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MIDTERM Flashcards

1
Q

is the scientific approach to execute decision making

A

Operations Research

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2
Q

Another term used for OR in US

A

Management Science

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3
Q

other terms in OR

A

Industrial Engineering” (“IE”) and “Decision Science” (“DS”).

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4
Q

What are the Deterministic Models

A

Linear Programming
Network Optimization
Integer Programming
Nonlinear Programming
Inventory Models

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5
Q

What are the Stochastic Models

A

Discrete-Time Markov Chains
Continuous-Time Markov Chains
Queuing Theory
Decision Analysis
Game Theory
Inventory models
Simulation

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6
Q

assume all data are known with certainty

A

Deterministic models

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7
Q

explicitly represent uncertain data via random variables or stochastic processes.

A

Stochastic models

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8
Q

characterize / estimate system performance.

A

Stochastic models

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9
Q

it involve optimization

A

Deterministic models

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10
Q

When OR originated

A

World War II

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11
Q

The application of maths in the real world

A

Operations Research

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12
Q

give real application in OR eg in SM

A

Understanding people’s buying patterns
Determining the number of staff needed on checkouts and at what times
Calculating how many of each product to be ordered and when to be delivered

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13
Q

OR is the maths used in sport, e.g.

A

Formula One pit stop strategy
Scheduling football games
Designing a stadium

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14
Q

is a way to solve problems in which one seeks to achieve the best outcome.

A

Optimisation

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15
Q

allows you to try out different approaches by simulating events numerous times and answer the “What if…?” question.

A

Simulation

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16
Q

is used to approximate a real queuing situation or system, so the queuing behavior can be analysed mathematically.

A

Queuing theory

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17
Q

7 Steps of Problem Solving

A

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.

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18
Q

Potential Reasons for a Quantitative Analysis Approach to Decision Making

A

The problem is complex.
The problem is very important.
The problem is new.
The problem is repetitive.

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19
Q

is not a trivial step, due to the time required and the possibility of data collection errors.

A

Data preparation

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20
Q

is a series of rules, usually embodied in a computer program

A

logical Model

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21
Q

Problem must be translated from verbal, qualitative terms to logical, quantitative terms

A

Constructing a Model

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22
Q

is a collection of functional relationships by which allowable actions are delimited and evaluated.

A

mathematical model

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23
Q

are physical replicas (scalar representations) of real objects.

A

Iconic models

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24
Q

are physical in form, but do not physically resemble the object being modeled.

A

Analog models

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25
Q

represent real world problems through a system of mathematical formulas and expressions based on key assumptions, estimates, or statistical analyses.

A

Mathematical models

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26
Q

are representations of real objects or situations.

A

Models

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27
Q

Generally, experimenting with models

A

Advantages of Models

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28
Q

requires less time
is less expensive
involves less risk

A

Advantages of Models

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29
Q

A model consisting of linear relationships representing a firm’s objective and resource constraints.

A

Linear Programming (LP)

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30
Q

is a mathematical modeling technique used to determine a level of operational activity in order to achieve an objective, subject to restrictions called constraints

A

Linear Programming (LP)

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31
Q

determines the resource capacity needed to meet demand over an immediate time horizon, including units produced, workers hired, and inventory.

A

Aggregate Production Planning

32
Q

mix of different products to produce that will maximize profit or minimize cost given resources constraints such as materials, labor, budget, etc.

A

Product Mix

33
Q

logical flow of items (goods or services) from sources to destinations. For example, truckloads of goods from plants to warehouses.

A

Transportation

34
Q

flow of items from sources to destinations with intermediate points. For example shipping from plant to distribution center and then to stores.

A

Transshipment

35
Q

assigns work to limited resources, called “loading.” For example, assigning jobs or workers to different machines.

A

Assignment

36
Q

schedules regular and overtime production, plus inventory to carry over, to meet demand in future periods.

A

Multiperiod Scheduling

37
Q

determine recipe requirements. For example, how to blend different petroleum components to produce different grades of gasoline and other petroleum products.

A

Blend

38
Q

menu of food items that meets nutritional or other requirements. For example, hospital or school cafeteria menu.

A

Diet

39
Q

financial model that determines amount to invest in different alternatives given return objectives and constraints for risk, diversity, etc. For example how much to invest in new plant, facilities, or equipment.

