ISSR Operations Research

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Decision theory or theory of choice in economics, psychology, philosophy, mathematics, computer science, and statistics is concerned with identifying the values, uncertainties and other issues relevant in a given decision, its rationality, and the resulting optimal decision. It is closely related to the field of game theory;
decision theory is concerned with the choices of individual agents whereas game theory is concerned with interactions of agents whose decisions affect each other.

Game theory
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life is full of conflict and competition. Numerous examples involving adversaries in
conflict include parlor games, military battles, political campaigns, advertising and
marketing campaigns by competing business firms, and so forth. A basic feature in many
of these situations is that the final outcome depends primarily upon the combination of
strategies selected by the adversaries. Game theory is a mathematical theory that deals
with the general features of competitive situations like these in a formal, abstract way. It
places particular emphasis on the decision-making processes of the adversaries.
Because competitive situations are so ubiquitous, game theory has applications in a
variety of areas, including in business and economics. For example, Selected Reference 2
presents various business applications of game theory. The 1994 Nobel Prize for Economic
Sciences was won by John F. Nash, Jr. (whose story is told in the movie A Beautiful Mind ),
John C. Harsanyi, and Reinhard Selton for their analysis of equilibria in the theory of
noncooperative games. Then Robert J. Aumann and Thomas C. Schelling won the 2005
Nobel Prize for Economic Sciences for enhancing our understanding of conflict and cooperation
through game-theory analysis.
As briefly surveyed in Sec. 15.6, research on game theory continues to delve into
rather complicated types of competitive situations. However, the focus in this chapter is
on the simplest case, called two-person, zero-sum games. As the name implies, these
games involve only two adversaries or players (who may be armies, teams, firms, and so
on). They are called zero-sum games because one player wins whatever the other one
loses, so that the sum of their net winnings is zero.
Section 15.1 introduces the basic model for two-person, zero-sum games, and the next
four sections describe and illustrate different approaches to solving such games. The chapter
concludes by mentioning some other kinds of competitive situations that are dealt with
by other branches of game theory.
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Network Optimization Models
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PROTOTYPE EXAMPLE

Important notes in

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https://www.facebook.com/groups/1451606001760419/1626998067554544/

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Hillier - Introduction to Operations Research 10th Edition
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https://www.facebook.com/groups/1451606001760419/1623468257907525/

OR515
Presentation by Prof. Mahmoud Moustafa El-Sherbiny
Head of Operations Research Department (ISSR)
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https://www.facebook.com/groups/1451606001760419/1623277274593290/

programming
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In the field of mathematical optimization, stochastic programming is a framework for modeling optimization problems that involve uncertainty. Whereas deterministic optimization problems are formulated with known parameters, real world problems almost invariably include some unknown parameters. When the parameters are known only within certain bounds, one approach to tackling such problems is called robust optimization. Here the goal is to find a solution which is feasible for all such data and optimal in some sense. Stochastic programming models are similar in style but take advantage of the fact that probability distributions governing the data are known or can be estimated. The goal here is to find some policy that is feasible for all (or almost all) the possible data instances and maximizes the expectation of some function of the decisions and the random variables. More generally, such models are formulated, solved analytically or numerically, and analyzed in order to provide useful information to a decision-maker.

Network
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(or Project) evaluation and review technique
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PERT is a method to analyze the involved tasks in completing a given project, especially the time needed to complete each task, and to identify the minimum time needed to complete the total project.
PERT was developed primarily to simplify the planning and scheduling of large and complex projects. It was developed for the U.S. Navy Special Projects Office in 1957 to support the U.S. Navy's Polaris nuclear submarine project.[2] It was able to incorporate uncertainty by making it possible to schedule a project while not knowing precisely the details and durations of all the activities. It is more of an event-oriented technique rather than start- and completion-oriented, and is used more in projects where time is the major factor rather than cost. It is applied to very large-scale, one-time, complex, non-routine infrastructure and Research and Development projects. An example of this was for the 1968 Winter Olympics in Grenoble which applied PERT from 1965 until the opening of the 1968 Games.
This project model was the first of its kind, a revival for scientific management, founded by Frederick Taylor (Taylorism) and later refined by Henry Ford (Fordism). DuPont's critical path method was invented at roughly the same time as PERT.
*From WIKI*

