Since the advent of the industrial revolution, the world has seen a remarkable growth in
the size and complexity of organizations. The artisans' small shops of an earlier era have
evolved into the billion-dollar corporations of today. An integral part of this revolutionary change has been a tremendous increase in the division of labor and segmentation of management responsibilities in these organizations. The results have been spectacular. However, along with its blessings, this increasing specialization has created new problems, problems that are still occurring in many organizations. One problem is a tendency for the many components of an organization to grow into relatively autonomous empires with their own goals and value systems, thereby losing sight of how their activities and objectives mesh with those of the overall organization. What is best for one component frequently is detrimental to another, so the components may end up working at cross purposes. A related problem is that as the complexity and specialization in an organization increase, it becomes more and more difficult to allocate the available resources to the various activities in a way that is most effective for the organization as a whole. These kinds of problems and the need to find a better way to solve them provided the environment for the emergence of operations research (commonly referred to as OR).
The roots of OR can be traced back many decades, when early attempts were made to use a scientific approach in the management of organizations. However, the beginning of the activity called operations research has generally been attributed to the military services early in World War II. Because of the war effort, there was an urgent need to allocate scarce resources to the various military operations and to the activities within each operation in an effective manner. Therefore, the British and then the U.S. military management called upon a large number of scientists to apply a scientific approach to dealing with this and other strategic and tactical problems. In effect, they were asked to do research on (military) operations. These teams of scientists were the first OR teams. By developing effective methods of using the new tool of radar, these teams were instrumental in winning the Air Battle of Britain. Through their research on how to better manage convoy and antisubmarine operations, they also played a major role in winning the Battle of the North Atlantic. Similar efforts assisted the Island Campaign in the Pacific. When the war ended, the success of OR in the war effort spurred interest in applying OR outside the military as well. As the industrial boom following the war was running its course, the problems caused by the increasing complexity and specialization in organizations were again coming to the forefront. It was becoming apparent to a growing number of people, including business consultants who had served on or with the OR teams during the war, that these were basically the same problems that had been faced by the military but in a different context. By the early 1950s, these individuals had introduced the use of OR to a variety of organizations in business, industry, and government. The rapid spread of OR soon followed.
At least two other factors that played a key role in the rapid growth of OR during this period can be identified. One was the substantial progress that was made early in improving the techniques of OR. After the war, many of the scientists who had participated on OR teams or who had heard about this work were motivated to pursue research relevant to the field; important advancements in the state of the art resulted. A prime example is the simplex method for solving linear programming problems, developed by George Dantzig in 1947. Many of the standard tools of OR, such as linear programming, dynamic programming, queueing theory, and inventory theory, were relatively well developed before the end of the 1950s.
A second factor that gave great impetus to the growth of the field was the onslaught of the computer revolution. A large amount of computation is usually required to deal most effectively with the complex problems typically considered by OR. Doing this by hand would often be out of the question. Therefore, the development of electronic digital computers, with their ability to perform arithmetic calculations thousands or even millions of times faster than a human being can, was a tremendous boon to OR. A further boost came in the 1980s with the development of increasingly powerful personal computers accompanied by good software packages for doing OR. This brought the use of OR within the easy reach of much larger numbers of people. Today, literally millions of individuals have ready access to OR software. Consequently, a whole range of computers from mainframes to laptops now are being routinely used to solve OR problems. (Hamdy A. Taha)

Operation Research

OR mempelajari tentang sejarah, pengertian, system & model OR dan tahap-tahap pengambilan keputusan serta Memecahkan permasalahan teknis dan manajemen dengan menggunakan metode LP, dan kondisi apabila terjadi perubahan pada parameter model dengan analisis sensitivitas. Memecahkan permasalahan transportasi agar biaya yang dikeluarkan minimum dgn menggunakan beberapa metode trasnportasi. Memecahkan permasalahan transportasi agar biaya penugasan yang dikeluarkan minimum (atau keuntungan yang diperoleh maksimum) dgn metode Hungarian, Merumuskan permasalahan LP dengan fungsi tujuan lebih dari satu. Namun Yang paling utama adalah bagaimana meletakkan dasar matematis linear programming
Linear Programming (pemrograman linier) merupakan teknik matematik yang didesain untuk membantu manajer dalam perencanaan dan pengambilan keputusan penggunaan sumber daya ekonomi yang dimiliki oleh suatu perusahaan.

Aplikasi di bidang marketing:
1. pemilihan media periklanan
2. riset pemasaran
3. dan distribusi produk dari gudang perusahaan ke berbagai pasar.

Aplikasi bidang produksi/operasi:
1. kombinasi produk yang akan diproduksi
2. penjadualan proses produksi
3. penjadualan tugas karyawan, dan lain-lain.

Aplikasi bidang keuangan:
1. pemilihan portfolio investasi.
2. financial decision making.

Aplikasi persoalan ekonomi makro: untuk menganalisis pengaruh kebijakan pemerintah dan perubahan pasar pada sektor ekonomi.

Karakteristik Permasalahan Programasi Linier
a. Semua permasalahan PL memiliki tujuan (objective function) untuk memaksimumkan atau meminimumkan sesuatu (kuanti-tas), seperti profit atau biaya.
b. Permasalahan PL memiliki restriksi (konstrain) yang membatasi tingkatan pencapaian tujuan (objective function).
c. Adanya beberapa alternatif tindakan yang bisa dipilih. Sebagai contoh, kalau suatu perusahaan menghasilkan tiga produk ma-ka alternatif solusinya adalah apakah ia akan mengalokasikan semua resources untuk satu produk, membagi rata resources untuk ketiga produk, atau mendistribusikannya dengan cara yang lainnya.
d. Fungsi tujuan dan kendala (konstrain) dalam permasalahan PL diekspresikan dalam bentuk persamaan atau pertidaksamaan linier.


Langkah-langkah Dalam Formulasi LP:

1. Mengidentifikasi dan menotasikan variabel dalam fungsi tujuan dan kendala.
2. Memformulasikan fungsi tujuan.
3. Memformulasikan fungsi kendala.
4. Memasukkan kendala nonnegativitas.

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Designer: Douglas Bowman | Dimodifikasi oleh Abdul Munir Original Posting Rounders 3 Column