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Pertemuan I – Konsep Dasar Riset Operasional

Pertemuan I – Konsep Dasar Riset Operasional. Riset Operasinal – 4010102053-Dewiyani. Agenda. Introduction Goals, Objectives and Expected outcome What is management science? What is Analytics, how does it relate to OR/MS? Remarks. Who am I?. Dr. M.J. Dewiyani Sunarto

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Pertemuan I – Konsep Dasar Riset Operasional

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  1. Pertemuan I – KonsepDasarRisetOperasional RisetOperasinal – 4010102053-Dewiyani

  2. Agenda Introduction Goals, Objectives and Expected outcome What is management science? What is Analytics, how does it relate to OR/MS? Remarks

  3. Who am I? • Dr. M.J.DewiyaniSunarto • dewiyani@stikom.edu • 08563062843 • DosentetapdiSTMIKSTIKOM Surabaya • RuangProdi S1 SistemInformasi – Lantai 2 GedungMerah • Senin – Jumat : 07.30 – 16.30

  4. Agenda Introduction Goals, Objectives and Expected outcome What is management science? What is Analytics, how does it relate to OR/MS? Remarks

  5. Goals, objectives and expected outcome Capaian Pembelajaran : Setelah mengikuti mata kuliah riset operasional, mahasiswa dapat menganalisis persoalan optimasi dan pembentukan model dalam proses pengambilan keputusan dengan perhitungan manual maupun hasil output komputer

  6. Materi • Dalam Mata kuliah ini mahasiswa akan mempelajari pokok bahasan- pokok bahasan sebagi berikut: • Konsep dasar riset operasional dan pembentukan model. • Pengantar program linier dan solusi grafik, • Solusi metode simpleks, Analisis post optimal, • Model transportasi dan penugasan, • Model arus jaringan, • Analytical hierarchy process (AHP), • Program dinamik, • Analisis markov, • Diagram pohon keputusan dan teori permainan.

  7. Kesepakatankitabersama…… • Apa yang sayaharapkandariAnda ? • Datangtepatwaktu – keterlambatan 0 menit • Persiapkandirisebelumkuliah – bacaRancanganPelaksanaanPembelajaran (RPP). • MembacareferensidanBerlatihsoalsebanyakmungkin • Kumpulkantugastepatwaktu. • Komposisinilai : 30% UTS, 30% UAS, 40% Tugas Nilai Minimal Kelulusan : B • 3 sksberartidalamseminggu : • 3 x 50 menitpersiapan • 3 x 50 menittatapmuka • 3 x 50 menitevaluasi

  8. Kesepakatankitabersama…… • Apa yang sayajanjikankepadaAnda? • Datangtepatwaktu – keterlambatan 0 menit • MenfasilitasibelajarAnda • Mengembalikanpekerjaan/tugasAndadalamwaktumaksimal 2 minggu • 3 sksberartidalamseminggu : • 3 x 50 menitpersiapan • 3 x 50 menittatapmuka • 3 x 50 menitevaluasi

  9. Don’t be shy! J.E. Stice, Engineering Education, pp. 291-296, 1987

  10. Review Syllabus

  11. Agenda Introduction Goals, Objectives and Expected outcome What is management science? What is Analytics, how does it relate to OR/MS? Remarks

  12. SEJARAH • PERANG DUNIA II --> ANGKATAN PERANG INGGRIS • TUJUAN : menentukan penggunaan sumber kemiliteran terbatas, dg cara paling efektif • Ditiru oleh Angkatan Perang Amerika ==> PENERAPAN ke MANAJEMEN BISNIS

  13. RISET OPERASIONAL • Masalah : alokasi optimal sumberdaya yang terbatas, dalamusahamencapaihasilterbaik. • Optimal berarti : memaksimalkanlaba, ataumeminimalkanbiaya.

  14. DEFINISI • Riset Operasional merupakan suatu pendekatan ilmiah dalam pengambilan keputusan yang digunakan untuk mencari model terbaik dalam menjalankan suatu perusahaan guna mencapai tujuan, dalam kondisi ketersediaan sumber daya yang terbatas

  15. Digunakan model matematis ( karena pendekatan ilmiah), berupa persamaan atau ketidaksamaan. • 2 macam model matematis: - deterministik : bersifat pasti, semua komponen diketahui dengan pasti. - probabilistik : tidak pasti, lebih realistik, tapi sulit dianalisa

  16. Home Runs in Management Science continued... • Sears, Roebuck & Company • One of the largest merchandise & service retailers in the world • Maintains 13,500 service and delivery vehicles, making approximately 20 million service and delivery calls annually • Combined OR techniques with GIS for more efficient service and delivery routes • Benefits: • Over $9 million in one time savings • Over $42 million in annual savings OREM, Spring 2014. Dr. Gigi Yuen-Reed

