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Excel Add-ins 2001


These are Excel add-in files (.xla). They are compressed and stored at this site with the (.zip) suffix. Download by clicking on the add-in name. The files may be expanded with the free Aladdin Expander.

Links are provided to the instructions for each add-in. Add-ins are not used like regular Excel workbook files. They must be installed through the add-in dialog on the tools menu or through the Add-in_Control. Review the General Instructions for loading add-ins.

The add-ins have been modified from earlier versions, especially those dealing with Mathematical Programming. If you have trouble with these add-ins, try the older add-ins that have a link at the bottom of this page. Of course, let the authors know about any bugs so they can be fixed.

Please note. Some major changes regarding the menu structure were incorporated on Feb. 5, 2001. The menu title for the add-ins has been changed to OR_MM (Operations Research Models and Methods). A new control add-in is used called Add ORMM (add_ormm). This item appears on the OR_MM menu rather than the tools menu. The add-ins now appear on the OR_MM menu in a prespecified order. If you use the new versions, download the whole set. The Math Programming modeling and solver add-ins specifically use the new format, so old versions and new versions should not be mixed. The documentation has not been adjusted to reflect these changes. The Teach add-ins have not been changed and still appear on the OR_MS menu. The documentation and Teach add-ins will be changed in the near future.


File Name
Date Modified
Add-in Name
General Instructions for Add-ins


OR_MM Add-in Control
OR_MM stands for Operations Research Models and Methods. This add-in allows the user to install and remove add-ins without using the Add-in command on the Tools menu. When called, the add-in lists all the add-ins that are in the same directory as the OR_MM add-in. Load the OR_MM add-in through the Add-in dialog on the Tools menu. If you do not intentionally remove it, the add-in menu will appear on all worksheets, making the other add-ins immediately available.


This add-in provides demonstrations of the other add-ins in the package, excluding the Teach add-ins. To run a demonstration, both the Demo add-in and the add-in being demonstrated must be installed. This version of the Demo add-in only works with the 2001 add-ins referenced on this page.

Math Programming Models
The Mathematical Programming Add-in constructs linear programming, integer programming, nonlinear programming, network and transportation models that can be solved using the Excel Solver Add-in or one of the solution add-ins provided in this collection.

LP/IP Solver
This add-in provides algorithms to solve Linear and Integer Programming problems. It can be used instead of the Excel solver for the Linear Programming models created by the Mathematical Programming add-in. The mp_models add-in must be installed to use this add-in. Note that the file name for this add-in has been changed.

Network Solver
A network solution algorithm is provided by this add-in. Network algorithms are generally faster than linear programming algorithms for solving problems that can be modeled entirely as networks. The problem solves generalized networks and can handle models with integer restrictions. The mp_models add-in must be installed to use this add-in. Note that the file name for this add-in has been changed.

Random Variables
The Random Variables Add-in performs computations associated with probability distributions. Random variables with any of 16 different named distributions may be defined. Functions compute probabilities of events, inverse probabilities and moments. Complex probability problems may be solved through simulation.

Stochastic Models
This is a new add-in to build models for stochastic processes. The models are symbolic in that formulas are entered for transitions, transition rates, transition probabilities and other relevant components of a stochastic process model. The Markov Chain or Markov Process model is constructed automatically. The models constructed are analyzed using the Stochastic Analysis described below.


Stochastic Analysis
This add-in replaces the Markov Chain and Markov Process add-ins. It performs a wide range of computations associated with Markov Chains and Markov Processes including: economic analysis, steady state analysis, computation of n-step probabilities, simulation, computation of first passage probabilities and computation of absorbing state probabilities.

The Queueing Add-in computes steady-state measures associated with Poisson queueing models, non Markovian queues, and networks of queues. The program also simulates multiple channel queues using two methods, a discrete next-event simulation and an entity simulation. The documentation has not been updated to incorporate the 7/1/01 changes.

Decision Analysis
This is a completely revised version of the Decision Analysis add-in formerly at this site. It constructs and analyzes decision trees. A tree is defined by decision, chance and terminal nodes. The tree is described by a network of nodes and arcs, and can be displayed graphically. Bayes' analysis is available. Utility functions represent the decision maker's attitude toward risk.


Teach Linear Programming
The add-in teaches three solution algorithms for linear programming, the Tableau Simplex method, the Revised Simplex method and the Interior Point Method.

Teach Transportation
This add-in is concerned with solving the Transportation problem with the primal simplex method. The program works on problems with up to 10 suppliers or demands.

Teach Network
This add-in considers the primal simplex method for network flow programming problems. Included are algorithms for the pure and the generalized minimum cost flow problems.

Teach Integer Programming
Two methods for solving integer programming problems are shown, branch and bound and Gomery cuts.

Teach Nonlinear Programming
This add-in demonstrates procedures used to solve unconstrained nonlinear programming problems. Two procedures are available, gradient search and conjugate gradient search. The convexity of the objective function at specified points is determined,


Teach Dynamic Programming
The add-in provides a structure for the various model components for dynamic programming. The student provides the various components using Excel functions. The optimization algorithms use recursion and reaching to find optimum solutions.

This is the complete collection of add-ins in a single zip file. It is 2.5 mb in zipped form. The achive also includes the manufacturing system add-ins.


Link to the older versions of the Add-ins
Older versions of the add-ins described above can be found by clicking the link at the right. If you have trouble with the new algorithms on this page, you might try the older ones. Most of them have been extensively tested.


Link to the original OR Add-ins site
The original site includes links to manufacturing system add-ins for Process Flow Analysis, Facility Layout and Materials Requirements Planning.


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