Operational research (OR) is a scientific approach to decision-making that aims to provide rational solutions to complex problems. It involves modeling real-world situations mathematically and using analytical and numerical techniques to determine optimal or near-optimal solutions. OR emerged in the 1940s to help Allied forces in World War II and has since been applied widely in business and industry. Key aspects of OR include quantitative modeling and analysis, interdisciplinary team-based problem solving, and using data and experimentation to evaluate alternative solutions and recommend optimal decisions.
Introduction to Operations Research with basic concepts along with Models in Operation Research also addressed.
Subscribe to Vision Academy YouTube Channel
https://www.youtube.com/channel/UCjzpit_cXjdnzER_165mIiw
The operation research book that involves all units including the lpp problems, integer programming problem, queuing theory, simulation Monte Carlo and more is covered in this digital material.
Why Operations Research?
Introduction
Origin of operations research
Definition of operations research
Characteristics of operations research
Role of operations research in decision-making
Methods of solving operations research problem
Phases in solving operations research problems
Typical problems in operations research
Scope of operations research
Why to study operations research
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Introduction to Operations Research with basic concepts along with Models in Operation Research also addressed.
Subscribe to Vision Academy YouTube Channel
https://www.youtube.com/channel/UCjzpit_cXjdnzER_165mIiw
The operation research book that involves all units including the lpp problems, integer programming problem, queuing theory, simulation Monte Carlo and more is covered in this digital material.
Why Operations Research?
Introduction
Origin of operations research
Definition of operations research
Characteristics of operations research
Role of operations research in decision-making
Methods of solving operations research problem
Phases in solving operations research problems
Typical problems in operations research
Scope of operations research
Why to study operations research
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
2. 2
Introduction
• Operations Research is an Art and Science
• It had its early roots in World War II and is
flourishing in business and industry with the
aid of computer
• Primary applications areas of Operations
Research include forecasting, production
scheduling, inventory control, capital
budgeting, and transportation.
3. INTRODUCTION TO
OPERATIONAL RESEARCH
Operational Research is a systematic and
analytical approach to decision making and
problem solving.
Operational Research is an Branch of
applied mathematics that uses techniques
and statistics to arrive at Optimal solutions to
solve complex problems.
4. • It is typically concerned with determining the
maximum profit, sale, output, crops yield and
efficiency
• And minimum losses, risks, cost, and time of
some objective function. It have also become
an important part of PROFESSION.
5. 5
What is Operations Research?
Operations
The activities carried out in an organization.
Research
The process of observation and testing characterized
by the scientific method. Situation, problem
statement, model construction, validation,
experimentation, candidate solutions.
Operations Research is a quantitative approach to
decision making based on the scientific method of
problem solving.
6. 6
What is Operations Research?
• Operations Research is the scientific
approach to execute decision making, which
consists of:
– The art of mathematical modeling of
complex situations
– The science of the development of solution
techniques used to solve these models
– The ability to effectively communicate the
results to the decision maker
7. 7
What Do they do
1. OR professionals aim to provide rational bases for
decision making by seeking to understand and
structure complex situations and to use this
understanding to predict system behavior and
improve system performance.
2. Much of this work is done using analytical and
numerical techniques to develop and manipulate
mathematical and computer models of
organizational systems composed of people,
machines, and procedures.
8. HISTORY OF
OPERATIONAL RESEARCH
• There is no clear history that marks the Birth if
O.R., it is generally accepted that the field
originated in England during the World War II.
• Some say that Charles Babbage (1791-1871) is the
Father of O.R because his research into the cost of
transportation and sorting of mail led to England’s
University Penny Post in 1840.
9. Modern Operations Research originated at the Bowdsey
Research Station in U.K. in 1937 to analyse and improve the
working of the UK’s Early Warning Rador System.
During the Second World War about 1000 Men and Women
were engaged to work for British Army.
After World War II, Military Operational Research in U.K.
became Operational Analysis (OA) within the U.K. Ministry of
Defence with expanded techniques and graving awareness.
HISTORY OF
OPERATIONAL RESEARCH
11. Definition
• OR is the application of the methods of science to
complex problems in the direction and management of
large system of men, machines, materials and money in
industry, business, government and defense.
• The distinctive approach is to develop a scientific model
of the system incorporating measurements of factors
such as chance and risk, with which to predict and
compare the outcomes of alternative decisions,
strategies or controls. The purpose is to help
management in determining its policy & actions
scientifically
-- Operation Research Society - UK
12. • OR is concerned with scientifically deciding
how to best design and operate man-machine
systems usually requiring the allocation of
scarce resources.
-- Operation Research Society, America
14. DECISION MAKING
Every industrial organisation faces multifacet
problems to identify best possible solution to
their problems.
OR aims to help the executives to obtain optimal
solution with the use of OR techniques.
It also helps the decision maker to improve his
creative and judicious capabilities, analyse and
understand the problem situation leading to
better control, better co-ordination, better
systems and finally better decisions.
