Transcript Ch19.PowerPoint
Spreadsheet-Based Decision Support Systems
Chapter 19: Solver Re-Visited Prof. Name Position University Name [email protected]
(123) 456-7890
Overview
19.1 Introduction 19.2 Review of Chapter 8 19.3 Object – Oriented API in Risk Solver Platform 19.4 Application 19.5 Summary 2
Introduction
Preparing an optimization problem to be solved by the Solver Preparing and running the Risk Solver Platform using Object –Oriented API Creating a dynamic optimization application using Object – Oriented API 3
Review of Chapter 8
Understanding the problem Preparing the spreadsheet Solving the Model 4
Review of Chapter 8
In Chapter 8 we described how to transform a problem into a mathematical model and then use the Risk Solver Platform to solve it.
We will review the main parts of a mathematical model and the Solver preparation steps.
There are important steps which take place in the Excel spreadsheet before the Solver is used.
– Reading and Interpreting the Problem – Preparing the Spreadsheet – Solving the Model and Reviewing the Results 5
Understanding the Problem
Mathematical models transform a word problem into a set of equations that clearly define the values you are seeking given the limitations of the problem.
There are three main parts of a mathematical model.
– – –
Decision variables Objective function Constraints
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Decision Variables
Decision variables
are variables that are assigned to a quantity or response that you must determine in the problem.
They can be defined as negative, non-negative, or unrestricted variables.
– An unrestricted variable can be either negative or non-negative.
These variables are used to represent all other relationships in the model, including the objective function and constraints.
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Objective Function
The
objective function
is an equation that states the goal, or objective, of the model.
Objective functions are either maximized or minimized.
– Most applications involve maximizing profit or minimizing cost.
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Constraints
The
constraints
are the limitations of the problem.
In most realistic problems there are certain limitations, or constraints, which we must satisfy.
Constraints can be a limited amount of resources, labor, or requirements for a particular demand.
These constraints are also written as equations in terms of the decision variables.
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Applying a Mathematical Model
We saw some examples in Chapter 8 which involved production of different parts.
– The amount to produce of each part was considered the decision variables.
– Maximizing profit (given certain costs and revenues for each part) was the objective function.
– There were also constraints which limited the resources needed for each part and stated a minimum demand that had to be met.
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Preparing the Spreadsheet
We must translate and clearly define each part of our model in the spreadsheet.
The Solver will then interpret our model according to how we have declared the decision variables, objective function, and constraints in the spreadsheet.
We use referencing and formulas to mathematically represent the model in the spreadsheet cells.
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Entering Decision Variables
To enter the decision variables, we list them in individual cells with an empty cell next to each one.
The Solver will place values in these cells for each decision variable as it solves the model.
All other equations (for the objective function and constraints) will reference these cells.
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Entering Objective Function
To enter the objective function, we place our objective function equation in a cell with an adjacent description.
This equation should be entered as a formula which references the decision variable cells.
As the Solver changes the decision variable values in the decision variable cells, the objective function value will automatically be updated.
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Entering Constraints
To enter the constraints we list the equations separately with a description next to each constraint.
The most important part of setting up the constraint table is expressing the left side of our equations as formulas.
– As each constraint is in terms of the decision variables, all of these formulas must be in terms of the decision variable cells that Solver uses.
– These equations should reference the decision variable cells so that as the Solver places values in these cells the constraint values will automatically be calculated.
Another important consideration when laying out the constraints in preparation for Solver is that the RHS (right-hand side) values of each constraint should be in individual cells to the right of these equations.
We should also place all inequality signs in their own cells.
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Naming the Ranges
Another advantageous way to keep our constraints organized as we use the Solver is to name our cells.
Using the methods discussed in Chapter 3, we can name the ranges decision variables and the cell which holds the objective function equation.
We can also name ranges of constraint equations which are in a similar category of constraints or which have similar inequality signs.
This makes inserting these model parts into the Solver easier when using both Excel and VBA code .
