Lampiran 1 Digram Pencar dan Perhitungan Parameter Peramalan Setiap Produk

  

DAFTAR PUSTAKA (Lanjutan)

Siswanto, 2006. Operations Research, Jilid I. Jakarta: Erlangga

  LAMPIRAN

  Lampiran 1 Digram Pencar dan Perhitungan Parameter Peramalan Setiap Produk

Penjualan PPNW

  60000000 55000000 50000000 45000000

  JUMLAH 40000000 35000000 30000000

  Nop-13 Jan-14 Mar-14 Apr-14 Jun-14 Jul-14 Sep-14 Nop-14 Des-14 Gambar Diagram Pencar Penjualan Jenis Produk PPNW

Penjualan Produk PPHW

  20000000 18000000 16000000 14000000 12000000 10000000

  JUMLAH 8000000 6000000 4000000 2000000

  2

  4

  6

  

8

  10

  12

  14 Gambar Diagram Pencar Penjualan Jenis Produk PPHW

• 1,000 0,000 0,000 1,000 0,000 -57810060,00
• 1,000 0,000 0,000 1,000 0,000 -48467260,00
• 1,000 0,000 0,000 1,000 0,000 -52753800,00

  2πx n ) cos (

  2πx n )

  629.104.740 = 12 a + (0) + c (0) a =

  629104740

  12 = 52.425.395 ∑ y sin(

  2πx n ) = a ∑ sin(

  2πx n

) + b ∑ sin

  2 (

  2πx n ) + c ∑ sin(

  2πx n )

  78 629104740 0,000 0,000 0,000 6,000 6,000 - 10796480,000 1802180,000 ∑ y = n a + b ∑ sin(

  b = −10796480

  6 = -1.799.403

  ∑ y cos ( 2πx n

  ) = a ∑ cos ( 2πx n

  ) + c ∑ cos

  2 (

  2πx n ) + b ∑ sin(

  2πx n ) cos (

  2πx n )

  

2πx

n

) + c ∑ cos (

  0,000 1,000 0,000 0,000 1,000 0,00 57600200

  1802180 = a (0) + c (6) + b(0)

  1,000 0,000 0,000 1,000 0,000 51346820,00

  a. Metode Regresi dengan Kecenderungan Siklis Tabel Perhitungan Parameter Peramalan untuk Metode Regresi dengan Kecenderungan Siklis

  X Y Sin(2πx/n) Cos(2πx/n) Sin*Cos Sin ² Cos ² Y* Sin(2πx/n) Y* Cos(2πx/n) 1 53735820

  1,000 0,000 0,000 1,000 0,000 53735820,00

  2 53276600

  0,000 -1,000 0,000 0,000 1,000 0,00 -53276600

  3 57810060

  4 47088800

  0,000 1,000 0,000 0,000 1,000 0,00 47088800

  5 51346820

  6 52188600

  12 57600200

  0,000 -1,000 0,000 0,000 1,000 0,00 -52188600

  7 48467260

  8 57131580

  0,000 1,000 0,000 0,000 1,000 0,00 57131580

  9 43152000

  1,000 0,000 0,000 1,000 0,000 43152000,00

  10 54553200

  0,000 -1,000 0,000 0,000 1,000 0,00 -54553200

  11 52753800

• 10796480 = a (0) + b (6) + c (0)

  1802180

  c =

  6 = 300.363,3 Dengan metode siklis diperoleh fungsi peramalan:

  2 π x 2 π x + Yt = 52.425.395 -1.799.403 sin 300.363,3 cos n n

  b. Metode Dekomposisi Tabel Rekapitulasi Perhitungan Nilai Rata-rata Per 6 Periode Permintaan Nilai Rata-rata Per 4 No Periode (unit) Periode

  1 Januari 53.735.820

  2 Februari 53.276.600

  3 Maret 57.810.060 52.342.176

  4 April 47.088.800

  5 Mei 51.346.820

  6 Juni 52.188.600

  7 Juli 48.467.260

  8 Agustus 57.131.580

  9 September 43.152.000 52.276.340

  10 Oktober 54.553.200

  11 Nopember 52.753.800

  12 Desember 57.600.200

  

