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Metode Simpleks

Salah satu teknik penentuan solusi optimal yang digunakan dalam pemrograman linier adalah metode simpleks. Penentuan solusi optimal menggunakan metode simpleks didasarkan pada teknik eleminasi Gauss Jordan. Penentuan solusi optimal dilakukan dengan memeriksa titik ekstrim satu per satu dengan cara perhitungan iteratif. Sehingga penentuan solusi optimal dengan simpleks dilakukan tahap demi tahap yang disebut dengan iterasi. Iterasi ke-i hanya tergantung dari iterasi sebelumnya.

Ada beberapa istilah yang sangat sering digunakan dalam metode simpleks, diantaranya : 1. Iterasi adalah tahapan perhitungan dimana nilai dalam perhitungan itu tergantung dari nilai tabel sebelumnya. 2. Variabel non basis adalah variabel yang nilainya diatur menjadi nol pada sembarang iterasi. Dalam terminologi umum, jumlah variabel non basis selalu sama dengan derajat bebas dalam sistem persamaan. 3. Variabel basis merupakan variabel yang nilainya bukan nol pada sembarang iterasi. Pada solusi awal, variabel basis merupakan variabel slack (jika fungsi kendala merupakan pertidaksamaan ≤ ) atau variabel buatan (jika fungsi kendala menggunakan pertidaksamaan ≥ atau =). Secara umum, jumlah variabel basis selalu sama dengan jumlah fungsi pembatas (tanpa fungsi non negatif). 4. Solusi atau nilai kanan merupakan nilai sumber daya pembatas yang masih tersedia. Pada solusi awal, nilai kanan atau solusi sama dengan jumlah sumber daya pembatas awal yang ada, karena aktivitas belum dilaksanakan. 5. Variabel slack adalah variabel yang ditambahkan ke model matematik kendala untuk mengkonversikan pertidaksamaan ≤ menjadi persamaan (=). Penambahan variabel ini terjadi pada tahap inisialisasi. Pada solusi awal, variabel slack akan berfungsi sebagai variabel basis. 6. Variabel surplus adalah variabel yang dikurangkan dari model matematik kendala untuk mengkonversikan pertidaksamaan ≥ menjadi persamaan (=). Penambahan ini terjadi pada tahap inisialisasi. Pada solusi awal, variabel surplus tidak dapat berfungsi sebagai variabel basis. 7. Variabel buatan adalah variabel yang ditambahkan ke model matematik kendala dengan bentuk ≥ atau = untuk difungsikan sebagai variabel basis awal. Penambahan variabel ini terjadi pada tahap inisialisasi. Variabel ini harus bernilai 0 pada solusi optimal, karena kenyataannya variabel ini tidak ada. Variabel hanya ada di atas kertas. 8. Kolom pivot (kolom kerja) adalah kolom yang memuat variabel masuk. Koefisien pada kolom ini akn menjadi pembagi nilai kanan untuk menentukan baris pivot (baris kerja). 9. Baris pivot (baris kerja) adalah salah satu baris dari antara variabel basis yang memuat variabel keluar. 10. Elemen pivot (elemen kerja) adalah elemen yang terletak pada perpotongan kolom dan baris pivot. Elemen pivot akan menjadi dasar perhitungan untuk tabel simpleks berikutnya. 11. Variabel masuk adalah variabel yang terpilih untuk menjadi variabel basis pada iterasi berikutnya. Variabel masuk dipilih satu dari antara variabel non basis pada setiap iterasi. Variabel ini pada iterasi berikutnya akan bernilai positif. 12. Variabel keluar adalah variabel yang keluar dari variabel basis pada iterasi berikutnya dan digantikan oleh variabel masuk. Variabel keluar dipilih satu dari antara variabel basis pada setiap iiterasi. Variabel ini pada iterasi berikutnya akan bernilai nol.