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Solver Example from Chapter 8
A company produces six different types of products. They want to schedule their production to determine how much of each product type should be produced in order to maximize their profits. This is known as the Product Mix problem. Production of each product type requires labor and raw materials; but the company is limited by the amount of resources available. There is also a limited demand for each product, and no more than this demand per product type can be produced. Input tables for the necessary resources and the demand are given. 16
Step 1
Decision Variables:
The amount produced of each product type. – x1, x2, x3, x4, x5, x6
Objective Function:
Maximize Profit. – z = p1*x1 + p2*x2 + p3*x3 + p4*x4 + p5*x5 + p6*x6
Constraints:
There are two resource constraints: labor, l, and raw material, r. – Labor Constraint: l1*x1 + l 2*x2 + l 3*x3 + l4*x4 + l 5*x5 + l 6*x6 <= available labor = 4500 – Raw Material Constraint r1*x1 + r 2*x2 + r 3*x3 + r 4*x4 + r 5*x5 + r 6*x6 <= available raw material = 1600 There is also a constraint that all demand, D, must be met, and no extra amount can be produced. – Demand Constraint: xi <= Di for i = 1 to 6 17
Figure 19.1
The Spreadsheet layout for the Product Mix 18
Figure 19.2
Risk Solver Task Pane:
Set the
Objective
function formula.
to the location of the objective Set the
Variables
to the empty decision variable cells named “PMDecVar”.
The
Constraints
show the left and right sides of the constraint equations with the corresponding inequalities.
The labor and raw material constraints are listed as
Normal
constraints. Demand constraints are listed as the
Bound
constraints since they set an upper limit on the value that the decision variables can take. Set the
Assume Non-Negative
property of the model to true. 19
Figure 19.3
The Results of the Solver are shown.
All constraints are met.
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Object-Oriented API in Risk Solver Platform
Building a Problem Using Object-Oriented API Identifying the Solver Engine and Setting its Parameters Running the Solver Accessing Optimization Results 21
Object-Oriented API in Risk Solver Platform
Use object-oriented API to create a problem initially, and then add the decision variables, objective function, and constraints. From
Tools > References
list on the VBE select
Risk Solver Platform xx Type Library
. Use VBA code to manipulate Engine parameters, and optimize the problem using VBA commands.
Access the results of the optimization, such as, objective function value, optimal solution, dual variables, etc.
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Building a Problem Using Object-Oriented API
We start by creating an instance of the
Problem object
. The
Problem object
represents the whole optimization problem. We declare this object variable using the
Dim
statement as follows: –
Dim MyProb As New RSP.Problem
Set the
SolverType
Minimize
property of the
Solver
object to
Maximize
or to specify whether the optimization problem should be
maximized
or
minimized
. – The values that the
SolverType
property takes are:
Solver_Type_FindFeas
(find a feasible solution)
Solver_Type_Maximize
(maximize)
Solver_Type_Minimize
(minimize)
Solver_Type_Simulate
(simulate) 23
Adding New Decision Variables
To add new decision variables to
MyProb
, we create a
Variable
object, initialize the object, set its properties, and then add this object to the
Variable collection
of the problem object.
Use the
Init
method to specify the range which contains the decision variables. Use the
Variables.Add
method to add the decision variables to the problem object.
Dim MyVar As New Variable MyVar.Init Range("DecisionVariables") MyVar.NonNegative
MyProb.Variables.Add MyVar Set MyVar = Nothing
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Adding the Objective Function
To add the objective function to
MyProb
, we create a
Function
object, initialize this object, set its properties, add it to the
Functions collection
of the problem, and finally set its value to nothing.
Use the
Init
method to specify the range of the objective function.
Use the
FunctionType
property to identify the type of the function that is being added to the problem.
– This property takes the value
Function_Type_Objective
for the objective function, and
Function_Type_Constraint
for normal constraints.
Dim MyObj As New RSP.Function
MyObj.Init Range("ProdObjFunc") MyObj.FunctionType = Function_Type_Objective MyProb.Functions.Add MyObj Set MyObj = Nothing
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Adding Constraints
Use a
For Next
loop to add each individual constraint.
Create an array of
Functions
objects using the
Dim
statement. – The size of this array is set to the total number of constraints.
Use the
Init
method specifies the range which contains a constraint equation. The
Relation
method allows us to specify the relation (<=, =, or >=) and the RHS value of the constraints..
– The constants
Cons_Rel_EQ
(=),
Cons_Rel_GE
(<=) are used to specify the relation.
(>=) or
Cons_Rel_LE
The
Add
method is used to add each individual constraint to the end of the
Function collection
of the problem object.