15. SCIENTIFIC APPROACH
OR applies scientific methods, techniques and tools
for the purpose of analysis and solution of the
complex problems.
In this approach there is no place for guesswork and
the person bias of the decision maker.
16. INTER-DISCIPLINARY
TEAM APPROACH
Basically the industrial problems are of complex
nature and therefore require a team effort to
handle it.
This team comprises of scientist, mathematician
and technocrats. Who jointly use the OR tools to
obtain a optimal solution of the problem.
They tries to analyse the cause and effect
relationship between various parameters of the
problem and evaluates the outcome of various
alternative strategies.
17. SYSTEM APPROACH
The main aim of the system approach is to trace out
all significant and indirect effects for each proposal
on all sub-system on a system and to evaluate each
action in terms of effects for the system as a whole.
The inter-relationship and interaction of each sub-
system can be handled with the help of
mathematical/analytical models of OR to obtain
acceptable solution.
18. OBJECTIVE
Operational Research always try to find the
best and optimal solution to the problem.
For this purpose objectives of the
organisation are defined and analysed.
These objectives are then used as the basis
to compare the alternative courses of action.
19. SCOPE OF
OPERATIONAL RESEARCH
The scope of OR is not only confined to any specific
agency like defence services but today it is widely used
in all industrial organisations.
It can be used to find the best solution to any problem
be it simple or complex. It is useful in every field of
human activities. Thus, it attempts to resolve the
conflicts of interest among the components of
organization in a way that is best for the organisation as
a whole.
The main fields where OR is extensively used are given
in next slide.
20. FIELDS
National Planning and Budgeting
Defence Services
Industrial Establishment and Private Sector
Units
R & D and Engineering
21. PHASES IN OR
• JUDGEMENT PHASE.
• RESEARCH PHASE.
• ACTION PHASE.
22. JUDGEMENT PHASE
• Identification of real-life problem.
• Selection of an appropriate objective & the values of
various variable related to this objective
• Application of the appropriate scale of measurement
• Formulation of appropriate model of the problem,
abstracting the essential information to obtain the
decision’s makers goals.
23. RESEARCH PHASE
• Observation & data collection for better
understanding of the problem
• Formulation of hypothesis and model
• Experimentation to test the hypothesis on basis of
additional data.
• Analysis of the available information & verification of
the hypothesis using pre-established measure of
desirability
• Prediction of various result from the hypothesis
• Generalization of the result & consideration of
alternative methods.
24. ACTION PHASE
• Making recommendation for implementing
the decision by an individual who is an the
position to implement result
26. Deterministic models assume all data are known
with certainty
Deterministic models involve optimization
Stochastic models explicitly represent uncertain
data via random variables or stochastic processes
Stochastic models characterize / estimate system
performance.
Deterministic vs. Stochastic Models
27. Problem Solving and Decision Making
• 7 Steps of Problem Solving
(First 5 steps are the process of decision making)
– Identify and define the problem.
– Determine the set of alternative solutions.
– Determine the criteria for evaluating the alternatives.
– Evaluate the alternatives.
– Choose an alternative.
---------------------------------------------------------------
– Implement the chosen alternative.
– Evaluate the results.
28. Quantitative Analysis and Decision
Making
• Potential Reasons for a Quantitative Analysis
Approach to Decision Making
– The problem is complex.
– The problem is very important.
– The problem is new.
– The problem is repetitive.
29. Problem Solving Process
Data
Solution
Find
a Solution
Tools
Situation
Formulate the
Problem
Problem
Statement
Test the Model
and the Solution
Procedure
Establish
a Procedure
Implement
the Solution
Construct
a Model
Model
Implement a Solution
Goal: solve a problem
• Model must be valid
• Model must be
tractable
• Solution must be
useful
30. The Situation
• May involve current operations or proposed
developments due to expected market shifts
• May become apparent through consumer
complaints or through employee
suggestions
• May be a conscious effort to improve
efficiency or respond to an unexpected crisis
Example: Internal nursing staff not happy with their schedules; hospital using
too many external nurses.
Data
Situation
31. Problem Formulation
• Define variables
• Define constraints
• Identify data requirements
Example: Maximize individual nurse preferences subject to demand
requirements, or minimize nurse dissatisfaction costs.
Formulate the
Problem
Problem
Statement
Data
Situation
• Describe system
• Define boundaries
• State assumptions
• Select performance measures
32. Constructing a Model
• Problem must be translated
from verbal, qualitative terms to
logical, quantitative terms
• A logical model is a series of
rules, usually embodied in a
computer program
Example: Define relationships between individual nurse assignments
and preference violations; define tradeoffs between the use
of internal and external nursing resources.
Construct
a Model
Model
Formulate the
Problem
Problem
statement
Data
Situation
• A mathematical model is a collection of
functional relationships by which allowable
actions are delimited and evaluated.