  17. Home Runs in Management Science continued... • Grantham, May, Van Otterloo and Co. • Boston-based investment firm with over $26 billion in assets • Developed a model to design portfolios that achieve investment objectives while minimizing custodial and transaction fees • Benefits: • 40-60% reduction in the average number of stocks held • Number of trades reduced by 75-80% • Reduced annual trading costs by $4 million OREM, Spring 2014. Dr. Gigi Yuen-Reed

  18. Business Use of Management Science • Some application areas: • Project planning • Capital budgeting • Inventory analysis • Production planning • Scheduling • Interfaces - Applications journal published by Institute for Operations Research and Management Sciences OREM, Spring 2014. Dr. Gigi Yuen-Reed

  19. The Management Science Process Figure 1.1 The Management Science Process OREM, Spring 2014. Dr. Gigi Yuen-Reed

  20. Apakahitu Model ? • Model adalahbentuksederhanadarisuatumasalah. • Biasanyaditulisdalampersamaanmatematika model matematika • Disebutsebagaiformulasi model

  21. Example of a Mathematical Model Profit = Revenue - Expenses or Profit = f(Revenue, Expenses) or Y = f(X1, X2)

  22. A Generic Mathematical Model Y = f(X1, X2,…,Xn) Where: Y = dependent variable (a bottom line performance measure) Xi = independent variables (inputs having an impact on Y) f(.) = function defining the relationship between the Xi and Y

  23. Model Building Illustration: Break-Even Analysis Example:Western Clothing Company Fixed Costs: cf = $10000 Variable Costs: cv = $8 per pair Price : p = $23 per pair The Break-Even Point is: FC + VC = p. v 10.000 + (8.v) = 23 v 10.000 = 15 v v = (10,000)/(15) = 666.7 pairs

  24. Model Building Illustration: Break-Even Analysis Graphical Solution Figure 1.2

  25. Model Building Illustration: Break-Even Analysis What if unit price increase from $23 to $30? Figure 1.3

  26. Things to Consider Above and Beyond OREM, Spring 2014. Dr. Gigi Yuen-Reed • Break-even is good, but how do we optimize profit? • How many pairs of jeans can we realistically sell given market condition? • Do we have sufficient resources to produce the desired quantity of products? • What is the impact of price elasticity?

  27. Formulasi model: PROGRAM LINEAR • Merupakan fungsi Linear • Mempunyai target memaksimumkan atau meminimumkan suatu nilai • Teknik Penyelesaian yang digunakan: - dua variabel : metoda grafik lebih dari dua variabel : metoda Simpleks • Model LP Secara Umum : - Variabel - Fungsi Tujuan - Fungsi Pembatas

  28. TAHAPANDALAMPROGRAM LINEAR 1.Merumuskanmasalah 2. Membuat model matematika Komponen : - variabelkeputusan - fungsitujuan - fungsipembatas 3. Menentukansuatupenyelesaian, agar diperoleh optimal solution 4. Pengujian model dansolusi 5. PembuatanImplementasi

  29. Linearitas • Suatumesinmemerlukanwaktu 10 menituntukmemprosesproduk A dan 20 menituntukmemprosesproduk B. Jam operasimesin : ……………… • Biayaangkut per unit produkdaripabrikkedaerahpemasaranA,Bdan C adalahRp 2,- , Rp 4,- danRp 6,-. Biayaangkut total : ……………

  30. General Form of a Linear Programming (LP) Problem MAX (or MIN): c1X1 + c2X2 + … + cnXn Subject to: a11X1 + a12X2 + … + a1nXn <= b1 : ak1X1 + ak2X2 + … + aknXn >=bk : am1X1 + am2X2 + … + amnXn = bm

  31. Aqua-Spa Hydro-Lux Pumps 1 1 Labor 9 hours 6 hours Tubing 12 feet 16 feet Unit Profit $350 $300 An Example LP Problem Blue Ridge Hot Tubs produces two types of hot tubs: Aqua-Spas & Hydro-Luxes. There are 200 pumps, 1566 hours of labor, and 2880 feet of tubing available.

  32. 5 Steps In Formulating LP Models: 1. Understand the problem. 2. Identify the decision variables. X1=number of Aqua-Spas to produce X2=number of Hydro-Luxes to produce 3. State the objective function as a linear combination of the decision variables. MAX: 350X1 + 300X2

  33. 5 Steps In Formulating LP Models(continued) 4. State the constraints as linear combinations of the decision variables. 1X1 + 1X2 <= 200 } pumps 9X1 + 6X2 <= 1566 } labor 12X1 + 16X2 <= 2880 } tubing 5. Identify any upper or lower bounds on the decision variables. X1 >= 0 X2 >= 0

  34. Summary of the LP Model for Blue Ridge Hot Tubs MAX: 350X1 + 300X2 S.T.: 1X1 + 1X2 <= 200 9X1 + 6X2 <= 1566 12X1 + 16X2 <= 2880 X1 >= 0 X2 >= 0

  35. Feasible/Infeasible Solutions • A feasible solution does not violate any of the constraints: • An infeasible solution violates at least one of the constraints:

  36. Summary • LP • LP Components • Steps to formulate an LP

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