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Adding Constraints (cont’d)
Dim MyConstraints(NumCons) As New RSP.Function
For i = 1 To NumCons MyConstraints (i).Init Range("A1").Offset(i - 1, 0) MyConstraints (i).Relation Cons_Rel_GE, Range("B1").Offset(i - 1, 0).Value
MyConstraints (i).FunctionType = Function_Type_Constraint MyProb.Functions.Add MyConstraints (i) Set MyConstraints (i) = Nothing Next
The
Remove
method allows us to delete constraints from the problem formulation.
The only parameter this function takes is the index of the constraint that will be removed. The following statement removes the last constraint of
MyProb
.
–
MyProb.Functions.Remove NumCons
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Identifying Solver Engine and Parameters
Prior to solving an optimization problem, we should specify the appropriate
Solver Engine
to use. For an optimization problem, the
Engine LP/Quadratic
,
Standard Evolutionary
object represents the or the
GRG Nonlinear
solver depending on the type of the problem we are solving. When the
Solver.Optimize
method is then called, this engine will run. If the problem we are working with is a linear program, we will select the LP/Quadratic Solver using: –
MyProb.Engine = MyProb.Engines("Standard LP/Quadratic")
Prior to modifying Solver Engine parameters you should reset all the parameters to their default value by using the
ParamReset
method. –
MyProb.Engine.ParamReset
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Identifying Solver Engine and Parameters
There are a large number of problem parameters for a particular Solver Engine. Use the
Name
property to identify the name and the index of a parameter of interest. Use this index to access and modify the corresponding parameter.
For i = 0 To MyProb.Engine.Params.Count - 1 If MyProb.Engine.Params(i).Name = "Iterations" Then Next i MyProb.Engine.Params(i).Value = 100 End If MyProb.Engine.Params
(“AssumeNonneg”).Value = True
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Running the Solver
Use
Solver.Optimize()
method to run the Solver.
– This method’s only argument,
Solve_Type
, takes two values:
Solve_Type_Analyze
to perform a model analysis without actually solving,
Solve_Type_Solve
to solve the optimization problem.
MyProb.Solver.Optimize (Solve_Type)
The
OptimizeStatus
property of the
Solver object
returns an integer value classifying the result of the optimization. – The values 0, 1, or 2 signify a successful run in which a solution has been found. – The value 4 implies that there was no convergence – The value 5 implies that no feasible solution could be found. 30
Running the Solver (cont’d)
Dim result As Integer result = MyProb.Solver.OptimizeStatus
If result = 5 Then MsgBox “Your problem was infeasible. Please modify your model.” End If
Solver.Optimize
is the only command needed to run the Solver. – If we have already set up the Solver in the spreadsheet or in some initial part of the VBA code, at execution time, we need to write only the
Solver.Optimize
command. 31
Accessing Optimization Results
We can access the results of the optimization by using the
Value
property of
Variable
object, or
FinalValue
,
DualValue
and
Slack
properties of the
Function object
. VBA is also used to automatically generate the solution reports listed in the
Report
drop-down menu of the
Ribbon
. The
Size
property of
VarDecision
object identifies the total number of the decision variables in a problem.
For i = 0 To MyProb.VarDecision.Size - 1 MsgBox MyProb.VarDecision.Value(i) Next i
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Accessing Optimization Results (cont’d)
The
Count
property of the
Functions
functions in the Problem object.
object identifies the total number of –
MsgBox MyProb.Functions(MyProb.Functions.Count - 1).Value(0)
To print the dual variables for problem constraints, we would write:
For i = 0 To MyProb.Functions.Count - 2 Next i MsgBox MyProb.Functions(i).DualValue(0)
If we decide to generate the reports, then we need to set the value of
Bypass Solver Reports
engine parameter to
False
. After the Solver has finished running, we can access the reports using the following statement.
–
Solver.Report ReportName
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Accessing Optimization Results (cont’d)
ReportName
is one of the following strings “
Answer
”, “
Feasibility
”, “
Linearity
”, “
Limits
”, “
Population
”, “
Sensitivity
”, “
Scaling
”, and “
Solution
”. The Report method generates a report in the form of an Excel worksheet and inserts it into the active workbook. 34
Other Solver Methods
The
Init
method instantiates a problem using a named model or worksheet. – This method creates the variable and function objects using an optimization problem already defined in the worksheet. The
Save
method is used to save model specifications in a cell range, or as a text string model name. The
Load
– method is used to load model specifications.