Tabel Perhitungan Nilai Indeks Musim

Rata-rata Periode

  Faktor Indeks Periode Permintaan Per 4 Pembagian Musim Musim Periode

  1 Januari 26.867.910 1,03 0,98

  2 Februari 20.491.000 1,02 1,06

  3 Maret 24.775.740 1,10 0,96 52.342.176

  4 April 34.246.400 0,90 0,97

  5 Mei 28.329.280 0,98 1,00

  6 Juni 21.745.250 1,00 1,05

  7 Juli 13.289.410 0,93 0,98

  8 Agustus 21.640.750 1,09 1,06

  Tabel Perhitungan Nilai Indeks Musim (Lanjutan) Periode Pembagian Periode Permintaan Rata-rata Per 4 Periode Faktor Musim Indeks Musim

  12 ) (

  12 = 52.221.897

  629.104.740−(31.307,34)(78)

  − =

       629.104.74 760 4.093.657. x x n y x xy n b a =

  

   

   

    

  2

  2

  2

  12

  ( 78 )

  31 ) 650 78 (

  9 September 25.891.200 52.276.340

  . 307 343 ,

       

  78 629.104.740 650 4.093.657.760 Parameter peramalan : Yt = a + bx

  11 52.753.800 121 580.291.800 12 57.600.200 144 691.202.400

  16 188.355.200 5 51.346.820 25 256.734.100 6 52.188.600 36 313.131.600 7 48.467.260 49 339.270.820 8 57.131.580 64 457.052.640 9 43.152.000 81 388.368.000 10 54.553.200 100 545.532.000

  1 53.735.820 1 53.735.820 2 53.276.600 4 106.553.200 3 57.810.060 9 173.430.180 4 47.088.800

  XY

  X Y X^2

  Fungsi peramalan: Yt = a + bx

Tabel Perhitungan Parameter Peramalan Metode Linier

  12 Desember 21.394.360 1,10 1,05 Garis trend dapat dicari dengan menggunakan persamaan

  11 Nopember 17.584.600 1,01 1,00

  10 Oktober 27.276.600 1,04 0,97

  0,83 0,96

  Persamaan peramalan: Yt = 52.221.897 + 31.307,34x

  

Tabel Perhitungan Nilai Persamaan Garis Trend

Periode

  

X

Nilai Persamaan Garis Trend Januari 13 52.628.893 Februari

  14 52.660.200 Maret 15 52.691.507 April 16 52.722.815 Mei 17 52.754.122 Juni 18 52.785.429 Juli 19 52.816.737 Agustus 20 52.848.044 September 21 52.879.351 Oktober 22 52.910.659 November 23 52.941.966 Desember 24 52.973.273 Fungsi peramalan akhir adalah :

  Yt = 52.221.897 +( 31.307,34)(X) (Indeks Musim) Tabel Perhitungan SEE untuk Metode Regresi dengan Kecenderungan

Siklis

  X Y Y' Y-Y' (Y-Y')^2

  1 53.735.820 50.625.982 3109838 9671094438737 2 53.276.600 52.125.032 1151568 1326109618659 3 57.810.060 54.224.808 3585252 12854029537238 4 47.088.800 52.725.758 -5636958 31775299214156 5 51.346.820 50.625.982 720838 519607897997 6 52.188.600 52.125.032 63568 4040932579 7 48.467.260 54.224.808 -5757548 33149362772286 8 57.131.580 52.725.758 4405822 19411264587842 9 43.152.000 50.625.982 -7473982 55860402003496 10 54.553.200 52.125.032 2428168 5896001438815

  11 52.753.800 54.224.808 -1471008 2163865506929 12 57.600.200 52.725.758 4874442 23760181594232

  78 629.104.740 629.104.740 196391259542967 SEE = √

  ∑ (y - y

  333.688.124.125.563.000 12-2

  = 0,00049 F tabel = 0,05 (9,10) = 3,02 Maka: F hitung

  3 182.671.32 4.045.4832 SEEDekompo sisi SEEsiklis _hitung F

  

    

     

    

    

  2

  

2

   = 182.671.323,45 Pengujian Hipotesa Ho = Metode siklis lebih baik dari metode dekomposisi Hi = Metode siklis tidak lebih baik dari metode dekomposisi α = 0,05 Uji statistik :

