Operations Research is a discipline that uses analytical techniques to improve decision-making. It employs mathematical modeling, statistical analysis, and other optimization methods to arrive at optimal or near-optimal solutions to complex problems. Operations Research overlaps with fields like industrial engineering, operations management, and computer science. It has a long history dating back to the early 20th century and has been widely used in both business and military applications.

1. OPERATIONS OF RESEARCH

2.  OPERATION RESEARCH
 3 UNITS
 11: 00 PM – 2:00 PM

3.  DEBORAH L. NUQUE
 MIT
 11 YRS IN ACADEME
 CONSULATION HRS FRI AND SAT
 LEVEL OF EXPECTATIONS
 PRESCRIBED UNIFORM
 ID TO BE DISPLAYED
 BAG FOR MATERIALS
 GOOD GROOMING
 THE USE OF CELLPHONES
 OTHERS

4.  PROBLEM SETS/ASSIGNMENTS
 QUIZZES
 PROJECT
 MAJOR EXAMS
 HAND OUTS/ NOTEBOOK
 COMPLETE ATTENDANCE

5.  CLASS STANDING (60 %)
 P.S 20%
 AT 10%
 Q 30%
 MAJOR EXAM (40%)
 ABSENCES
 ONCE A WEEK 3 ABSENCES
 MORE THAN 3 MEANS FAILURE DUE TO ABSENCES

7. Operational Research is the use of
advanced analytical techniques to
improve decision making.
Also known as operations research,
management science or industrial
engineering.
People with skills in OR hold jobs in
decision support, business analytics,
marketing analysis and logistics planning
– as well as jobs with OR in the title.
http://www.lancaster.ac.uk/lums/study/masters/programmes/msc-operational-research-
management-science/what-is-operational-research/

8. Operations Research is a discipline that
deals with the application of advanced
analytical methods to help make better
decisions.
Employing techniques from other
mathematical sciences, such as
mathematical modeling, statistical
analysis, and mathematical optimization,
operations research arrives at optimal or
near-optimal solutions to complex
decision-making problems.
https://www.informs.org/About-INFORMS/What-is-Operations-Research

9. Operations research overlaps with other
disciplines, particularly industrial
engineering and operations management.
It is often concerned with determining a
maximum (such as profit, performance,
or yield) or minimum (such as loss, risk,
or cost.)
https://www.informs.org/About-INFORMS/What-is-Operations-Research

10. Because of the computational and
statistical nature of most of these fields,
O.R. also has strong ties to computer
science.
Operations researchers faced with a new
problem must determine which of these
techniques are most appropriate given
the nature of the system, the goals for
improvement, and constraints on time
and computing power.
https://www.informs.org/About-INFORMS/What-is-Operations-Research

11. Successful managers use quantitative
approaches to decision making when:
The problem is complex
The problem involves many variables
There are data which describes the
decision environment

12. Successful managers use quantitative
approaches to decision making when:
There are data which describe the value
or utility of the different possible
alternatives
The goals of the decision maker or her
organization can be described in
quantitative terms
Workable models are available for these
situations

13. Industrial Engineering Era
F.W Taylor
 Converted the Industrial Engineering into a
profession
 Considered as the Father of Scientific
Management
 His shovel study is an excellent example of
the application of the scientific method to a
management problem namely, the
productivity of men shoveling ore and other
materials

14. Henry Gantt
 Best known for his work in scheduling
production
MS/OR emerged as a separate field when:
 Industrial Engineers became interested in
the overall operations in the firm
 Natural and social scientists became
interested in management problems

15. Early Operations Research
Pre World War II
F.W Lanchester
 He attempted to treat military operations
quantitatively
 He derived equations relating the outcome
of a battle to both the relative numerical
strength of the combatant and their relative
firepower