BENTUK BAKU Sebelum melakukan perhitungan iteratif untuk menentukan solusi optimal, pertama sekali bentuk umum pemrograman linier dirubah ke dalam bentuk baku terlebih dahulu. Bentuk baku dalam metode simpleks tidak hanya mengubah persamaan kendala ke dalam bentuk sama dengan, tetapi setiap fungsi kendala harus diwakili oleh satu variabel basis awal. Variabel basis awal menunjukkan status sumber daya pada kondisi sebelum ada aktivitas yang dilakukan. Dengan kata lain, variabel keputusan semuanya masih bernilai nol. Dengan demikian, meskipun fungsi kendala pada bentuk umum pemrograman linier sudah dalam bentuk persamaan, fungsi kendala tersebut masih harus tetap berubah.

Ada beberapa hal yang harus diperhatikan dalam membuat bentuk baku, yaitu : 1. Fungsi kendala dengan pertidaksamaan ≤ dalam bentuk umum, dirubah menjadi persamaan (=) dengan menambahkan satu variabel slack. 2. Fungsi kendala dengan pertidaksamaan ≥ dalam bentuk umum, dirubah menjadi persamaan (=) dengan mengurangkan satu variabel surplus. 3. Fungsi kendala dengan persamaan dalam benttuk umum,ditambahkan satu artificial variabel (variabel buatan).

Perhatikan kasus A berikut :

Fungsi tujuan : minimumkan z = 2 x1 + 5.5 x2 Kendala : x1 + x2 = 90 0.001 x1 + 0.002 x2 ≤ 0.9 0.09 x1 + 0.6 x2 ≥ 27 0.02 x1 + 0.06 x2 ≤ 4.5 x1, x2 ≥ 0

Bentuk di atas adalah bentuk umum pemrograman liniernya. Kedalam bentuk baku, model matematik tersebut akan berubah menjadi : Fungsi tujuan : minimumkan z = 2 x1 + 5.5 x2 Kendala : x1 + x2 + s1 = 90 0.001 x1 + 0.002 x2 + s2 = 0.9 0.09 x1 + 0.6 x2 – s3 + s4 = 27 0.02 x1 + 0.06 x2 + s5 = 4.5 x1, x2 , s1, s2, s3, s4, s5 ≥ 0 Fungsi kendala pertama mendapatkan variable buatan (s1), karena bentuk umumnya sudah menggunakan bentuk persamaan. Fungsi kendala kedua dan keempat mendapatkan variabel slack (s2 dan s5) karena bentuk umumnya menggunakan pertidaksamaan ≤, sedangkan fungsi kendala ketiga mendapatkan variabel surplus (s3) dan variabel buatan (s4) karena bentuk umumnya menggunakan pertidaksamaan ≥.

Perhatikan pula kasus B berikut ini : Maksimumkan z = 2x1 + 3x2 Kendala : 10 x1 + 5 x2 ≤ 600 6 x1 + 20 x2 ≤ 600 8 x1 + 15 x2 ≤ 600 x1, x2 ≥

Bentuk di atas juga merupakan bentuk umum. Perubahan ke dalam bentuk baku hanya membutuhkan variabel slack, karena semua fungsi kendala menggunakan bentuk pertidaksamaan ≤ dalam bentuk umumnya. Maka bentuk bakunya adalah sebagai berikut : Maksimumkan z = 2x1 + 3x2 + 0s1 + 0s2 + 0s3 Kendala : 10 x1 + 5 x2 + s1 = 600 6 x1 + 20 x2 + s2 = 600 8 x1 + 15 x2 + s3 = 600 x1, x2 , s1 , s2 , s3 ≥ 0 s1 , s2 , s3 merupakan variable slack.

PEMBENTUKAN TABEL SIMPLEKS
Dalam perhitungan iterative, kita akan bekerja menggunakan tabel. Bentuk baku yang sudah diperoleh, harus dibuat ke dalam bentuk tabel.
Semua variabel yang bukan variabel basis mempunyai solusi (nilai kanan) sama dengan nol dan koefisien variabel basis pada baris tujuan harus sama dengan 0. Oleh karena itu kita harus membedakan pembentukan tabel awal berdasarkan variabel basis awal. Dalam sub bab ini kita hanya akan memperhatikan fungsikendala yang menggunakan variabel slack dalam bentuk bakunya, sedangkan yang menggunakan variabel buatan akan dibahas pada sub bab lainnya.

