MIDTERM Flashcards
is the scientific approach to execute decision making
Operations Research
Another term used for OR in US
Management Science
other terms in OR
Industrial Engineering” (“IE”) and “Decision Science” (“DS”).
What are the Deterministic Models
Linear Programming
Network Optimization
Integer Programming
Nonlinear Programming
Inventory Models
What are the Stochastic Models
Discrete-Time Markov Chains
Continuous-Time Markov Chains
Queuing Theory
Decision Analysis
Game Theory
Inventory models
Simulation
assume all data are known with certainty
Deterministic models
explicitly represent uncertain data via random variables or stochastic processes.
Stochastic models
characterize / estimate system performance.
Stochastic models
it involve optimization
Deterministic models
When OR originated
World War II
The application of maths in the real world
Operations Research
give real application in OR eg in SM
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
OR is the maths used in sport, e.g.
Formula One pit stop strategy
Scheduling football games
Designing a stadium
is a way to solve problems in which one seeks to achieve the best outcome.
Optimisation
allows you to try out different approaches by simulating events numerous times and answer the “What if…?” question.
Simulation
is used to approximate a real queuing situation or system, so the queuing behavior can be analysed mathematically.
Queuing theory
7 Steps of Problem Solving
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.
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.
is not a trivial step, due to the time required and the possibility of data collection errors.
Data preparation
is a series of rules, usually embodied in a computer program
logical Model
Problem must be translated from verbal, qualitative terms to logical, quantitative terms
Constructing a Model
is a collection of functional relationships by which allowable actions are delimited and evaluated.
mathematical model
are physical replicas (scalar representations) of real objects.
Iconic models
are physical in form, but do not physically resemble the object being modeled.
Analog models
represent real world problems through a system of mathematical formulas and expressions based on key assumptions, estimates, or statistical analyses.
Mathematical models
are representations of real objects or situations.
Models
Generally, experimenting with models
Advantages of Models
requires less time
is less expensive
involves less risk
Advantages of Models
A model consisting of linear relationships representing a firm’s objective and resource constraints.
Linear Programming (LP)
is a mathematical modeling technique used to determine a level of operational activity in order to achieve an objective, subject to restrictions called constraints
Linear Programming (LP)
determines the resource capacity needed to meet demand over an immediate time horizon, including units produced, workers hired, and inventory.
Aggregate Production Planning
mix of different products to produce that will maximize profit or minimize cost given resources constraints such as materials, labor, budget, etc.
Product Mix
logical flow of items (goods or services) from sources to destinations. For example, truckloads of goods from plants to warehouses.
Transportation
flow of items from sources to destinations with intermediate points. For example shipping from plant to distribution center and then to stores.
Transshipment
assigns work to limited resources, called “loading.” For example, assigning jobs or workers to different machines.
Assignment
schedules regular and overtime production, plus inventory to carry over, to meet demand in future periods.
Multiperiod Scheduling
determine recipe requirements. For example, how to blend different petroleum components to produce different grades of gasoline and other petroleum products.
Blend
menu of food items that meets nutritional or other requirements. For example, hospital or school cafeteria menu.
Diet
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.
Investment/capital budgeting
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.
Data Envelopment Analysis (DEA)
shortest routes from sources to destinations. For example, the shortest highway truck route from coast to coast.
Shortest route
maximizes the amount of flow from sources to destinations. For example, the flow of work-in-process through an assembly operation.
Maximal flow
determines patterns to cut sheet items to minimize waste. For example, cutting lumber, film, cloth, glass, etc.
Trim-Loss
selects facility location based on constraints such as fixed, operating, and shipping costs, production capacity, etc.
Facility location
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.
Set Covering
mathematical symbols representing levels of activity of a firm.
Decision variables
a linear mathematical relationship describing an objective of the firm, in terms of decision variables - this function is to be maximized or minimized.
Objective function
requirements or restrictions placed on the firm by the operating environment, stated in linear relationships of the decision variables.
Constraints
refers to a structured approach or framework used to organize tasks, steps, or events in a specific order to achieve a desired outcome.
sequencing model
It involves arranging elements or actions sequentially, where each step typically depends on the completion of
the previous step.
sequencing model
These models help break down complex activities into
manageable steps, making it easier to follow and execute the necessary actions in the correct order.
SEQUENCING MODEL
The selection of the appropriate order in which a number of jobs (customers) are to be performed (served) is called
sequencing
play a crucial role in ensuring efficiency, accuracy, and the successful completion of tasks or processes.
sequencing models
All task should be done on respective machine
NO PASSING RULE
All task should be done on respective machine
FIRST COME FIRST SERVED
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.
CRITICAL RATIO
Jobs are sequenced according to their due dates
EARLIEST DUE DATE
The shortest processing time are scheduled first.
SHORTEST PROCESSING TIME
T OR F
OR is a profession where initiative, creativity and enthusiasm are not equally as important as technical ability.
F
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.
T
Information systems specialists might be needed in this
Data Preparation
is a series of rules, usually embodied in a computer program
logical model
the progressive deterioration of operational efficiency of an item
Gradual failure
a failure that occurs after some period of desired service rather than deteriorates while in service.
sudden failure
probability of failure of an item increases with an increase in its life. Ex; light bulbs, tubes
Progressive failure
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.
Retrogressive failure
constant probability of failure associated with the item due to random causes not related to age.
Random failure
Frequent personnel departures, particularly those due to mortality, can disrupt operations and hinder knowledge transfer.
High Staff Turnover
Mortality is unpredictable, making it difficult to plan staff replacements.
Unforeseen Vacancies
Experienced staff depart with important information and skills.
Skill Gap and Knowledge Loss
Employee recruited into a position ‘up the ladder’ from the current job position.
Promotion
a replacement policy that replace items one by one when they fail.
I N D I V I D U A L R E P L A C E M E N T
a replacement policy that replace items in clusters or groups when they fail.
G R O U P R E P L A C E M E N T
A statistical model that predicts the probability of an employee transitioning between different employment states
Markov Model
Focuses on predicting staff departures due to retirement based on age demographics and historical data.
Age-Replacement Model
Considers the specific skills and knowledge required for different roles to identify potential skill gaps when employees leave.
Skill-Based Model