16. Thomas Alva Edison
 Studied the process of antisubmarine
warfare
 He collected statistics to be used in
analyzing maneuvers whereby surface ships
could evade and destroy submarines
 He devised a war game to be used for
simulating problems of naval maneuvers.
 He analyzed the merits of “zigzagging” as a
merchant ships countermeasure against
submarines

17.  Johannsen
 Waiting line problems were researched as early
as 1907
 Published a paper reporting his findings on
waiting times and the number of calls
 A.K Erlang
 Published his work “Solutions of Some Problems
in the Theory of Probabilities of Significance in
Automatic Telephone Exchanges”
 It contains his waiting time formula, the
fundamentals of the theory of telephone
traffic

18. In the area of Inventory Control
G.D Babcock
 Developed a model stated in the form of a
cubic equation but his technique was never
published
Ford W. Harris
 Developed the first published model on
inventory economic size in 1915

19. Probability Theory and Statistical Inference
Walter Shewhart
 Made the earliest recorded applications of
statistical inference in 1924
 He introduced the concept of quality control
charts
H.F Dodge and H.G Romig
 They developed the technique of sampling
inspection in connection with quality control
and published statistical sampling tables
which are now widely used

20. Linear Programming
Wassily Leontieff
 A Harvard Professor who developed a linear
programming model representing the entire
US economy
 Many military and industrial applications of
linear programming models have resulted
from these effort

21. Horace C. Levinson
 An astronomer who began his operations
research in the decade 1920-1930
 He applied the methods of science to the
problems of business
 Studied such problems as the relationship
between advertising and sales, and the
relationship between customer’s incomes
and home locations and types of articles
purchased

22. World War II
1939
 According to one historian, “there was a
nucleus of a British operational research
organization already in existence”
 And its contributions were quickly followed
and augmented in various important ways
 In improving the early-warning radar system
 In antiaircraft gunnery
 Antisubmarine warfare
 In civilian defense and in the conduct of
bombing raids upon Germany

23. World War II
1939
 According to one historian, “there was a
nucleus of a British operational research
organization already in existence”
 And its contributions were quickly followed
and augmented in various important ways
 In improving the early-warning radar system
 In antiaircraft gunnery
 Antisubmarine warfare
 In civilian defense and in the conduct of
bombing raids upon Germany

24. World War II
1942
 General Spaatz, Commanding General of the
Eight Air Force requests General Arnold to
send letter to all commanding generals of Air
Force command, recommending that they
include in their staffs “operations analysis
groups”
 The first Operations Analysis Team was
assigned to the Eight Bomber Command,
stationed in England

25. Post World War II
Operations research activity was
considered by American Military leaders
to be so valuable that such functions
were not discontinued after the war.
The army continued its OR functions
through the agency of the Operations
Research Office – Research Analysis Corp.
– in Chevy Chase, Maryland
 The Navy established the Operations
Evaluation Group

26. Operations Researched Today
A lot of journals and studies about
operations research are published
OR has also been part of colleges and
universities
Most institutions offer courses in MS/OR
There are about 110 textbooks in MS/OR

27. Accounting
 Forecasting Cash Flows
 Assigning Audit Teams Effectively
 Management of Accounts Receivable
 Deciding which customers to give credit to
and how much
Finance
 Building Cash Management Models
 Allocating Capitals among Various
Alternatives
 Forecasting Long Range Capital Needs

28. Marketing
 Determining the best product mix
 Effectively allocating advertising among
various media
Operations
 Balancing plant capacity with market
requirements
 Leveling a production schedule to minimize
hiring and layoffs

29. Human Resource
 Minimizing the need for temporary help
through better scheduling
 Staffing emergency rooms in hospitals to
provide best level of care
 Determining how to negotiate in a
bargaining situation

30. Opportunities
 MS/OR forces managers to be quite explicit
about their objectives, their assumptions
and their way of seeing constraints
 MS/OR quickly points out gaps in the data
required to support workable solutions to
problems
 MS/OR permits us to examine a situation,
change the condition under which decisions
are being made and examine the effects of
those changes—all without serious damage
or excessive cost

