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The programs described in this site, must be installed as add-ins
for Microsoft Excel. This article explains how to install add-ins
and what to do in case of trouble. We also show how to get
access to the source code. This section is in the ORMM Section. |
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When called, this add-in lists all the Teach OR add-ins
that are in the same directory as the Add Teach add-in.
Add-ins are installed and removed with a simple click of a button.
The add-in also loads demonstration files that illustrate the
add-ins. To use this feature these files must be stored in a
the same directory as the add-ins. See the instructions for details. |
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This add-in performs a variety of procedures to evaluate investment
alternatives. Included are procedures to define projects and compute
measures of effectiveness such as present worth, annual worth
and internal rate of return. Functions are provided to compute
the traditional time value of money factors. Projects may be defined
with tax and inflation considerations. Cash flows are presented
in either tabular or graphical displays. |
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This add-in considers a portfolio of investment securities. Markowitz
Portfolio analysis determines an optimum mix of financial
securities considering both security returns and risk. Risk is
measured by the statistical variance of the portfolio. The analysis
uses historical return data to determine the expected return
for each security and the covariance matrix showing the dependence
between securities. |
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This add-in uses optimization for capital budgeting problems.
Capital Budgeting selects a portfolio from a set of
candidate projects. A project is described by its initial investment,
annual return, salvage value and life. Models may include statistical
variance as a measure of risk. Multiperiod models accept data
for individual periods in a time horizon. The add-in constructs
efficient frontiers. |
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The Process
Flow Add-in provides a tool to analyze and design manufacturing
systems. Four principal activities are supported: process definition,
economic analysis, resource requirements analysis and product
mix decisions. The add-in constructs decision models
using the Math Programming add-in. That add-in must be installed
in order to construct linear and integer models. |
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This add-in
accepts as data: a list of departments, physical size of departments,
part flows between departments and the size of a proposed plant.
The program uses the CRAFT procedure in an attempt to find the
layout of departments that minimizes the distance over which parts
must flow. A graphical presentation of the layout allows the designer
to experiment with alternatives. |
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The add-in implements several forecasting methods for data series
including: moving average, exponential smoothing, regression
and double exponential smoothing. The add-in constructs a
form that holds the data and uses functions to compute forecasts
and forecast errors. Several data series can be analyzed on
a single worksheet page. An option allows comparison of several
methods for a single time series. A simulation option creates
data simulated with the Monte Carlo technique. Equity stock
investment analysis provides an example. |
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The subject of inventory control is a major consideration in many
situations. Questions must be constantly answered as to when and
how much raw material should be ordered, when a production order
should be released to the plant, what level of safety stock should
be maintained at a retail outlet, or how in-process inventory
is to be maintained in a production process. This add-in provides
answers to some of these questions using the results of inventory
theory. |
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This unit describes inventory control games based on the stochastic
inventory models. The player makes decisions on when and how
much to order for an inventory carrying a single product. Two
games are provided. The first allows shortages to be backordered.
The second assumes that shortages result in lost sales. The
goal of each game is to minimize the total cost of running
the inventory for one year of operations. |
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This workbook contains a game that involves the release of raw
materials to a line with four production stages. The game is
similar to the matches game played by the boy scouts
in Goldratt's book The Goal. The purpose to illustrate
the effect of statistical variability on the rate of production
of the line and the build-up of WIP. |
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Materials
Requirement Planning (MRP) is a scheduling procedure for production
processes that have several levels of production. Given information
describing the production requirements of the several finished
goods of the system, the structure of the production system, the
current inventories for each operation and the lot sizing procedure
for each , MRP determines a schedule for the operations and raw
material purchases. |
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The Project Management add-in implements
the Critical Path Method (CPM) and Project Evaluation and Review
Technique (PERT) for scheduling the activities in a project.
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The purpose of the Estimate add-in is
to estimate the capital cost and Life-Cycle
cost for a project. Projects are described by a Work Breakdown
Schedule or a Cost Breakdown Schedule. Costs
and revenues may be point estimates or described by probability
distributions.
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The Routing add-in builds models and solves the vehicle
routing problem for several vehicles visiting several delivery
sites. It is to be used with the Optimal Sequence add-in
that provides search heuristics for finding solutions. A typical
application of the Routing add-in is to
plan routes for a delivery company that serves a small geographic
area. The company schedules deliveries for each day to several
sites in the area, and the company has one or more delivery
vehicles. Our goal is to assign vehicles to the sites and sequence
the deliveries so that all deliveries are completed during
the day. Vehicles have resource limitations and deliveries
may have time windows.
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The Routing add-in builds models and solves the vehicle
routing problem for several vehicles visiting several delivery
sites. It is to be used with the Optimal Sequence add-in
that provides search heuristics for finding solutions. A typical
application of the Routing add-in is to plan routes
for a delivery company that serves a small geographic area.
The company schedules deliveries for each day to several sites
in the area, and the company has one or more delivery vehicles.
Our goal is to assign vehicles to the sites and sequence the
deliveries so that all deliveries are completed during the
day. Vehicles have resource limitations and deliveries may
have time windows.
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Many organizations have multiple
subdivisions, all carrying out the same general purpose. Examples
are a bank with branches, armies with combat divisions, combat
divisions with regiments, school districts with schools and
many others. Comparison between the subdivisions is difficult
when there are multiple inputs and outputs of the subdivisions.
Data
Envelopment Analysis (DEA) provides a theory
and analysis method to compare subdivisions objectively. The
DEA add-in accepts data describing the inputs and outputs,
performs the basic computations of the method, and presents
the results.
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