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Operations Research Models and Methods
Computation Section
Subunit Functions
 - Algorithm

The examples given to this point are dynamic. After the values of the random variables are changed, the expressions computing the function values and feasibility states are automatically evaluated. The add-in controls when the worksheet is recalculated, but the Excel program manages the details of the calculation. Some OR models, such as mathematical programming, require algorithms to determine values. Unless the algorithms are implemented in user-defined functions, the cells holding the results of algorithms are not dynamic. A button on the worksheet or a command on a menu must be clicked to initiate the algorithm. The add-in implementing the algorithm places the algorithm results on the worksheet.

There are many cases when we would like the enumeration or simulation to include the results of an algorithm. An example is a mathematical program where uncertain parameters are modeled as random variables. One assumption in this context is that the decision maker can wait to make a decision until after the random variables are realized and then determine the optimum solution. Say we would like to learn about the distribution of the optimum objective value. This could be accomplished with the Functions feature of this add-in if the solution algorithm could be called for each sample of the random variables. This capability is provided by the Algorithm feature. On this page we provide a general discussion on the use of the algorithm feature. We provide complete examples elsewhere.



An LP Example

  To illustrate the Algorithm feature we use the LP model below. The model was constructed using the Random Problem option of the Math Programming add-in. The solution shown is optimal. We consider a situation where the right-hand-side values in the range F15:F19 are random variables. We assume that the decision maker can wait to choose values of the variables X1 through X10 until after the RHS values are known. Our problem is to learn about the distribution of the optimum objective value.

The model of the stochastic features is constructed by choosing the Add Function command. The dialog below is presented. We have selected the simulation option. During the simulation process, the add-in will run the specified algorithm at every evaluation of a sample if a valid algorithm is named in the Algorithm field. For the current example we enter LP/IP. This indicates that for every sample, the solution of the LP will be computed with the Jensen LP/IP add-in.

  The function form is placed on the worksheet below the LP model. An additional row is added just below the title (row 23). The name of the algorithm is in G23. Some algorithms require additional parameters. These are placed to the right of the name starting in cell H23.

  The example will call the Jensen LP/IP Solver at each sample point. In order to use that algorithm, a linear programming model constructed with the Math Programming add-in must be present on the worksheet and the LP/IP add-in must be installed.


Linking the Models


Before proceeding, the LP form must be linked to the Function form. We have chosen the random variables to represent deviations of the RHS values from the original values. Two kinds of links are required. The random variables of the Function form must be linked to the input cells of the LP model, and the LP solution and its feasibility must be linked to the function values of the Function Form. These are illustrated below.

The random variables are in row 30. These are linked to the RHS values with an Excel function. We find in cell F15 the function,

=C23 + G30.

Similarly the other RHS values are linked to the random variables. The example shows the effect.

The LP objective is in cell F4. We link that to the function form by placing in G32 the equation:


This is the name of F4. The feasibility state equation is in G33 (not shown). That equation returns TRUE when the LP has a feasible solution and FALSE when it does not.




  To perform the simulation we choose Moments from the menu. The results of 1000 simulation iterations are below. All simulated samples were feasible and the mean simulated objective (122) is less than the deterministic objective value (125.6). We consider this example further on the Stochastic Programming pages.


Named Algorithms


We have used math programming as an example, but the algorithm feature can be used for a variety of situations. Several named algorithms are available, and the user can provide references to others. The named algorithms and their conditions for use are listed in the table. Links on the names lead to more detailed descriptions.

Algorithm Name Purpose Condition for Use
LP/IP Solve an LP The LP/IP add-in must be installed and the active worksheet must contain an LP model constructed by the Math Programming add-in.
Network Solve a Network Model The Jensen Network Solver add-in must be installed and either a network model or a transportation model constructed by the Math Programming add-in must be on the active worksheet.
Solve_MP Use the Solver specified for the MP model The Math Programming add-in and whatever add-in used for the solver must be installed. This command can call the Excel Solver for nonlinear models.
IRR Compute the Internal Rate of Return A NPW model constructed by the Economics add-in must be on the active worksheet. The Economics add-in must be installed. This algorithm also needs the name of the model. The name of the model is placed in the cell to the right of the name "IRR".

The call statement for the algorithms are programmed in VBA using a command similar to the one below.

my_result = Application.Run("lpip_solver.xla!Solvmod")

By placing the name of the subroutine in this format in the Algorithm field, any algorithm programmed in VBA can be run. For example if the string below is entered, the LP/IP solver will run with each simulation. The add-in name is first, then "!" and then the subroutine name.


We illustrate each of the named cases on the pages elsewhere on this site.


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Operations Research Models and Methods
by Paul A. Jensen
Copyright 2004 - All rights reserved