31. Opportunities
 MS/OR forces managers to be precise about
how variables in problem interact with each
other
 MS/OR makes managers consider very
carefully just what variables influence
decisions
 MS/OR lets us find solutions to a complex
problem much more quickly than if we had
to compute it by hand and often is the only
way we can solve large complex problems

32. Opportunities
 MS/OR lets us model a problem and its
solution so that future solutions can be done
by a computer, thus freeing management
time for decisions that require a more
intuitive approach

33. Shortcomings
 MS/OR approaches have to simplify the
problem or make simplifying assumptions in
order to solve it and thus produce solutions
which have limitations
 For problems that a manager must solve only
one time, constructing a complex model is
often too expensive when compared with
other less sophisticated approaches
 Sometimes MS/OR specialists become so
enamored with the model they have to built
that they forget it doesn’t represent the “real
world” in which decisions must be made

34.  Shortcomings
 Sometimes MS/OR specialists forget to counsel
managers on the limitations of models they build,
including the fact that many of them have to be
combined with judgment and intuition for effective
use
 Often managers forget to include an important
constraint or assign an incorrect value to a
constraint
 Many MS/OR solutions are so complex that they are
difficult to explain to managers in a way that builds
support and confidence
 Many “real world” problems just don’t have an
MS/OR solution

35. A good solution is one that works.
A solution that works but which
costs far more than the potential
savings from its application is not a
successful one.
A solution that fits our budget but
which falls short for accomplishing
our goal is not successful either

36. MS/OR solutions which really work ought
to meet these requirements:
 The solution should be technically
appropriate
 The solution should be reliable
 The solution should be economically viable
 The solution should be behaviorally
appropriate.

37. Many approaches require numerous, long,
complex, mathematical calculations.
Digital computers that are able to do this
calculations rapidly and effectively
become part of the MS/OR approach
Too much computer you will get
mechanistic solution, inflexible responses
and narrow decisions
Too much human you will get slow
responses, limited use of applicable data
a narrow ability to examine alternatives

38. • Humans have imagination, creative
powers, judgment and common
sense
• Human decision makers can learn
from their experiences
• Human decision makers are not
always accurate; sometimes their
behavior is inconsistent
• Human decision makers can see the
overall problem; they can then see
each sub problem as part of an
overall scheme
• Human decision makers have
flexibility; when the road signs
change, they can alter their behavior
to optimize under new situation
• Most humans have enormous
memory; they forget a lot and are
not very precise in terms of what
they remember, but they can
accommodate a wide variety of
information from bits of data to
complex thinking processes
• Computers do only what their
programmers tell them to do in the
form of instructions
• Computers can follow rules but
cannot learn deductively except in
the simplest logic situations
• Computers react consistently and
give or take electronic problems,
quite accurately
• Computers can be programmed to
follow complex, intersecting set of
rules; but even the situation changes
they will continue to follow these
rules toward faulty outcomes
• Computers have flexibility only if
someone has programmed it in;
otherwise they react identically
regardless of changed road signs
• Computers never forget; they have
enormous memory in terms of pieces
of information and they are very fast
in retrieving this information

39. Management Information System
that generates reports
Management Information System
that answers “what-if” questions

40.  Characteristics of DSS
 A system that provides management with quick access to
information which can be used in their decision making
 A system which has flexible databases
 A system which integrates MS/OR models with
information processing software
 A system which helps management deal with
unstructured problems (problems that cannot be
completely solved by an individual MS/OR model or by
using an information system report)
 A system which improves the impact of management
decisions by extending the capability of managers who
make those decisions
 A system which accommodates the intuition and
judgmental capability of management at all stages of
decision making

41. AI is an attempt to solve problems that:
 Cannot be solved by mathematics of
optimization (standard MS/OR models)
 Contains parts or components that cannot be
quantified
 Involve large knowledge bases (things that
are known)
 Involve the discovery and use of alternatives
as a part of the solution process
 Involves objectives and constraints that are
often difficult to specify in quantitative
terms

