastic programming techniques have been proposed in the literature to determine robust solutions. However, application of post-optimality analysis in the petrochemical industry has received less attention. In this paper, a modified method for post-optimality (stability) analysis is applied to a petrochemical complex model formulated as a linear programming problem. The proposed approach differs from stochastic programming in that it can provide the decision-maker with simple and useful information that enables identification of sensitive parameters and constraints that need better estimation and monitoring. The results present the stability limits within which the assigned production rates remain optimum and sensitivity information like the Lagrange multiplier is still valid. By applying these results, the profit of the petrochemical complex can be increased by between 3.4 and 6.3 % by adjusting some model constraints. Petrochemical industry (dpeaa)DE-He213 Optimization (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 Post-optimality analysis (dpeaa)DE-He213 Stability analysis (dpeaa)DE-He213 Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 41(2015), 7 vom: 23. Okt., Seite 2429-2438 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:41 year:2015 number:7 day:23 month:10 pages:2429-2438 https://dx.doi.org/10.1007/s13369-015-1863-y 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_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_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 31.00 ASE AR 41 2015 7 23 10 2429-2438
language	English
source	Enthalten in The Arabian journal for science and engineering 41(2015), 7 vom: 23. Okt., Seite 2429-2438 volume:41 year:2015 number:7 day:23 month:10 pages:2429-2438
sourceStr	Enthalten in The Arabian journal for science and engineering 41(2015), 7 vom: 23. Okt., Seite 2429-2438 volume:41 year:2015 number:7 day:23 month:10 pages:2429-2438
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Petrochemical industry Optimization Uncertainty Post-optimality analysis Stability analysis
dewey-raw	600
isfreeaccess_bool	false
container_title	The Arabian journal for science and engineering
authorswithroles_txt_mv	Al-Shammari, Abdallah @@aut@@
publishDateDaySort_date	2015-10-23T00:00:00Z
hierarchy_top_id	588780731
dewey-sort	3600
id	SPR031899641
language_de	englisch
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A modified approach to post-optimality analysis is used to assess and evaluate such effects. Frequent and small variations in parameters, like product prices, or constraints, like demand and supply, can seriously affect the optimum solution. To overcome this problem, various fuzzy and stochastic programming techniques have been proposed in the literature to determine robust solutions. However, application of post-optimality analysis in the petrochemical industry has received less attention. In this paper, a modified method for post-optimality (stability) analysis is applied to a petrochemical complex model formulated as a linear programming problem. The proposed approach differs from stochastic programming in that it can provide the decision-maker with simple and useful information that enables identification of sensitive parameters and constraints that need better estimation and monitoring. 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author	Al-Shammari, Abdallah
spellingShingle	Al-Shammari, Abdallah ddc 600 bkl 31.00 misc Petrochemical industry misc Optimization misc Uncertainty misc Post-optimality analysis misc Stability analysis Post-optimality Analysis of Production Levels in a Petrochemical Complex
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title	Post-optimality Analysis of Production Levels in a Petrochemical Complex
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title_full	Post-optimality Analysis of Production Levels in a Petrochemical Complex
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title_sort	post-optimality analysis of production levels in a petrochemical complex
title_auth	Post-optimality Analysis of Production Levels in a Petrochemical Complex
abstract	Abstract This study investigates the effect of uncertainty or variation associated with some model parameters on the production rates in a petrochemical complex. A modified approach to post-optimality analysis is used to assess and evaluate such effects. Frequent and small variations in parameters, like product prices, or constraints, like demand and supply, can seriously affect the optimum solution. To overcome this problem, various fuzzy and stochastic programming techniques have been proposed in the literature to determine robust solutions. However, application of post-optimality analysis in the petrochemical industry has received less attention. In this paper, a modified method for post-optimality (stability) analysis is applied to a petrochemical complex model formulated as a linear programming problem. The proposed approach differs from stochastic programming in that it can provide the decision-maker with simple and useful information that enables identification of sensitive parameters and constraints that need better estimation and monitoring. The results present the stability limits within which the assigned production rates remain optimum and sensitivity information like the Lagrange multiplier is still valid. By applying these results, the profit of the petrochemical complex can be increased by between 3.4 and 6.3 % by adjusting some model constraints.
abstractGer	Abstract This study investigates the effect of uncertainty or variation associated with some model parameters on the production rates in a petrochemical complex. A modified approach to post-optimality analysis is used to assess and evaluate such effects. Frequent and small variations in parameters, like product prices, or constraints, like demand and supply, can seriously affect the optimum solution. To overcome this problem, various fuzzy and stochastic programming techniques have been proposed in the literature to determine robust solutions. However, application of post-optimality analysis in the petrochemical industry has received less attention. In this paper, a modified method for post-optimality (stability) analysis is applied to a petrochemical complex model formulated as a linear programming problem. The proposed approach differs from stochastic programming in that it can provide the decision-maker with simple and useful information that enables identification of sensitive parameters and constraints that need better estimation and monitoring. The results present the stability limits within which the assigned production rates remain optimum and sensitivity information like the Lagrange multiplier is still valid. By applying these results, the profit of the petrochemical complex can be increased by between 3.4 and 6.3 % by adjusting some model constraints.
abstract_unstemmed	Abstract This study investigates the effect of uncertainty or variation associated with some model parameters on the production rates in a petrochemical complex. A modified approach to post-optimality analysis is used to assess and evaluate such effects. Frequent and small variations in parameters, like product prices, or constraints, like demand and supply, can seriously affect the optimum solution. To overcome this problem, various fuzzy and stochastic programming techniques have been proposed in the literature to determine robust solutions. However, application of post-optimality analysis in the petrochemical industry has received less attention. In this paper, a modified method for post-optimality (stability) analysis is applied to a petrochemical complex model formulated as a linear programming problem. The proposed approach differs from stochastic programming in that it can provide the decision-maker with simple and useful information that enables identification of sensitive parameters and constraints that need better estimation and monitoring. The results present the stability limits within which the assigned production rates remain optimum and sensitivity information like the Lagrange multiplier is still valid. By applying these results, the profit of the petrochemical complex can be increased by between 3.4 and 6.3 % by adjusting some model constraints.
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title_short	Post-optimality Analysis of Production Levels in a Petrochemical Complex
url	https://dx.doi.org/10.1007/s13369-015-1863-y
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doi_str	10.1007/s13369-015-1863-y
up_date	2024-07-04T01:44:41.750Z
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Post-optimality Analysis of Production Levels in a Petrochemical Complex

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