  = √

  ' ) n x = 1

  2 n - f

  ) n x = 1

  (y - y '

  √ ∑

  78 629.104.740 635.024.764 -589.532.640 333.688.124.125.563.000 SEE =

  11 52.753.800 52.680.355 -2.839.178 8.060.930.329.413 12 57.600.200 55.592.978 -577.424.564 333.419.127.019.932.000

  1 53.735.820 51.412.147 -1.839.877 3.385.145.773.359 2 53.276.600 55.575.697 2.431.306 5.911.249.071.652 3 57.810.060 50.845.294 6.584.882 43.360.669.549.263 4 47.088.800 51.225.178 -5.404.639 29.210.124.136.252 5 51.346.820 52.493.439 -4.049.024 16.394.596.872.306 6 52.188.600 55.395.844 592.951 351.591.239.958 7 48.467.260 51.595.649 -7.306.680 53.387.579.640.330 8 57.131.580 55.773.940 6.105.024 37.271.314.334.416 9 43.152.000 51.026.556 -8.255.686 68.156.356.144.060 10 54.553.200 51.407.686 1.872.845 3.507.548.540.209

  X Y Y' Y-Y' (Y-Y')^2

  12-3 = 4.045.483

Tabel Perhitungan SEE untuk Metode Dekomposisi

   = √

  2 n - f

  ≤ F tabel maka Ho diterima Kesimpulan : Metode yang digunakan untuk meramalkan produk tipe PPNW adalah metode regresi dengan kecenderungan siklis dengan fungsi sebagai berikut.

  2 π x 2 π x + Yt = 52.425.395 -1.799.403 sin 300.363,3 cos n n

Tabel Perhitungan Hasil Verifikasi

x Y Y' Y-Y' MR

  1 53.735.820 50.625.982 3109838 2 53.276.600 52.125.032 1151568 4261406,66 3 57.810.060 54.224.808 3585252 4736820 4 47.088.800 52.725.758 -5636958 9222210 5 51.346.820 50.625.982 720838 6357796,66 6 52.188.600 52.125.032 63568 784406,66 7 48.467.260 54.224.808 -5757548 5821116,66 8 57.131.580 52.725.758 4405822 10163370 9 43.152.000 50.625.982 -7473982 11879803,34 10 54.553.200 52.125.032 2428168 9902150

  11 52.753.800 54.224.808 -1471008 3899176,66 12 57.600.200 52.725.758 4874442 6345450

  73373706,64 MR 73373706,6

  4 

  MR =  = 6670336,96 

  12

  1

  1 n 

  BKA = 2,66 x MR = 2,66 x 6670336,96 = 17743096,33 2/3 BKA = 2/3 x 17743096,33 = 11828730,89 1/3 BKA = 1/3 x 17743096,33 = 5914365,44 BKB = - 2,66 x MR = -2,66 x 6670336,96 = -17743096,33 2/3 BKB = 2/3 x -17743096,33 = -11828730,89 1/3 BKB = 1/3 x -17743096,33 = -5914365,44

  12 Y-Y' MR BKA 2/3 BKA 1/3 BKA BKB 2/3 BKB 1/3 BKB

  5

  11

  10

  9

  8

  7

  

6

  4

  3

  2

  1

• 5000000 5000000 10000000 15000000 20000000
>20000000
• 15000000
• 1000
• 17584600,00 12 3291440 0,000 1,000 0,000 0,000 1,000 0,00 3291440

  78 117886050 0,000 0,000 0,000 6,000 6,000 -8107460,000 - 21032250,000

  1 8955970 1,000 0,000 0,000 1,000 0,000 8955970,00 2 8196400 0,000 -1,000 0,000 0,000 1,000 0,00 -8196400 3 9176200 -1,000 0,000 0,000 1,000 0,000 -9176200,00 4 4280800 0,000 1,000 0,000 0,000 1,000 0,00 4280800 5 8852900 1,000 0,000 0,000 1,000 0,000 8852900,00 6 13047150 0,000 -1,000 0,000 0,000 1,000 0,00 -13047150 7 16416330 -1,000 0,000 0,000 1,000 0,000