42. Expert systems are subfield of artificial
intelligence which concerns systems
that reproduce the behavior of human
experts.
Expert systems actually make decisions
as an expert would in his field of
specialization by mimicking his behavior
as he solves the problem

44. Quantitative Analysis involves the use of
mathematical equations or relationships
in solving a particular problem.
It is the scientific approach to managerial
decision making. QA uses logic, historical
data, scientific approach and research in
analyzing problems to come up with
decisions.

45. Gut feelings, intuition, and emotions
have no place in the Quantitative Analysis
approach.
QA produce more than one solution to a
problem giving managers and decision
makers several alternatives or options in
making better decisions.
Calculating IRRs, financial ratios,
breakeven analysis, forecasting, and
project planning are examples of QA.

46.  Qualitative Analysis involves the investigation
of factors in a decision-making problem that
cannot be quantified or stated in
mathematical terms.
 Factors such as the economic condition,
government legislations and regulations, new
technology and the like merit a Qualitative
Analysis approach.
 In a typical managerial decision making
scenario, both the qualitative and
quantitative approaches are used to come
up with a better decision.

48. Define the problem
In all cases, defining the problem is
the first step. The problem could be
too many stockouts, too many bad
debts, or determining the products to
produce that will result in the
maximum profit for the organization.

49. Develop a Model
After the problems have been defined,
the next step is to develop one or
more models. These models could be
inventory control models, models that
describe the debt situation in the
organization, and so on.

50. Acquire Data
 In the inventory problem, for example, factors
such as the annual demand, the ordering cost,
and the carrying cost are the input data that are
used by the model developed in the preceding
step.
 In determining the products to produce in order to
maximize profits, the input data could be things
such as the profitability for all the different
products, the amount of time that is available at
the various production departments that produce
the products and the amount of time it takes for
each product to be produced in each production
department.

51. Develop a solution
The next step is developing the
solution.
 This requires manipulation of the
model in order to determine the best
solution.

52. Test the Solution
Analyze the Results and Perform
Sensitivity Analysis
Implement the Results
 Next, the results are tested, analyzed and
implemented. In the inventory control problem,
this might result in determining and implementing
a policy to order a certain amount of inventory at
specified intervals. For the problem of
determining the best products to produce, this
might mean testing, analyzing, and implementing
a decision to produce a certain quantity of given
products.

53. Define the Problem
 All else depends on this
 Clear and concise statement required
 May be the most difficult step
 Must go beyond symptoms to causes
 Problems are related to one another
 Must identify the “right” problem
 May require specific, measurable
objectives

54. Develop the Model
 Model - a representation of a situation
 Types of Models: Physical, Logical, Scale,
 Schematic or Mathematical
 Models consist of:
1. Variables = measurable quantity subject to
change, usually unknown. i.e. new forecast
value
a. Controllable or Decision Variables = used in
decision making.
b. Uncontrollable

55. Develop the Model
 Models consist of:
2. Parameters = measurable quantity that is
inherent in the problem, usually known. i.e.
number of years in forecast
 Models must be:
 Solvable
 Realistic
 Easy to understand
 Easy to modify

56. Acquire Data
 Accurate data is essential. If input data are
not accurate, the results can be misleading
and very costly, thus “Garbage In, Garbage
Out” (GIGO).
 Data could be sourced from:
 Company reports
 Company documents
 Interviews
 On-site direct measurement
 Statistical sampling

57. Develop a Solution
 Manipulate the model, find the “best”
solution
 Solution should be:
 Practical
 Implementable
 Various methods:
 Solution of equation(s)
 Trial and Error
 Complete Enumeration
 Implementation of Algorithm

58. Test the Solution
 Must test both input data and model.
 Determine:
 Accuracy
 Completeness of input data
 Collect data from a different sources and
compare
 Check results for consistency
 Do they make sense?
 Test before analysis

59. Analyze the Results
 Understand the actions implied by the
solution.
 Determine the implications of the action.
 Conduct sensitivity analysis - change input
value or model parameter and see what
happens.
 Use sensitivity analysis to help gain
understanding of problem (as well as for
answers)

60. Implement the Results
 Incorporate the solution into the company.
 Monitor the results.
 Use the results of the model and sensitivity
analysis to help you sell the solution to
management.