A

Investment/capital budgeting

40
Q

compares service units of the same type – banks, hospitals, schools – based on their resources and outputs to see which units are less productive or inefficient.

A

Data Envelopment Analysis (DEA)

41
Q

shortest routes from sources to destinations. For example, the shortest highway truck route from coast to coast.

A

Shortest route

42
Q

maximizes the amount of flow from sources to destinations. For example, the flow of work-in-process through an assembly operation.

A

Maximal flow

43
Q

determines patterns to cut sheet items to minimize waste. For example, cutting lumber, film, cloth, glass, etc.

A

Trim-Loss

44
Q

selects facility location based on constraints such as fixed, operating, and shipping costs, production capacity, etc.

A

Facility location

45
Q

selection of facilities that can service a set of other facilities. For example, the selection of distribution hubs that will be able to deliver packages to a set of cities.

A

Set Covering

46
Q

mathematical symbols representing levels of activity of a firm.

A

Decision variables

47
Q

a linear mathematical relationship describing an objective of the firm, in terms of decision variables - this function is to be maximized or minimized.

A

Objective function

48
Q

requirements or restrictions placed on the firm by the operating environment, stated in linear relationships of the decision variables.

A

Constraints

49
Q

refers to a structured approach or framework used to organize tasks, steps, or events in a specific order to achieve a desired outcome.

A

sequencing model

50
Q

It involves arranging elements or actions sequentially, where each step typically depends on the completion of
the previous step.

A

sequencing model

51
Q

These models help break down complex activities into
manageable steps, making it easier to follow and execute the necessary actions in the correct order.

A

SEQUENCING MODEL

52
Q

The selection of the appropriate order in which a number of jobs (customers) are to be performed (served) is called

A

sequencing

53
Q

play a crucial role in ensuring efficiency, accuracy, and the successful completion of tasks or processes.

A

sequencing models

54
Q

All task should be done on respective machine

A

NO PASSING RULE

55
Q

All task should be done on respective machine

A

FIRST COME FIRST SERVED

56
Q

Compute the ratio of processing time of the job and remaining time until the due date. Schedule the job with the largest CR value next.

A

CRITICAL RATIO

57
Q

Jobs are sequenced according to their due dates

A

EARLIEST DUE DATE

58
Q

The shortest processing time are scheduled first.

A

SHORTEST PROCESSING TIME

59
Q

T OR F
OR is a profession where initiative, creativity and enthusiasm are not equally as important as technical ability.

A

F

60
Q

T OR F
OR teams are involved in projects which involve a wide range of business skills and have dealings with anyone from shop floor to boardroom.

A

T

61
Q

Information systems specialists might be needed in this

A

Data Preparation

62
Q

is a series of rules, usually embodied in a computer program

A

logical model

63
Q

the progressive deterioration of operational efficiency of an item

A

Gradual failure

64
Q

a failure that occurs after some period of desired service rather than deteriorates while in service.

A

sudden failure

65
Q

probability of failure of an item increases with an increase in its life. Ex; light bulbs, tubes

A

Progressive failure

66
Q

probability of failure of an item at the beginning of the life of an item is more but then as time passes the chances of failure becomes less.

A

Retrogressive failure

67
Q

constant probability of failure associated with the item due to random causes not related to age.

A

Random failure

68
Q

Frequent personnel departures, particularly those due to mortality, can disrupt operations and hinder knowledge transfer.

A

High Staff Turnover

69
Q

Mortality is unpredictable, making it difficult to plan staff replacements.

A

Unforeseen Vacancies

70
Q

Experienced staff depart with important information and skills.

A

Skill Gap and Knowledge Loss

71
Q

Employee recruited into a position ‘up the ladder’ from the current job position.

A

Promotion

72
Q

a replacement policy that replace items one by one when they fail.

A

I N D I V I D U A L R E P L A C E M E N T

73
Q

a replacement policy that replace items in clusters or groups when they fail.

A

G R O U P R E P L A C E M E N T

74
Q

A statistical model that predicts the probability of an employee transitioning between different employment states

A

Markov Model

75
Q

Focuses on predicting staff departures due to retirement based on age demographics and historical data.

A

Age-Replacement Model

76
Q

Considers the specific skills and knowledge required for different roles to identify potential skill gaps when employees leave.

A

Skill-Based Model

OR (2 decks)

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