Success Story قصة نجاح
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شركه في مجال صناعة السيارات تستعين ببيت خبره في بحوث العمليات
و النتيجة نجاح في توفير مائه الف جنيه استريليني سنويا و ذلك باستخدام تكنولوجيا المحاكاه Simulation technology
نأمل في انتشار بيوت الخبره في مصر التي تنشر مثل هذه التطبيقات التي ستساعد علي النهوض بالدوله في مجالات عديدة
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http://www.scienceofbetter.co.uk/article/lucas-varity-sro-reduces-operating-costs-almost-£100000-annum

Lucas Varity s.r.o Reduces Operating Costs by Almost £100,000 Per Annum | The Science of Better TRW Automotive is one of the world's leading suppliers of car accessories. Founded in 1999 and located in Jablonec nad Nisou in the Czech Republic, Lucas Varity s.r.o is a division of TRW Automotive focused on the development, manufacture and sale of hydraulic brakes.

- 612 ( )
Department
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الانسان ام الحاسوب ؟
من الملفت أن الإنسان لا يمتلك قدرة كافية على التعامل مع المعلومات العددية والمعطيات الدقيقة لكميات كبيرة ورغم ذلك فإن له براعة مذهلة في اتخاذ قرارات معقدة, تماماً عكس جهاز الحاسوب والذي بإمكانه القيام بأكثر العمليات الحسابية تعقيداً وفي جزء من الثانية في حين يعجز تماماً أمام أبسط الأنشطة البشرية مالم يتم تمثيلها عدديا
هذا التفوق الإنساني الواضح، وعجز الأنظمة العددية الفاضح
دفعا العلماء للبحث والوصول إلى نظرية منطق الغموض
Fuzzy Logic سنة 1965 م

ثم تطوير هذا المنطق بعد ذلك ليمس معظم الجوانب التكنولوجية الحديثة على أيدي اليابانيين الذين لم يترددوا في استخدامه لتطوير منتجاتهم وصناعاتهم
أصبح من المألوف أن نرى في الأسواق مكيفات وكاميرات
وغسالات وغيرها من الأجهزة تعمل بنظام منطق الغموض Fuzzy Logic

- 509 ( , )
Department
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What is Engineering Economy?
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Engineering economy is a subset of economy for application to engineering projects. Engineers seek solutions to problems, and the economic viability of each potential alternative or design is normally considered along with the technical aspects. Engineering economy involves the evaluation of the costs and benefits of proposed projects. So, Engineering Economy study is necessary to balance the unlimited desire versus the resource-constrained world; to maximize output (worth) given input (cost) and to take the necessary for maximizing efficiency (output / input or worth / cost).
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Why Engineering Economy is Important?
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There are lots of factors that are considered in making decisions. These factors are combinations of economic and non-economic ones. Engineers play a major role in investment by making decisions based on economic analysis and design considerations. Thus, decisions often reflect the engineer’s choice of how to best invest funds by housing the proper alternative out of a set of alternatives.

- 515 ( Project Management)
Department
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is the process and activity of planning, organizing, motivating, and controlling resources, procedures and protocols to achieve specific goals in scientific or daily problems. A project is a temporary endeavor designed to produce a unique product, service or result with a defined beginning and end (usually time-constrained, and often constrained by funding or deliverables) undertaken to meet unique goals and objectives, typically to bring about beneficial change or added value. The temporary nature of projects stands in contrast with business as usual (or operations), which are repetitive, permanent, or semi-permanent functional activities to produce products or services. In practice, the management of these two systems is often quite different, and as such requires the development of distinct technical skills and management strategies.

