# All Educational Materials for MGOC10H3 at University of Toronto Scarborough (UTSC)

## Popular Study Guides

UTSCMGOC10H3Vinh QuanFall

## MGOC10H3- Midterm Exam Guide - Comprehensive Notes for the exam ( 22 pages long!)

OC31292222 Page
5 Oct 2017
0
Chapter 2 solving lp model graphical method & sensitivity analysis of constraints. Given optimal solution, can compute the slack for any constraints. S
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UTSCMGOC10H3Vinh QuanSummer

## MGOC10H3 Quiz: MGOC10H3 Quiz: assignment 2 solution

OC18106952 Page
16 Jan 2019
0
Mgoc10 analysis for decision making l01/l02 - assignment #2 solution. B = thousands of \$ invest in bond fund, s = thousands of \$ invest in stock fund.
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UTSCMGOC10H3Vinh QuanSummer

## MGOC10H3 Lecture 8: Scoring models and AHP

OC121666713 Page
24 Jun 2019
0
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UTSCMGOC10H3Vinh QuanSummer

## MGOC10H3 Midterm: MGOC10-MT-SOL-post

OC3325233 Page
19 Aug 2016
28
Mgoc10 analysis for decision making l01/l02 midterm solution. Q1. (a) a = 0, b = 10000, c = 6000, d = 0 (b) 6 6 range coeff x5 6 + so 0 range coeff x5
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UTSCMGOC10H3Vinh QuanSummer

## MGOC10H3 Lecture 10: Waiting line models 1

OC121666710 Page
24 Jun 2019
0
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UTSCMGOC10H3Vinh QuanSummer

## MGOC10H3 Lecture 6: Applications involving 0-1 variables

OC121666710 Page
24 Jun 2019
0
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UTSCMGOC10H3Vinh QuanSummer

## MGOC10H3 Lecture 9: Decision trees

OC12166677 Page
24 Jun 2019
0
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UTSCMGOC10H3Vinh QuanSummer

## MGOC10H3 Study Guide - Midterm Guide: Expected Return, Feasible Region, Graph Paper

OC12166679 Page
24 Jun 2019
0
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UTSCMGOC10H3Vinh QuanSummer

## MGOC10H3 Study Guide - Final Guide: Feasible Region, Radiography, Perfect Information

OC121666713 Page
24 Jun 2019
0
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UTSCMGOC10H3Vinh QuanSummer

## MGOC10H3 Study Guide - Midterm Guide: White Meat, Cutlet

OC18106953 Page
16 Jan 2019
0
Mgoc10 analysis for decision making midterm solution. Q1. (a) a = 0, b = 0, c = 37. 5, d = 2. 65625 (b) 7 3. 125 coeff c 7 + . 875 3. 875 coeff c 7. 87
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## Trending

Frequently-seen exam questions from 2014 - 2018.
UTSCMGOC10H3Vinh QuanSummer

## MGOC10H3 Lecture 8: Scoring models and AHP

OC121666713 Page
24 Jun 2019
0
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UTSCMGOC10H3Vinh QuanSummer

## MGOC10H3 Lecture 10: Waiting line models 1

OC121666710 Page
24 Jun 2019
0
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UTSCMGOC10H3Vinh QuanSummer

## MGOC10H3 Lecture 6: Applications involving 0-1 variables

OC121666710 Page
24 Jun 2019
0
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UTSCMGOC10H3Vinh QuanSummer

## MGOC10H3 Lecture 9: Decision trees

OC12166677 Page
24 Jun 2019
0
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UTSCMGOC10H3Vinh QuanSummer

## MGOC10H3 Study Guide - Midterm Guide: Expected Return, Feasible Region, Graph Paper

OC12166679 Page
24 Jun 2019
0
View Document
UTSCMGOC10H3Vinh QuanSummer

## MGOC10H3 Study Guide - Final Guide: Feasible Region, Radiography, Perfect Information

OC121666713 Page
24 Jun 2019
0
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UTSCMGOC10H3Vinh QuanSummer

## MGOC10H3 Lecture 11: Waiting line models 2

OC12166678 Page
24 Jun 2019
0
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UTSCMGOC10H3Vinh QuanSummer

## MGOC10H3 Lecture 7: Goal Programming

OC12166678 Page
24 Jun 2019
0
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UTSCMGOC10H3Vinh QuanSummer

## MGOC10H3 Quiz: MGOC10H3 Quiz: assignment 2 solution

OC18106952 Page
16 Jan 2019
0
Mgoc10 analysis for decision making l01/l02 - assignment #2 solution. B = thousands of \$ invest in bond fund, s = thousands of \$ invest in stock fund.
View Document
UTSCMGOC10H3Vinh QuanSummer