61.  Deterministic models - Knowing all values used in
the model with certainty.
 Examples: Breakeven Quantity model, financial
ratio formulas, Moving Averages, regression
analysis, etc…
 Probabilistic models - Knowing the probability that
parameters in the model will take on a specific
value. These models consider risk or chance. Risk,
expressed by a probability value maybe a market
condition, say 50% chance of a good market or 50%
chance the market for a product is unfavorable.
 Examples: Decision Tree Models, Decision making
techniques under risks, etc…

62.  Accurately represent reality
 Models can translate situation and events
into variables and parameters and assigned
values.
 Help a decision maker understand the
problem
 Models are built for management problems
and encourage management input.
 Models allow “What if” questions to be
asked.
 It enforces consistency in approach following
the scientific method.

63.  Save time and money in problem solving and
decision making
 Models are less expensive and disruptive than
experimenting with real world systems
 Help communicate problems and solutions to
others
 Complex problems can be simplified and translated
into understandable terms that can be understood
by managers and decision makers.
 Models provide the only way to solve large or
complex problems in a timely fashion.
 Require specific constraints and goals thus,
solutions to problems are driven towards answering

64. In general, models can help managers
to:
 Gain deeper insight into the nature of
business relationships.
 Find better ways to assess values in such
relationships; and
 See a way of reducing, or at least
understanding uncertainty that surrounds
business plans and actions

65. Models may be expensive and time-
consuming to develop and test.
 Developing of models for some complex
problems require a lot of time and
investment like advanced and specialized
computers. Also, others require specialized
and expensive software or applications in
order to come up with models to solve it.

66. Tend to downplay the role and value of
non-quantifiable information.
 Models may not include qualitative data or
factors that are important to finding an
accurate solution to a given problem.
 This is dangerous especially involving
decisions that have financial implications.
 A good decision maker would always
consider both qualitative and quantitative
factors in coming up with a solution.

67.  Models often have assumptions that
oversimplify the variables of the real world.
 Oversimplification occurs when someone leaves out
so many important features that it is no longer
accurate. For instance, when financial reporters say
that the stock market rose or fell because of one
particular event, this is oversimplification. That
event may have played a role, but it is unlikely that
it is the only factor.
 Often it is tempting to look for a quick and easy
explanation for a problem or to see an easy solution
as the answer.
 When it comes to major issues of the day, in almost
every case, people get the wrong answer because
they oversimplify.

68.  Profit Model
 Profits = Revenue – Expenses

69.  Profit Model
 Profits = Revenue – Expenses
 Revenue = Unit Selling Price (USP) X Quantity
Sold
 Fixed Cost (FC) = Refers to investment or
any fixed asset costs.
 Variable Cost = Unit Variable Cost (UVC) X
Quantity Sold

70.  Profit Model
 Profits = Revenue – Expenses
 As an example, let us assume that,
 USP = P100
 UVC = P50
 FC = P10,000
 Then the profit function or equation or
model is:
 Profit = P100Q – P10,000 – P50Q
 Given the profit equation,
 The decision variable is the obtained profit
 The parameters are the unit selling price (USP),
fixed cost (FC), and unit variable cost (UVC)

71.  Breakeven Point Quantity Model
 Q = quantity sold
 F = fixed cost
 V = variable cost/unit
 Set Revenue = 0
 PQ - F – VQ = 0
 Then F = PQ – VQ
 and: Q = F/(P – V)
 Breakeven Quantity = F/(P-V)

72.  Define the Problem
 Conflicting Viewpoints
 There would always be conflicting viewpoints of
different departments in the organization. For
example, in a Jollibee fast-food chain, people
would always want more staff in the counter for
fast service, while managers would place only a
few staff to save money.
 It is therefore essential for an analyst to consider
different viewpoints to minimize resistance in the
acceptability and implementation of the solution.