The primary challenge of project management is to achieve all of the project goals and objectives while honoring the preconceived constraints The primary constraints are scope, time, quality and budget. The secondary — and more ambitious — challenge is to optimize the allocation of necessary inputs and integrate them to meet pre-defined objectives.

- 508 ( Modeling & Simulation)
Research Department
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Modeling and simulation (M&S) is getting information about how something will behave without actually testing it in real life. For instance, if we wanted to design a race car, but weren't sure what type of spoiler would improve traction the most, we would be able to use a computer simulation of the car to estimate the effect of different spoiler shapes on the coefficient of friction in a turn. We're getting useful insights about different decisions we could make for the car without actually building the car.
More generally, M&S is using models, including emulators, prototypes and stimulators, either statically or over time, to develop data as a basis for making managerial or technical decisions. The terms "modeling" and "simulation" are often used interchangeably.
The use of M&S within engineering is well recognized. Simulation technology belongs to the tool set of engineers of all application domains and has been included in the body of knowledge of engineering management. M&S has already helped to reduce costs, increase the quality of products and systems, and document and archive lessons learned.

- 505 ( L.P. solution)
Research Department
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In , Dantzig's simplex (or simplex method) is a popular algorithm for linear programming.
The journal Computing in Science and Engineering listed it as one of the top 10 algorithms of the twentieth century.

George Dantzig worked on planning methods for the US Air Force during World War II using a desk calculator. During 1946 his colleague challenged him to mechanize the planning process in order to entice him into not taking another job. Dantzig formulated the problem as linear inequalities inspired by the work ofWassily Leontief, however, at that time he didn't include an objective as part of his formulation. Without an objective, a vast number of solutions can be feasible, and therefore to find the "best" feasible solution, military-specified "ground rules" must be used that describe how goals can be achieved as opposed to specifying a goal itself. Dantzig's core insight was to realize that most such ground rules can be translated into a linear objective function that needs to be maximized Development of the simplex method was evolutionary and happened over a period of about a year
After Dantzig included an objective function as part of his formulation during mid-1947, the problem was mathematically more tractable. Dantzig realized that one of the unsolved problems that he mistook as homework in his professor Jerzy Neyman's class (and actually later solved), was applicable to finding an algorithm for linear programs. This problem involved finding the existence of Lagrange multipliers for general linear programs over a continuum of variables, each bounded between zero and one, and satisfying linear constraints expressed in the form of Lebesgue integrals. Dantzig later published his "homework" as a thesis to earn his doctorate. The column geometry used in this thesis gave Dantzig insight that made him believe that the Simplex method would be very efficient

method

method

method

method

- 511 (inventory control )
Research Department
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AND CONTROL
Inventory is the physical stock of items held in any business for the purpose of future production or sales. In a production shop the inventory may be in the form of raw materials. When the items
are in production process, we have the inventory as in-process inventory and at the end of the production cycle inventory is in the form of finished goods. We shall be dealing only with the
finished goods inventory. The problem of determining inventory policies is not a new concept beginning. It is only in he last two decades that it has been tackled with quantitative techniques
and mathematical models, a method amenable to optimization.
Inventory planning is the determination of the type and quantity of inventory items that would be required at future points for maintaining production schedules. Inventory planning is generally based on information from the past and also on factors that would arise in future. Once this sort of is over, the control process starts, which means that actual and planned inventory positions are compared and necessary action taken so that the business process can function efficiently.
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In inventory control, we are primarily concerned with the inventory cost control. The aim is focussed to bring down the total inventory cost per annum as much as possible. Two important questions are (1) how much to stock or how much to buy and (2) how often to buy or when to buy. An answer to the above questions is usually
given by certain , popularly known as ` order quantity models' or `economic lot/batch
size models (E.O.Q.).'

Operations researcher:
Job description
- Operations researchers use mathematical, analytical and computational techniques to provide quantitative and qualitative information that will improve managerial decision-making.
- Operations research is used in government services, commerce and industry.