## MGOC10H3 Study Guide - Midterm Guide: White Meat, Cutlet

OC18106953 Page
16 Jan 2019
0
Mgoc10 analysis for decision making midterm solution. Q1. (a) a = 0, b = 0, c = 37. 5, d = 2. 65625 (b) 7 3. 125 coeff c 7 + . 875 3. 875 coeff c 7. 87
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## Popular Professors

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## Popular Class Notes

UTSCMGOC10H3Igor AverbakhWinter

## MGOC10H3 Lecture Notes - Lecture 1: R2-D2, Optical Fiber

OC4196437 Page
17 Feb 2016
24
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UTSCMGOC10H3Douglas KongFall

## MGOC10H3 Lecture Notes - Crowdsourcing

OC3068151 Page
6 Oct 2014
81
Musicjuice. net: the challenge of starting up a new internet venture. Europe, more precisely netherlands and was at this time still unknown in north am
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UTSCMGOC10H3Vinh QuanSummer

## MGOC10H3 Lecture Notes - Lecture 3: Gamble-Skogmo, Graph Paper, Perfect Information

OC4118504 Page
26 Feb 2015
216
The government is attempting to determine whether immigrants should be tested for a disease. Let"s assume that the decision will be made based on a fin
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UTSCMGOC10H3Igor AverbakhWinter

## MGOC10H3 Lecture Notes - Lecture 9: Contin, Unita, Nissan Sd Engine

OC4678011 Page
23 May 2016
24
The issue at hand was to determine the optimal amount of dry and wet cranberries to harvest through the development of a linear programming model. A br
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UTSCMGOC10H3Igor AverbakhWinter

## MGOC10H3 Lecture Notes - Lecture 6: Susan Boyle, Microsoft Excel, Gerontology Research Group

OC4678013 Page
23 May 2016
37
The issue at hand was to determine the optimal amount of dry and wet cranberries to harvest through the development of a linear programming model. A br
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UTSCMGOC10H3V.QuenSummer

## MGOC10H3 Lecture Notes - Lecture 9: Merv, Horse Length, Stb

OC447386 Page
24 Aug 2012
50
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UTSCMGOC10H3Vinh QuanSpring

## MGOC10H3 Lecture Notes - Lecture 1: Dynamic Programming, Standard Deviation, Goal Programming

OC1678296 Page
26 May 2017
0
Chapter 1 introduction to quantitative decision making. Outline: body of knowledge, problem solving and decision making, qualitative vs quantitative ap
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UTSCMGOC10H3Igor AverbakhWinter

## MGOC10H3 Lecture Notes - Lecture 1: X 2000, Variable Cost, Fixed Cost

OC4196432 Page
17 Feb 2016
18
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UTSCMGOC10H3V.QuenSummer

## MGOC10H3 Lecture Notes - Lecture 8: Ontario Libertarian Party

OC447385 Page
24 Aug 2012
43
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UTSCMGOC10H3Igor AverbakhWinter

OC4196434 Page
7 Mar 2016
40
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## Popular Textbook Notes

UTSCMGOC10H3Vinh QuanFall

## MGOC10H3 Chapter Notes - Chapter 2: Slack Variable, Feasible Region

OC26022 Page
23 Sep 2012
50
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UTSCMGOC10H3Vinh QuanFall

## Chapter 1.docx

OC26022 Page
23 Sep 2012
41
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UTSCMGOC10H3Vinh QuanFall

## MGOC10H3 Chapter Notes - Chapter 3: Sensitivity Analysis, Sunk Costs

OC26021 Page
23 Sep 2012
32
Chapter 3 linear programming: sensitivity analysis and interpretation of. 3. 2 graphical sensitivity analysis for linear programming problems with two
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## Most Popular

UTSCMGOC10H3Igor AverbakhWinter

## MGOC10H3 Study Guide - Midterm Guide: Sport Coat, Variable Cost, Ice Wine

OC13056115 Page
5 Feb 2016
106
Decision making process: define the problem, identify the alternatives, determine the criteria, evaluate the alternatives, choose an alternative. Margi
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UTSCMGOC10H3Igor AverbakhWinter

## MGOC10H3 Lecture Notes - Lecture 1: R2-D2, Optical Fiber

OC4196437 Page
17 Feb 2016
24
View Document
UTSCMGOC10H3Vinh QuanSummer

## MGOC10H3 Midterm: MGOC10-MT-SOL-post

OC3325233 Page
19 Aug 2016
28
Mgoc10 analysis for decision making l01/l02 midterm solution. Q1. (a) a = 0, b = 10000, c = 6000, d = 0 (b) 6 6 range coeff x5 6 + so 0 range coeff x5
View Document
UTSCMGOC10H3Igor AverbakhWinter