  X Y Sin(2πx/n) Cos(2πx/n) Sin*Cos Sin ² Cos ² Y* Sin(2πx/n) Y* Cos(2πx/n)

  

Gambar Moving Range Chart

PERAMALAN PRODUK PPHW a. Metode Regresi dengan Kecenderungan Siklis Tabel Perhitungan Parameter Peramalan untuk Metode Regresi dengan Kecenderungan Siklis

• 16416330,00 8 1731260 0,000 1,000 0,000 0,000 1,000 0,00 1731260 9 17260800 1,000 0,000 0,000 1,000 0,000 17260800,00 10 9092200 0,000 -1,000 0,000 0,000 1,000 0,00 -9092200 11 17584600 -1,000 0,000 0,000 1,000 0,000
• 8107460 = a (0) + b (6) + c (0)

  4 April 4.280.800

  ) = a ∑ cos ( 2πx n

  3 Maret 9.176.200

  2 Februari 8.196.400

  1 Januari 8.955.970 7.652.343

  b. Metode Dekomposisi Tabel Rekapitulasi Perhitungan Nilai Rata-rata Per 4 Periode No Periode Permintaan (unit) Nilai Rata-rata Per 4 Periode

  2 π x n – 3.505.375 cos 2 π x n

  Yt = 9.823.838

  6 = -3505375 Dengan metode siklis diperoleh fungsi peramalan:

  c = −

  2πx n )

  2πx n ) cos (

  2πx n ) + b ∑ sin(

  2 (

  

) + c ∑ cos

  ∑ y cos ( 2πx n

  ∑ y = n a + b ∑ sin( 2πx n

  6 = -1351243

  b = − 8107460

  2πx n )

  2πx n ) cos (

  2πx n ) + c ∑ sin(

  2 (

  2πx n ) + b ∑ sin

  2πx n ) = a ∑ sin(

  12 = 9823838 ∑ y sin(

  a = 117886050

  117886050 = 12 a + (0) + c (0)

  )

  ) + c ∑ cos ( 2πx n

• 21032250 = a (0) + c (6) + b(0)
• – 1.351.243 sin

  Tabel Rekapitulasi Perhitungan Nilai Rata-rata Per 4 Periode (Lanjutan) No Periode Permintaan (unit) Nilai Rata-rata Per 4 Periode

  4 April 4.280.800 0,56 0,34

  12 Desember 3.291.440 0,28 0,34 Garis trend dapat dicari dengan menggunakan persamaan

  11 Nopember 17.584.600 1,49 1,44

  10 Oktober 9.092.200 0,77 1,05

  1,46 1,17

  9 September 17.260.800 11.807.260

  8 Agustus 1.731.260 0,17 0,34

  7 Juli 16.416.330 1,64 1,44

  6 Juni 13.047.150 1,30 1,05

  0,88 1,17

  5 Mei 8.852.900 10.011.910

  3 Maret 9.176.200 1,20 1,44

  5 Mei 8.852.900 10.011.910

  2 Februari 8.196.400 1,07 1,05

  1,17 1,17

  1 Januari 8.955.970 7.652.343

  Tabel Perhitungan Nilai Indeks Musim Periode Pembagian Periode Permintaan Rata-rata Per 4 Periode Faktor Musim Indeks Musim

  12 Desember 3.291.440

  11 Nopember 17.584.600

  10 Oktober 9.092.200

  9 September 17.260.800 11.807.260

  8 Agustus 1.731.260

  7 Juli 16.416.330

  6 Juni 13.047.150

  Fungsi peramalan: Yt = a + bx

  

Tabel Perhitungan Parameter Peramalan Metode Linier

  2

  

X

Nilai Persamaan Garis Trend Januari 13 11.380.661 Februari

  Persamaan peramalan: Yt = 8267014,1 + 239511,29x

Tabel Perhitungan Nilai Persamaan Garis Trend

Periode

  12 = 8267014,1

  117.886.050−(23951129)(78)

  − =

       800.509.44 x x n y x xy n b a =

  

   

   

    

  2

  X Y X^2

  2

  12

  ( 78 )

