Question 1

In comparing different policies using simulation, one should use the same set of random numbers whenever possible.

Accepted Answer

A) True 
 B)False

Question 2

Computer-generated random numbers are normally distributed.

Accepted Answer

A) True 
 B)False

Question 3

Simulation

Accepted Answer

A)  does not guarantee optimality. 
B)  is flexible and does not require the assumptions of theoretical models. 
C)  allows testing of the system without affecting the real system. 
D)  All of the alternatives are correct. 
A)  does not guarantee optimality. 
B)  is flexible and does not require the assumptions of theoretical models. 
C)  allows testing of the system without affecting the real system. 
D)  All of the alternatives are correct.

In comparing different policies using simulation, one should use the same set of random numbers whenever possible.

Computer-generated random numbers are normally distributed.

Simulation

Introduction

An Introduction to Linear Programming

Linear Programming: Sensitivity Analysis and Interpretation of Solution

Linear Programming Applications in Marketing, Finance, and Operations Management

Advanced Linear Programming Applications

Distribution and Network Models

Integer Linear Programming

Nonlinear Optimization Models

Project Scheduling: Pertcpm

Inventory Models

Waiting Line Models

Decision Analysis

Multicriteria Decisions

Forecasting

Markov Processes

Filters

Exam 12: Simulation

In comparing different policies using simulation, one should use the same set of random numbers whenever possible.

Computer-generated random numbers are normally distributed.

Simulation

Introduction

An Introduction to Linear Programming

Linear Programming: Sensitivity Analysis and Interpretation of Solution

Linear Programming Applications in Marketing, Finance, and Operations Management

Advanced Linear Programming Applications

Distribution and Network Models

Integer Linear Programming

Nonlinear Optimization Models

Project Scheduling: Pertcpm

Inventory Models

Waiting Line Models

Decision Analysis

Multicriteria Decisions

Forecasting

Markov Processes

Filters