ex8_2_4.gms

	[ GLOBAL World Home \| Board \| Solvers \| GLOBALLib \| Links \| GamsWorld group \| Search \| Contact ]

ex8_2_4.gms:

References:

Floudas, C A, Pardalos, P M, Adjiman, C S, Esposito, W R, Gumus, Z H, Harding, S T, Klepeis, J L, Meyer, C A, and Schweiger, C A, Handbook of Test Problems in Local and Global Optimization. Kluwer Academic Publishers, 1999.
Harding, S T, and Floudas, C A, Global Optimization in Multiproduct and Multipurpose Batch Design Under Uncertainty. I and EC Res. 36 (1997), 1644-1664.
Original source: Global Model of Chapter 8 ex8.2.4.gms from Floudas e.a. Test Problems

Point:

* NLP written by GAMS Convert at 07/19/01 13:39:59 * * Equation counts * Total E G L N X * 82 1 6 75 0 0 * * Variable counts * x b i s1s s2s sc si * Total cont binary integer sos1 sos2 scont sint * 56 56 0 0 0 0 0 0 * FX 0 0 0 0 0 0 0 0 * * Nonzero counts * Total const NL DLL * 366 63 303 0 * * Solve m using NLP minimizing objvar; Variables objvar,x2,x3,x4,x5,x6,x7,x8,x9,x10,x11,x12,x13,x14,x15,x16,x17,x18 ,x19,x20,x21,x22,x23,x24,x25,x26,x27,x28,x29,x30,x31,x32,x33,x34,x35 ,x36,x37,x38,x39,x40,x41,x42,x43,x44,x45,x46,x47,x48,x49,x50,x51,x52 ,x53,x54,x55,x56; Equations e1,e2,e3,e4,e5,e6,e7,e8,e9,e10,e11,e12,e13,e14,e15,e16,e17,e18,e19 ,e20,e21,e22,e23,e24,e25,e26,e27,e28,e29,e30,e31,e32,e33,e34,e35,e36 ,e37,e38,e39,e40,e41,e42,e43,e44,e45,e46,e47,e48,e49,e50,e51,e52,e53 ,e54,e55,e56,e57,e58,e59,e60,e61,e62,e63,e64,e65,e66,e67,e68,e69,e70 ,e71,e72,e73,e74,e75,e76,e77,e78,e79,e80,e81,e82; e1.. - 0.3*(10*exp(0.6*x2) + 10*exp(0.6*x3) + 10*exp(0.6*x4)) + objvar + 1.54711033913716E-6*x5 + 0.000219040316990534*x6 + 0.00264813118267794*x7 + 0.000219040316990534*x8 + 1.54711033913716E-6*x9 + 0.000219040316990533*x10 + 0.0310117896917886*x11 + 0.374923157717238*x12 + 0.0310117896917886*x13 + 0.000219040316990532*x14 + 0.00264813118267793*x15 + 0.374923157717237*x16 + 4.5327075795914*x17 + 0.374923157717237*x18 + 0.00264813118267791*x19 + 0.000219040316990532*x20 + 0.0310117896917884*x21 + 0.374923157717236*x22 + 0.0310117896917884*x23 + 0.000219040316990531*x24 + 1.54711033913713E-6*x25 + 0.000219040316990529*x26 + 0.00264813118267789*x27 + 0.000219040316990529*x28 + 1.54711033913712E-6*x29 + 1.9690495225382E-6*x30 + 0.000278778585260679*x31 + 0.00337034877795374*x32 + 0.000278778585260679*x33 + 1.9690495225382E-6*x34 + 0.000278778585260678*x35 + 0.0394695505168218*x36 + 0.477174928003758*x37 + 0.0394695505168218*x38 + 0.000278778585260677*x39 + 0.00337034877795373*x40 + 0.477174928003756*x41 + 5.7689005558436*x42 + 0.477174928003756*x43 + 0.00337034877795371*x44 + 0.000278778585260677*x45 + 0.0394695505168216*x46 + 0.477174928003755*x47 + 0.0394695505168216*x48 + 0.000278778585260676*x49 + 1.96904952253816E-6*x50 + 0.000278778585260674*x51 + 0.00337034877795368*x52 + 0.000278778585260674*x53 + 1.96904952253816E-6*x54 =E= 0; e2.. x2 - x55 =G= 0.693147180559945; e3.. x3 - x55 =G= 1.09861228866811; e4.. x4 - x55 =G= 1.38629436111989; e5.. x2 - x56 =G= 1.38629436111989; e6.. x3 - x56 =G= 1.79175946922805; e7.. x4 - x56 =G= 1.09861228866811; e8.. x5*exp(2.07944154167984 - x55) + x30*exp(2.77258872223978 - x56) =L= 8; e9.. x6*exp(2.07944154167984 - x55) + x31*exp(2.77258872223978 - x56) =L= 8; e10.. x7*exp(2.07944154167984 - x55) + x32*exp(2.77258872223978 - x56) =L= 8; e11.. x8*exp(2.07944154167984 - x55) + x33*exp(2.77258872223978 - x56) =L= 8; e12.. x9*exp(2.07944154167984 - x55) + x34*exp(2.77258872223978 - x56) =L= 8; e13.. x10*exp(2.07944154167984 - x55) + x35*exp(2.77258872223978 - x56) =L= 8; e14.. x11*exp(2.07944154167984 - x55) + x36*exp(2.77258872223978 - x56) =L= 8; e15.. x12*exp(2.07944154167984 - x55) + x37*exp(2.77258872223978 - x56) =L= 8; e16.. x13*exp(2.07944154167984 - x55) + x38*exp(2.77258872223978 - x56) =L= 8; e17.. x14*exp(2.07944154167984 - x55) + x39*exp(2.77258872223978 - x56) =L= 8; e18.. x15*exp(2.07944154167984 - x55) + x40*exp(2.77258872223978 - x56) =L= 8; e19.. x16*exp(2.07944154167984 - x55) + x41*exp(2.77258872223978 - x56) =L= 8; e20.. x17*exp(2.07944154167984 - x55) + x42*exp(2.77258872223978 - x56) =L= 8; e21.. x18*exp(2.07944154167984 - x55) + x43*exp(2.77258872223978 - x56) =L= 8; e22.. x19*exp(2.07944154167984 - x55) + x44*exp(2.77258872223978 - x56) =L= 8; e23.. x20*exp(2.07944154167984 - x55) + x45*exp(2.77258872223978 - x56) =L= 8; e24.. x21*exp(2.07944154167984 - x55) + x46*exp(2.77258872223978 - x56) =L= 8; e25.. x22*exp(2.07944154167984 - x55) + x47*exp(2.77258872223978 - x56) =L= 8; e26.. x23*exp(2.07944154167984 - x55) + x48*exp(2.77258872223978 - x56) =L= 8; e27.. x24*exp(2.07944154167984 - x55) + x49*exp(2.77258872223978 - x56) =L= 8; e28.. x25*exp(2.07944154167984 - x55) + x50*exp(2.77258872223978 - x56) =L= 8; e29.. x26*exp(2.07944154167984 - x55) + x51*exp(2.77258872223978 - x56) =L= 8; e30.. x27*exp(2.07944154167984 - x55) + x52*exp(2.77258872223978 - x56) =L= 8; e31.. x28*exp(2.07944154167984 - x55) + x53*exp(2.77258872223978 - x56) =L= 8; e32.. x29*exp(2.07944154167984 - x55) + x54*exp(2.77258872223978 - x56) =L= 8; e33.. x5*exp(2.99573227355399 - x55) + x30*exp(1.38629436111989 - x56) =L= 8; e34.. x6*exp(2.99573227355399 - x55) + x31*exp(1.38629436111989 - x56) =L= 8; e35.. x7*exp(2.99573227355399 - x55) + x32*exp(1.38629436111989 - x56) =L= 8; e36.. x8*exp(2.99573227355399 - x55) + x33*exp(1.38629436111989 - x56) =L= 8; e37.. x9*exp(2.99573227355399 - x55) + x34*exp(1.38629436111989 - x56) =L= 8; e38.. x10*exp(2.99573227355399 - x55) + x35*exp(1.38629436111989 - x56) =L= 8; e39.. x11*exp(2.99573227355399 - x55) + x36*exp(1.38629436111989 - x56) =L= 8; e40.. x12*exp(2.99573227355399 - x55) + x37*exp(1.38629436111989 - x56) =L= 8; e41.. x13*exp(2.99573227355399 - x55) + x38*exp(1.38629436111989 - x56) =L= 8; e42.. x14*exp(2.99573227355399 - x55) + x39*exp(1.38629436111989 - x56) =L= 8; e43.. x15*exp(2.99573227355399 - x55) + x40*exp(1.38629436111989 - x56) =L= 8; e44.. x16*exp(2.99573227355399 - x55) + x41*exp(1.38629436111989 - x56) =L= 8; e45.. x17*exp(2.99573227355399 - x55) + x42*exp(1.38629436111989 - x56) =L= 8; e46.. x18*exp(2.99573227355399 - x55) + x43*exp(1.38629436111989 - x56) =L= 8; e47.. x19*exp(2.99573227355399 - x55) + x44*exp(1.38629436111989 - x56) =L= 8; e48.. x20*exp(2.99573227355399 - x55) + x45*exp(1.38629436111989 - x56) =L= 8; e49.. x21*exp(2.99573227355399 - x55) + x46*exp(1.38629436111989 - x56) =L= 8; e50.. x22*exp(2.99573227355399 - x55) + x47*exp(1.38629436111989 - x56) =L= 8; e51.. x23*exp(2.99573227355399 - x55) + x48*exp(1.38629436111989 - x56) =L= 8; e52.. x24*exp(2.99573227355399 - x55) + x49*exp(1.38629436111989 - x56) =L= 8; e53.. x25*exp(2.99573227355399 - x55) + x50*exp(1.38629436111989 - x56) =L= 8; e54.. x26*exp(2.99573227355399 - x55) + x51*exp(1.38629436111989 - x56) =L= 8; e55.. x27*exp(2.99573227355399 - x55) + x52*exp(1.38629436111989 - x56) =L= 8; e56.. x28*exp(2.99573227355399 - x55) + x53*exp(1.38629436111989 - x56) =L= 8; e57.. x29*exp(2.99573227355399 - x55) + x54*exp(1.38629436111989 - x56) =L= 8; e58.. x5*exp(2.07944154167984 - x55) + x30*exp(1.38629436111989 - x56) =L= 8; e59.. x6*exp(2.07944154167984 - x55) + x31*exp(1.38629436111989 - x56) =L= 8; e60.. x7*exp(2.07944154167984 - x55) + x32*exp(1.38629436111989 - x56) =L= 8; e61.. x8*exp(2.07944154167984 - x55) + x33*exp(1.38629436111989 - x56) =L= 8; e62.. x9*exp(2.07944154167984 - x55) + x34*exp(1.38629436111989 - x56) =L= 8; e63.. x10*exp(2.07944154167984 - x55) + x35*exp(1.38629436111989 - x56) =L= 8; e64.. x11*exp(2.07944154167984 - x55) + x36*exp(1.38629436111989 - x56) =L= 8; e65.. x12*exp(2.07944154167984 - x55) + x37*exp(1.38629436111989 - x56) =L= 8; e66.. x13*exp(2.07944154167984 - x55) + x38*exp(1.38629436111989 - x56) =L= 8; e67.. x14*exp(2.07944154167984 - x55) + x39*exp(1.38629436111989 - x56) =L= 8; e68.. x15*exp(2.07944154167984 - x55) + x40*exp(1.38629436111989 - x56) =L= 8; e69.. x16*exp(2.07944154167984 - x55) + x41*exp(1.38629436111989 - x56) =L= 8; e70.. x17*exp(2.07944154167984 - x55) + x42*exp(1.38629436111989 - x56) =L= 8; e71.. x18*exp(2.07944154167984 - x55) + x43*exp(1.38629436111989 - x56) =L= 8; e72.. x19*exp(2.07944154167984 - x55) + x44*exp(1.38629436111989 - x56) =L= 8; e73.. x20*exp(2.07944154167984 - x55) + x45*exp(1.38629436111989 - x56) =L= 8; e74.. x21*exp(2.07944154167984 - x55) + x46*exp(1.38629436111989 - x56) =L= 8; e75.. x22*exp(2.07944154167984 - x55) + x47*exp(1.38629436111989 - x56) =L= 8; e76.. x23*exp(2.07944154167984 - x55) + x48*exp(1.38629436111989 - x56) =L= 8; e77.. x24*exp(2.07944154167984 - x55) + x49*exp(1.38629436111989 - x56) =L= 8; e78.. x25*exp(2.07944154167984 - x55) + x50*exp(1.38629436111989 - x56) =L= 8; e79.. x26*exp(2.07944154167984 - x55) + x51*exp(1.38629436111989 - x56) =L= 8; e80.. x27*exp(2.07944154167984 - x55) + x52*exp(1.38629436111989 - x56) =L= 8; e81.. x28*exp(2.07944154167984 - x55) + x53*exp(1.38629436111989 - x56) =L= 8; e82.. x29*exp(2.07944154167984 - x55) + x54*exp(1.38629436111989 - x56) =L= 8; * set non default bounds * set non default levels * set non default marginals Model m / all /; m.limrow=0; m.limcol=0; $if NOT '%gams.u1%' == '' $include '%gams.u1%' Solve m using NLP minimizing objvar;