  12 ) 050 . 886 . 117 (

  239511 29 , ) 650 78 (

       

  78 117.886.050 650 800.509.440 Parameter peramalan : Yt = a + bx

  11 17.584.600 121 193.430.600 12 3.291.440 144 39.497.280

  16 17.123.200 5 8.852.900 25 44.264.500 6 13.047.150 36 78.282.900 7 16.416.330 49 114.914.310 8 1.731.260 64 13.850.080 9 17.260.800 81 155.347.200 10 9.092.200 100 90.922.000

  1 8.955.970 1 8.955.970 2 8.196.400 4 16.392.800 3 9.176.200 9 27.528.600 4 4.280.800

  XY

  14 11.620.172 Maret 15 11.859.683 April 16 12.099.195 Mei 17 12.338.706 Juni 18 12.578.217 Juli 19 12.817.729 Agustus 20 13.057.240 September 21 13.296.751 Oktober 22 13.536.263

  Tabel Perhitungan Nilai Persamaan Garis Trend (Lanjutan) Nilai Persamaan Periode

  

X

Garis Trend November 23 13.775.774 Desember

  24 14.015.285 Fungsi peramalan akhir adalah : Yt = 8267014,1 + (239511,29)(X) (Indeks Musim) Tabel Perhitungan SEE untuk Metode Regresi dengan Kecenderungan

  

Siklis

  X Y Y' Y-Y' (Y-Y')^2

  1 8.955.970 8.472.594 483376 233652193028 2 8.196.400 13.329.213 -5132813 26345764160156 3 9.176.200 11.175.081 -1998881 3995524572541 4 4.280.800 6.318.463 -2037663 4152068463906 5 8.852.900 8.472.594 380306 144632524332 6 13.047.150 13.329.213 -282063 79559253906 7 16.416.330 11.175.081 5241249 27470692862026 8 1.731.260 6.318.463 -4587203 21042426776006 9 17.260.800 8.472.594 8788206 77232561710446 10 9.092.200 13.329.213 -4237013 17952274925156

  11 17.584.600 11.175.081 6409519 41081935990598 12 3.291.440 6.318.463 -3027023 9162865215506

  

78 117.886.050 117.886.050 228893958647608

  2 n '

  (y - y ) ∑ x = 1

  SEE = = = 4.367.436 √ √ n - f 12-3

  

Tabel Perhitungan SEE untuk Metode Dekomposisi

  Uji statistik :

  F = 0,92

  4.367.4362 4.207.793 SEEsiklis SEEdekompo sisi hitung

  

    

     

    

    

  2

  

2

   = 4.207.793 Pengujian Hipotesa Ho = Metode dekomposisi lebih baik dari metode regresi dengan kecenderungan siklis Hi = Metode dekomposisi tidak lebih baik dari metode regresi dengan kecenderungan siklis α = 0,05

  X Y Y' Y-Y' (Y-Y')^2

  177.055.239.127.892 12-2

  = √

  2 n - f

  ) n x = 1

  (y - y '

  √ ∑

  78 117.886.050 151.617.344 -33.731.294 177.055.239.127.892 SEE =

  11 17.584.600 19.874.402 -2.289.802 5.243.193.034.950 12 3.291.440 4.723.593 -1.432.153 2.051.061.494.808

  1 8.955.970 13.339.932 -4.383.962 19.219.121.422.242 2 8.196.400 12.179.147 -3.982.747 15.862.271.930.798 3 9.176.200 17.110.045 -7.933.845 62.945.904.039.497 4 4.280.800 4.077.810 202.990 41.204.972.642 5 8.852.900 14.462.912 -5.610.012 31.472.235.560.961 6 13.047.150 13.183.278 -136.128 18.530.718.578 7 16.416.330 18.492.224 -2.075.894 4.309.334.737.333 8 1.731.260 4.400.701 -2.669.441 7.125.917.036.373 9 17.260.800 15.585.892 1.674.908 2.805.315.725.590 10 9.092.200 14.187.408 -5.095.208 25.961.148.454.120

  F tabel = 0,05 (9,10) = 3,02 Maka: F hitung ≤ F tabel maka Ho diterima

  Kesimpulan : Metode yang digunakan untuk meramalkan produk tipe PPHW adalah metode dekomposisi dengan fungsi sebagai berikut.