Format
is one of the parameters required by this method. This parameter is a constant which takes one of the following two values:
File_Format_XLStd
or
File_Format_XLPSI
. 35
Example Code 1
Use this VBA code to: – Initiate a problem using a model already defined in an existing Excel worksheet. – Save problem parameters.
– Solve the problem.
– Display the objective function value found from the optimization.
Sub Init_Save_Prob_Methods() Dim prob As New RSP.Problem
prob.Init Worksheets("Prob_Setup") prob.Save Worksheets("Prob_Save").Range(“A1”) prob.Solver.Optimize
MsgBox prob.Functions(prob.Functions.Count - 1).Value(0) End Sub
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Example Code 2
Using the following lines of code you can load the model you already saved, optimize the problem, and display the corresponding objective function value.
Sub Load_Prob_Method() Dim prob As New RSP.Problem, Param As Range Set Param = Worksheets("Prob_Save").Range(Range(“A1”),Range(“A1”). End(xlDown)) prob.Load Param, File_Format_XLStd prob.Solver.Optimize
MsgBox prob.Functions(prob.Functions.Count - 1).Value(0) End Sub
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Application
Dynamic Production Problem 38
Description
We consider a production problem in which we are trying to determine how much to produce of different items in order to maximize profit. Each item has a given weight, space requirement, profit value, quota to satisfy, and limit on production. Each item must meet its quota but be less than its limit.
There is also a total weight requirement and space requirement for shipping which will limit the production. 39
Dynamic Solver
We want this production problem to be dynamic. – We want the user to decide how many items to consider in the problem and to provide the input for each item.
We limit these dynamic options to five possible items and prepare the spreadsheet for the maximum number possible. To make this problem dynamic, we will develop a user interface. 40
Figure 19.4
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Figure 19.5
The
Parameters form
asks the users for the number of decision variables.
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Figure 19.6
The Input form is dynamic in that it allows the users to enter input values for the number of items they specified in the
Parameters
form.
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Initial Code
We will have a main procedure associated with the
Solve Dynamic Problem
button called
SetParameters
. In this procedure, we – Initialize our range variables – Call the
ClearPrev
procedure – Show the users the first form (the second form will be shown from the first form code). – Redefine our dynamic ranges by using the values for the number of decision variables (
NumDV
) and the number of constraints (
NumCons
) identified in the code of the first form.
– Call the
SolveProb
procedure procedure.
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We show the variable declarations and code for the
SetParameters
procedure and a
ClearPrev
procedure.
Figure 19.7
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Parameters Form code.
Figure 19.8
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Input Form code.
Figure 19.9
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Figure 19.10
After initializing the text box arrays, it is easy to set these default values simply by looping through each array 48
Figure 19.11
This part of
SolveProb
procedure creates a problem instance and adds the corresponding decision variables, constraints and objective function.
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Figure 19.12
This part of
SolveProb
procedure selects an engine, sets engine parameters, solves the problem, and calls the
ViewResults
procedure.
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The
ViewResults
procedure displays the final value of the decision variables, the value of the dual variables, and the objective function value.
The
ClearProb
procedure clears the memory we allocated to the problem, variable and function objects.
Figure 19.13
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Application Conclusion
The application is now complete.
We can now solve this problem multiple times using the
Solve Dynamic Problem
button and varying the number of items for which the problem is solved. If the result is infeasible, we can simply modify the input values and solve it again.
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Summary
There are three main parts of an optimization model:
decision variables, objective function,
and
constraints
. Using the Risk Solver Platform requires a short sequence of steps: 1) reading and interpreting the problem, 2) preparing the spreadsheet, 3) solving the model and reviewing the results.
We use object-oriented API in Risk Solver Platform to create an instance of an optimization problem, and add to this problem the corresponding decision variables, constraints, and objective function. To select an engine for solving the problem we use the Engine property of the problem object.
To solve an optimization problem, we use the Solver.Optimize method. There is one argument for this method: Solve_Type.
We use the Solver.OptimizeStatus property of the problem object to keep or ignore the Solver results. We use the Solver.Report method of the problem object to generate solution reports. There is one argument for this method: ReportName. We use the
Init
,
Save
, and
Load
methods of the problem object to initialize a problem instance, save, and load a optimization problem.
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(place links here)
Additional Links
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