33. Model Development
• Models are representations of real objects
or situations.
• Three forms of models are iconic, analog,
and mathematical.
– Iconic models are physical replicas (scalar
representations) of real objects.
– Analog models are physical in form, but do not
physically resemble the object being modeled.
34. – Mathematical models represent real world
problems through a system of mathematical
formulas and expressions based on key
assumptions, estimates, or statistical analyses.
35. 35
Advantages of Models
• Generally, experimenting with models
(compared to experimenting with the real
situation):
– requires less time
– is less expensive
– involves less risk
36. Mathematical Models
• Cost/benefit considerations must be made in
selecting an appropriate mathematical
model.
• Frequently a less complicated (and perhaps
less precise) model is more appropriate than
a more complex and accurate one due to cost
and ease of solution considerations.
37. Mathematical Models
• Relate decision variables (controllable inputs) with
fixed or variable parameters (uncontrollable inputs).
• Frequently seek to maximize or minimize some
objective function subject to constraints.
• Are said to be stochastic if any of the uncontrollable
inputs (parameters) is subject to variation (random),
otherwise are said to be deterministic.
• Generally, stochastic models are more difficult to
analyze.
• The values of the decision variables that provide the
mathematically-best output are referred to as the
optimal solution for the model.
38. 38
Transforming Model Inputs into
Output
Uncontrollable Inputs
(Environmental Factors)
Controllable
Inputs
(Decision Variables)
Output
(Projected Results)
Mathematical
Model
39. Solving the Mathematical Model
• Many tools are available as
discussed in this course
• Some lead to “optimal”
solutions
• Others only evaluate
candidates trial and
error to find “best” course
of action
Example: Collect input data -- nurse profiles and demand requirements; apply
algorithm; post-process results to get monthly schedules.
Model
Solution
Find a
solution
Tools
40. Model Solution
• Involves identifying the values of the decision variables
that provide the “best” output for the model.
• One approach is trial-and-error.
– might not provide the best solution
– inefficient (numerous calculations required)
• Special solution procedures have been developed for
specific mathematical models.
– some small models/problems can be solved by hand
calculations
– most practical applications require using a computer
41. Computer Software
• A variety of software packages are available
for solving mathematical models, some are:
– Spreadsheet packages such as Microsoft Excel
– The Management Scientist (MS)
– Quantitative system for business (QSB)
– LINDO, LINGO
– Quantitative models (QM)
– Decision Science (DS)
42. Model Testing and Validation
• Often, the goodness/accuracy of a model cannot be assessed
until solutions are generated.
• Small test problems having known, or at least expected,
solutions can be used for model testing and validation.
• If the model generates expected solutions:
– use the model on the full-scale problem.
• If inaccuracies or potential shortcomings inherent in the
model are identified, take corrective action such as:
– collection of more-accurate input data
– modification of the model
43. Implementation
• A solution to a problem usually implies
changes for some individuals in the
organization
• Often there is resistance to change,
making the implementation difficult
• A user-friendly system is needed
• Those affected should go through
training
Situation
Procedure
Implement
the Procedure
Example: Implement nurse scheduling system in one unit at a time. Integrate
with existing HR and T&A systems. Provide training sessions during
the workday.
44. Implementation and Follow-Up
• Successful implementation of model results is
of critical importance.
• Secure as much user involvement as possible
throughout the modeling process.
• Continue to monitor the contribution of the
model.
• It might be necessary to refine or expand the
model.
45. Report Generation
• A managerial report, based on the results of
the model, should be prepared.
• The report should be easily understood by the
decision maker.
• The report should include:
– the recommended decision
– other pertinent information about the results (for
example, how sensitive the model solution is to the
assumptions and data used in the model)
46. Examples of OR Applications
• Rescheduling aircraft in response to groundings and
delays
• Planning production for printed circuit board
assembly
• Scheduling equipment operators in mail processing
& distribution centers
• Developing routes for propane delivery
• Adjusting nurse schedules in light of daily
fluctuations in demand
48. Shortcomings
• Solutions are derived by making simplified
assumptions so the solutions have limitations.
• Sometime Models do not represent the realistic
situations in which decisions must be made.
• Decision maker is not fully aware of the limitations of
the model.
• Many real world problems just cannot have an OR
solution.
49. Opportunities
• It compels the decision maker to quite explicit
about the objective, assumption and his
perspective to the constraints.
• Variables which influence decisions are
considered.
• Gaps in data required to support solutions to a
problem.
• Management of time because models can be
solved by a computer.
50. Application OR
• Finance & Accounting
• Marketing
• Purchasing, procurement and Exploration
• Production Management
• Manufacturing
• Maintenance and Project scheduling
• Personnel Management
• MIS & General Management
• Government.
51. Application Areas
• Strategic planning
• Supply chain management
• Pricing and revenue management
• Logistics and site location
• Optimization
• Marketing research