  Yt = 8267014,1 + (239511,29)(X) (Indeks Musim)

Tabel Perhitungan Hasil Verifikasi

x Y Y' Y-Y' MR

  8.955.970 13.339.932 -4.383.961,84 4383961,841 8.196.400 12.179.147 -3.982.746,78 3982746,782 8366708,623 9.176.200 17.110.045 -7.933.845,48 7933845,476 11916592,26 4.280.800 4.077.810 202.990,08 202990,0802 8136835,556 8.852.900 14.462.912 -5.610.012,08 5610012,082 5813002,162

  13.047.150 13.183.278 -136.127,58 136127,582 5746139,664 16.416.330 18.492.224 -2.075.893,72 2075893,72 2212021,302

  1.731.260 4.400.701 -2.669.441,33 2669441,334 4745335,054 17.260.800 15.585.892 1.674.907,68 1674907,677 4344349,011

  9.092.200 14.187.408 -5.095.208,38 5095208,382 6770116,059 17.584.600 19.874.402 -2.289.801,96 2289801,964 7385010,346

  3.291.440 4.723.593 -1.432.152,75 1432152,748 3721954,713

  69158064,75 MR = 

   

  1 n MR

  1

  12  5 69158064,7

  = 6287096,79 BKA = 2,66 x MR = 2,66 x 6287096,79 = 16723677,48 2/3 BKA = 2/3 x 16723677,48 = 11149118,32 1/3 BKA = 1/3 x 16723677,48 = 5574559,15 BKB = - 2,66 x MR = -2,66 x 6287096,79 = -16723677,48 2/3 BKB = 2/3 x -16723677,48 = -11149118,32 1/3 BKB = 1/3 x -16723677,48 = -5574559,15

  20000000 15000000 Y-Y' 10000000 MR 5000000 BKA 2/3 BKA 1/3 BKA

  1

  2

  3

  4

  5

  6

  

7

  8

  9

  10

  11

  12 BKB

• 5000000

  2/3 BKB

• 10000000

  1/3 BKB

• 15000000
• 20000000

  Gambar Moving Range Chart

  Lampiran 2. Hasil Penyelesaian Perencanaan Produksi dengan Goal Programming Menggunakan Software LINDO Input Januari 2015

  Min DB10 + DB11 + DB12 + DB13 + DA1 + DA2 + DA3 + DA4 + DA5

• DA6 + DA7 + DA8 + DA9 SUBJECT TO

  X1 + 0.03 X1 + DB10 - DA10 = 19124491 X2 + 0.03 X2 + DB11 - DA11 = 50625982 X3 + 0.03 X3 + DB12 - DA12 = 13339932

  15X1 + 20X2 + 25X3 + DB13 - DA13 = 1632885301 0.017X1 + 0.024X2 + 0.031X3 + DB1 - DA1 = 2343600 0.00255X1 + 0.00291X2 + 0.00306X3 + DB2 - DA2 = 350000 0.00007X1 + 0.00008X2 + 0.00008X3 + DB3 - DA3 = 10000 0.00004X1 + 0.00005X2 + 0.00005X3 + DB4 - DA4 = 10000 0.00003X1 + 0.00003X2 + 0.00004X3 + DB5 - DA5 = 5000 0.00122X1 + 0.00139X2 + 0.00146X3 + DB6 - DA6 = 180000 0.00002X1 + 0.00002X2 + 0.00002X3 + DB7 - DA7 = 2500 0.00105X1 + 0.00120X2 + 0.00126X3 + DB8 - DA8 = 150000 0.00002X1 + 0.00002X2 + 0.00002X3 + DB9 - DA9 = 3000 X1 >= 5899309 X2 >= 11712403 X3 >= 2468972

  END

  Output Januari 2015

  LP OPTIMUM FOUND AT STEP 3 OBJECTIVE FUNCTION VALUE 1) 0.0000000E+00

  VARIABLE VALUE REDUCED COST DB10 0.000000 1.000000 DB11 0.000000 1.000000 DB12 0.000000 1.000000 DB13 0.000000 1.000000 DA1 0.000000 1.000000 DA2 0.000000 1.000000 DA3 0.000000 1.000000 DA4 0.000000 1.000000 DA5 0.000000 1.000000 DA6 0.000000 1.000000