Gunakan kasus B di atas, maka tabel awal simpleksnya adalah :
|VB |X1 |X2 |S1 |S2 |S3 |solusi |
|Z |-2 |-3 |0 |0 |0 |0 |
|S1 |10 |5 |1 |0 |0 |600 |
|S2 |6 |20 |0 |1 |0 |600 |
|S3 |8 |15 |0 |0 |1 |600 |

Sumber: http://rezakusuma.blog.com/metode-simpleks/od

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...2장 연습문제 B >문제 1.K스포츠기구사는 일반 야구글러브와 캐쳐용 야구글러브를 생산한다. 글러브는 봉제공정, 마무리공정, 포장 및 수송공정을 거쳐서 생산되는데 이 회사에서 다음달에 동원할 수 있는 생산능력은 봉제공정이 900시간, 마무리공정이 300시간, 그리고 포장/수송공정이 100시간이다. 각 제품생산에 투입되는 각 공정의 생산시간과 새당 이익은 다음표와 같다. 제품 | 봉제공정 | 마무리공정 | 포장수송 | 개당이익 | 일반글러브 | 1시간 | 1/2시간 | 1/8시간 | 25,000원 | 캐쳐용 글러브 | 3/2시간 | 1/3시간 | 1/4시간 | 40,000원 | 총 이익을 최대화 할 수 있는 선형계획모형을 개발하시오. >풀이 X1 = 일반글러브 개수, X2 = 캐쳐용 글러브 개수 Max Z = 25000X1+40000X2 ST. X1+3/2X2=<900(봉제공정) 1/2X1+1/3X2=<300(마무리공정) 1/8X1+1/4X2=<100(포장수송공정) X1, X2>=0(비음제약식) 최적해 (X1,X2) =(500,150) 최대이익 : 18,500,000원 >문제 2. 소와 염소를 기르는 농장에서는 보레토와 칼파를 섞어서 사료로 쓰고 있다. 보레토 한 부대는 10,000원이고 100단위의 칼슘과 400단위의 단백질을 갖고 있고, 칼파 한 부대는 15,000원이고200단위의 칼슘과 200단위의 단백질을 갖고 있다. 이 농장에 있는 가축들을 하루 먹이는 데에는 6,000단위의 칼슘과 12,000단위의 단백질이 소요된다. 또한 사료공급업자와의 계약조건에 따르면 칼파 한 부대를 사기 위해서는 보레토를 최소한 2부대를 사야만 한다. 총 비용을 최소화 할 수 있는 선형계획모형을 개발하시오. >풀이 제품 | 칼슘 | 단백질 | 가격 | 보레토(X1) | 100 | 400 | 10,000 | 칼파(X2) | 200 | 200 | 15,000 | X1 = 보레토의 개수, X2 = 칼파의 개수 Min = 10,000X1+15,000X2 ST. 100X1+200X2>=6,000(칼슘) 400X1+200X2>=12000(단백질) 2X1>=X2(칼파1부대는 최소한 보레토2부대) X1,X2>=0(비음제약식) 최적해(X1, X2) =(20,20) 최소 비용 : 500,000 >문제 3. L전자에서는 계산기용 IC칩을 생산하고 있다. L전자는 두 개의 공장을 통하여 IC칩을 생산하며 5개의 도매상에게 판매하고 있다. 공장 1과 공장 2에서의 개당 생산비용은 21,900원, 23,800원이다. 수요예측에 의하면 도매상 1,2,3,4,5에게 각각 2,000개, 3,000개 1,000개, 5,000개, 4,000개를 공급해야 한다. 한 개의 IC칩을 한 공장에서 도매상에게 넘기는 데 드는 수송비...

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