What do operations researchers do?

determining and clarifying strategic and operational problems via consultation with appropriate employees
outlining objectives
collecting, analyzing and interpreting data
identifying and assessing possible strategies
presenting results that are easily understood
implementing agreed solutions
A large proportion of the work involves numerical and computational analytical techniques, often using expert systems and artificial intelligence. Contact with managerial staff and other relevant personnel is a key feature of the job.

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Typical employers of operational researchers

Consultancies
The Civil Service
Financial organizations
Commercial organizations
Industrial organizations
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Qualifications and training required
Many entrants study subjects such as computing, mathematics and statistics. There are a number of postgraduate courses available in operational research and employers often prefer candidates who have completed one.

Relevant experience can also be helpful, although difficult to obtain: placements and vacation work are highly sought after.
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Key skills for operational researchers

Good intellectual skills
Logical skills
Mathematical skills
Problem solving abilities
Effective team working skills
Interpersonal skills
A diplomatic and persuasive manner
Verbal and written communication skills

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Average Salaries for Operations research in UAE - Dubai -Abo Zabi

Sample jobs related to operations management
source bayt .com

Career Prospects for Operations Research

The quantitative modeling expertise that you will have gained will open up extensive career opportunities for you in the private sector, as well as in the public and non-profit sectors.

Your degree prepares you for a career as Operations Research or Management specialist, in a consultancy firm, a software company, and many other companies.
You will also be in an excellent position to pursue a PHD or any related field.

The Master’s program in Operations Research and Management Science is one of the programs with the best career prospects. Modern developments such as Big Data and Business Analytics have increased the already high demand for specialists in Operations Research and Management Science. It is estimated that within a few years, 4.4 million vacancies will be available worldwide for, among others, graduates in Operations Research and Management Science. It is expected that only 1.5 million of these can be filled.

alumni work within Operations Research firms like Quintiq, Ortec, and CQM; logistics or general consultancy firms like KPMG and McKinsey; Operations Research departments in companies like Dutch Railways, KLM, and Shell; and firms developing logistics software like SAP, Oracle, and Incontrol. Examples of employers and job titles of our recent graduates are the following:

Supply Chain Consultant OM Partners
Implementation Consultant ORTEC
Life Cycle Management Analyst Philips Healthcare
Optimization Algorithm Expert Quintiq
Quantitative Analyst Rabobank Nederland
Quantitative Researcher Robeco
Consultant Quintiq
Supply Chain Consultant ORTEC
Risk Manager Cardano
Supply Chain Specialist Bavaria
Consultant Fabory Group

Prepare you for a successful career in a senior management position, equipping you with a range of skills and understanding to become a high-level operations.
Many of our graduates have gone on to successfully develop their careers as operations, logistics and supply management strategists. They now work for a range of international organizations in the automobile, oil and gas, construction and infrastructure and other sectors. The companies for which they work include:
Materials scheduler
Materials analyst/manager
Purchasing manager
Process engineer
Supply chain analyst
Supply chain systems manager
Fleet manager
Supply chain consultant
Inventory specialist
Warehouse operations manager
Facilities manager

and more .....

Operations Research
is the scientific study of operations for the purpose of making better decisions.

- In simple terms, it is described as
“the science of better”.

- Modern synonyms for Operational Research are
Management Science or Decision Science.
It is often considered to be a sub-field of mathematics.

- But now a days the principles and techniques of Operational Research are being applied in every field of human research and development.

History
- Operational Research has been existed as a science since 1930‘s.
But as a formal discipline Operational Research originated by the efforts of military planner during World War II .
- The analytical study of military problems was undertaken to provide scientific basis for decisions to improve military operations.
In the decade after World War-II the techniques began to be applied more widely in problems of business, industries and societies.
- Since the time Operational Research has expanded into a field widely used in industries ranging from petro-chemical to airlines, finances, logistics and government.
Now it has become an area of active academic and industrial research.