  DA7 0.000000 1.000000 DA8 0.000000 1.000000 DA9 0.000000 1.000000 X1 18567468.000000 0.000000 DA10 0.000000 0.000000 X2 49151440.000000 0.000000 DA11 0.000000 0.000000 X3 14853777.000000 0.000000 DA12 1959457.875000 0.000000 DA13 0.000000 0.000000 DB1 387851.375000 0.000000 DB2 114169.703125 0.000000 DB3 3579.859863 0.000000 DB4 6057.040527 0.000000 DB5 2374.281738 0.000000 DB6 67340.671875 0.000000 DB7 848.546326 0.000000 DB8 52806.667969 0.000000 DB9 1348.546387 0.000000 ROW SLACK OR SURPLUS DUAL PRICES 2) 0.000000 0.000000 3) 0.000000 0.000000 4) 0.000000 0.000000 5) 0.000000 0.000000 6) 0.000000 0.000000 7) 0.000000 0.000000 8) 0.000000 0.000000 9) 0.000000 0.000000 10) 0.000000 0.000000 11) 0.000000 0.000000 12) 0.000000 0.000000 13) 0.000000 0.000000 14) 0.000000 0.000000 15) 12668159.000000 0.000000 16) 37439036.000000 0.000000 17) 12384805.000000 0.000000 NO. ITERATIONS= 3 RANGES IN WHICH THE BASIS IS UNCHANGED: OBJ COEFFICIENT RANGES

  VARIABLE CURRENT ALLOWABLE ALLOWABLE COEF INCREASE DECREASE DB10 1.000000 INFINITY 1.000000 DB11 1.000000 INFINITY 1.000000 DB12 1.000000 INFINITY 1.000000 DB13 1.000000 INFINITY 1.000000 DA1 1.000000 INFINITY 1.000000 DA2 1.000000 INFINITY 1.000000 DA3 1.000000 INFINITY 1.000000

  DA4 1.000000 INFINITY 1.000000 DA5 1.000000 INFINITY 1.000000 DA6 1.000000 INFINITY 1.000000 DA7 1.000000 INFINITY 1.000000 DA8 1.000000 INFINITY 1.000000 DA9 1.000000 INFINITY 1.000000 X1 0.000000 1.030000 0.000000 DA10 0.000000 INFINITY 0.000000 X2 0.000000 1.030000 0.000000 DA11 0.000000 INFINITY 0.000000 X3 0.000000 0.000000 0.000000 DA12 0.000000 0.000000 0.000000 DA13 0.000000 INFINITY 0.000000 DB1 0.000000 0.000000 0.000000 DB2 0.000000 0.000000 1.000000 DB3 0.000000 0.000000 1.000000 DB4 0.000000 0.000000 1.000000 DB5 0.000000 0.000000 0.000000 DB6 0.000000 0.000000 1.000000 DB7 0.000000 0.000000 1.000000 DB8 0.000000 0.000000 1.000000 DB9 0.000000 0.000000 1.000000 RIGHTHAND SIDE RANGES ROW CURRENT ALLOWABLE ALLOWABLE RHS INCREASE DECREASE 2 19124492.000000 3265763.000000 13048204.000000 3 50625984.000000 2449322.250000 38562208.000000 4 13339932.000000 1959457.875000 INFINITY 5**************** 312783360.000000 47559656.000000 6 2343600.000000 INFINITY 387851.375000 7 350000.000000 INFINITY 114169.703125 8 10000.000000 INFINITY 3579.859863 9 10000.000000 INFINITY 6057.040527 10 5000.000000 INFINITY 2374.281738 11 180000.000000 INFINITY 67340.671875 12 2500.000000 INFINITY 848.546326 13 150000.000000 INFINITY 52806.667969 14 3000.000000 INFINITY 1348.546387 15 5899309.000000 12668159.000000 INFINITY 16 11712403.000000 37439036.000000 INFINITY

  17 2468972.000000 12384805.000000

  INFINITY