## MGOC10H3 Study Guide - Final Guide: Expected Value Of Perfect Information, Joule, Minimax

OC1305611 Page
11 Apr 2016
68
Min cijxij (total transportation cost) i j s. t. Xij = dj for each destination j (demand constraints) i xij > 0 (nonnegativity constraints) for all i a
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UTSCMGOC10H3Douglas KongFall

## MGOC10H3 Lecture Notes - Crowdsourcing

OC3068151 Page
6 Oct 2014
81
Musicjuice. net: the challenge of starting up a new internet venture. Europe, more precisely netherlands and was at this time still unknown in north am
View Document
UTSCMGOC10H3Vinh QuanFall

## MGOC10H3- Midterm Exam Guide - Comprehensive Notes for the exam ( 22 pages long!)

OC31292222 Page
5 Oct 2017
0
Chapter 2 solving lp model graphical method & sensitivity analysis of constraints. Given optimal solution, can compute the slack for any constraints. S
View Document
UTSCMGOC10H3Vinh QuanFall

## [MGOC10] ExamAidSheet.doc

OC402213 Page
11 Dec 2013
262
View Document
UTSCMGOC10H3Vinh QuanSummer

## MGOC10H3 Lecture Notes - Lecture 3: Gamble-Skogmo, Graph Paper, Perfect Information

OC4118504 Page
26 Feb 2015
216
The government is attempting to determine whether immigrants should be tested for a disease. Let"s assume that the decision will be made based on a fin
View Document
UTSCMGOC10H3Igor AverbakhWinter

## MGOC10H3 Lecture Notes - Lecture 9: Contin, Unita, Nissan Sd Engine

OC4678011 Page
23 May 2016
24
The issue at hand was to determine the optimal amount of dry and wet cranberries to harvest through the development of a linear programming model. A br
View Document
UTSCMGOC10H3Igor AverbakhWinter

## MGOC10H3 Lecture Notes - Lecture 6: Susan Boyle, Microsoft Excel, Gerontology Research Group

OC4678013 Page
23 May 2016
37
The issue at hand was to determine the optimal amount of dry and wet cranberries to harvest through the development of a linear programming model. A br
View Document

## Most Recent

UTSCMGOC10H3Vinh QuanSummer

## MGOC10H3 Study Guide - Final Guide: Feasible Region, Radiography, Perfect Information

OC121666713 Page
24 Jun 2019
0
View Document
UTSCMGOC10H3Vinh QuanSummer

## MGOC10H3 Study Guide - Midterm Guide: Expected Return, Feasible Region, Graph Paper

OC12166679 Page
24 Jun 2019
0
View Document
UTSCMGOC10H3Vinh QuanSummer

## MGOC10H3 Lecture 11: Waiting line models 2

OC12166678 Page
24 Jun 2019
0
View Document
UTSCMGOC10H3Vinh QuanSummer

## MGOC10H3 Lecture 10: Waiting line models 1

OC121666710 Page
24 Jun 2019
0
View Document
UTSCMGOC10H3Vinh QuanSummer

## MGOC10H3 Lecture 7: Goal Programming

OC12166678 Page
24 Jun 2019
0
View Document
UTSCMGOC10H3Vinh QuanSummer

## MGOC10H3 Lecture 9: Decision trees

OC12166677 Page
24 Jun 2019
0
View Document
UTSCMGOC10H3Vinh QuanSummer

## MGOC10H3 Lecture 8: Scoring models and AHP

OC121666713 Page
24 Jun 2019
0
View Document
UTSCMGOC10H3Vinh QuanSummer

## MGOC10H3 Lecture 6: Applications involving 0-1 variables

OC121666710 Page
24 Jun 2019
0
View Document
UTSCMGOC10H3Vinh QuanSummer

## MGOC10H3 Study Guide - Midterm Guide: White Meat, Cutlet

OC18106953 Page
16 Jan 2019
0
Mgoc10 analysis for decision making midterm solution. Q1. (a) a = 0, b = 0, c = 37. 5, d = 2. 65625 (b) 7 3. 125 coeff c 7 + . 875 3. 875 coeff c 7. 87
View Document
UTSCMGOC10H3Vinh QuanSummer

## MGOC10H3 Quiz: MGOC10H3 Quiz: assignment 2 solution

OC18106952 Page
16 Jan 2019
0
Mgoc10 analysis for decision making l01/l02 - assignment #2 solution. B = thousands of \$ invest in bond fund, s = thousands of \$ invest in stock fund.
View Document

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