73.  Define the Problem
 Departmental impacts
 The rule always in defining the problem is to make
it encompassing. It means inputs from all the
stakeholders must be included in the formulation
to facilitate smooth implementation of the
solution.
 Solutions become quickly outdated
 Sometimes developing the model takes time and
the moment you come up with the solution, the
problem changes. This is true in IT industry where
changes are fast and inevitable.

74.  Define the Problem
 Assumptions
 Again the problem of oversimplification persists. A
problem of long waiting lines in a Jollibee counter
may mean increasing the number of counters.
Most people state a problem in terms of solutions.
 As a rule, a ‘good’ solution to the ‘right problem’
is much better than an ‘optimal’ solution to a
‘wrong’ problem (remember the importance of
effectiveness and efficiency).

75.  Develop a Model
 Fitting the Model
 Sometimes, textbook models do not match always the
problem in real situation. Other times, parameters in
the model may not be perceived by managers as
important in decision making.
 Understanding the Model
 Some managers would not accept or even use a
complex model that they do not understand. In reality,
complex problems do require complex models.
 Tradeoff or a compromise between model accuracy and
management acceptance must be considered carefully.
Simplifying the assumptions losses some reality but
might increase understandability of the model thus
increasing acceptability.

76.  Acquire Input Data
 Accounting Data
 Accounting data are usually sensitive data, meaning,
most of the time it is not open for public research.
Another thing, accounting data is all about cash
flows and turnovers. Some cost data requirements
needed by an analyst may find it hard to obtain for
it was never collected in the first place!
 Validity of Data
 This is always a problem in the Philippines. We tend
to manipulate data according to our own purposes to
make it look ‘good and clean’. Yet, the validity of
results rest on the validity of the input data.

77.  Develop a Solution
 Complex Mathematics
 There is a false notion in us that if someone thinks
complicatedly or elaborately thinks well. That is
not the case always, mathematics always shun a
lot of people even managers.
 Only One Answer is Limiting
 QA models tend to give one solution to a problem.
One way to offset this is to come up with
alternative scenarios or sensitivities to give
managers options to choose from. In this way
other nonquantitative factors may be considered
and the cost implication of deviating from the
optimal solution is known.

78.  Implement the Solution
 Selling the solution to others
 The implementation process always involved other
things such as politics, resistance and lack of
budget allocation. Some people with strong
analytical skills have weak people skills. And this
presents a barrier in convincing people to accept
changes in the organization. Implementation
requires people skills. Analysts must work
together with the users, managers and other
stakeholders to assure support and acceptability
of the model and results.

79.  Test the Solution
 Identifying appropriate test procedures
 Analysts should also have knowledge on appropriate
testing tools for the models they use or develop.
Testing the solution is also an opportunity for the
analyst to convince the managers about the validity of
the model and solution by involving them in the
process.
 Analyze the Results
 Analyze the results using the ceteris paribus
concept
 Holding all other conditions constant. In this way the
effects of each factor can be tested and analyzed,
thus, identifying the cause and effect

80. 1. Operations
Research
2. F.W Taylor
3. Henry Gantt
4. F.W Lanchester
5. Thomas Alva Edison
6. Johannsen
7. A.K Erlang
8. Ford W. Harris
9. Walter Shewhart
10. H.F Dodge
11. H.G Romig
12. Wassily Leontieff
13. Horace C. Levinson
14. DSS
15. AI
16. Expert Systems
17. Qualitative Analysis
18. Model
19. Parameters
20. Variables