How to use Solver Add-In in Excel?

Summary: The Solver Add-In in Excel is a powerful optimisation tool that helps users solve complex decision-making problems. Adjusting variables within specified constraints enables the maximisation or minimisation of objectives, making it invaluable for finance, manufacturing, and resource allocation applications across various industries.

Introduction

With a 10.33% market share in the productivity software market, Microsoft Excel is more than just a tool for basic calculations. It is a part of the Office Products and Services segment, which generated $44.9 billion in revenue for Microsoft in 2023. Excel offers powerful features like the Solver Add-In in Excel. 

Solver is an optimisation tool that helps solve complex decision-making problems, such as resource allocation, scheduling, and financial modelling. This blog explores how Solver enhances Excel’s capabilities and supports users in making informed decisions across various fields, boosting problem-solving efficiency in business, finance, and more.

Key Takeaways

• The Solver Add-In enhances Excel’s capabilities for complex decision-making.
• It supports linear, nonlinear, and integer optimisation problems.
• Users can enable Solver through Excel Options easily.
• Understanding constraints and variable definitions is crucial for effective use.
• Solver is widely applicable in finance, resource allocation, and supply chain management.

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What is the Solver Add-In in Excel? 

The Solver Add-In in Excel is a powerful tool for solving optimisation problems. It helps users find the best solution by adjusting variables within specified constraints. Solver can be used for linear, nonlinear, and integer optimisation, making it versatile in various scenarios where the goal is to maximise or minimise an objective.

Solver works by identifying the relationship between variables in a given problem. It takes an objective cell, which represents the goal, and adjusts the values of decision variables to reach the best possible result. 

For instance, in a business context, Solver might be used to determine the optimal production levels of different products, maximising profits while respecting limitations on resources such as labour or materials. In Excel, Solver can handle three main types of optimisation problems:

Linear Optimisation

This involves problems where the relationship between variables is linear. The Solver Add-In uses the Simplex LP method to solve these problems. An example would be maximising profit or minimising cost when there are linear relationships between factors like price, demand, or resource allocation.

Nonlinear Optimisation

In these problems, the relationship between variables is not linear, and Solver uses the GRG Nonlinear method to find solutions. Nonlinear problems are common in scenarios where changes in one variable disproportionately affect others, such as optimising supply chains or portfolio risk.

Integer Optimisation

Solver also handles problems where variables must be whole numbers (integers). This is useful in problems like scheduling, where fractional values don’t make sense, such as determining the number of workers or machines needed for a task.

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How to Enable the Solver Add-In in Excel

Solver is a useful tool in Excel that helps solve optimisation problems, such as maximising profits or minimising costs, by adjusting variables based on set conditions. However, it’s not always enabled by default, so you must activate it before using it. Here’s a simple, step-by-step guide to help you allow the Add-In Solver in Excel.

Accessing Excel Options

You must first access the Excel Options menu to enable the Solver Add-In. This is where you can configure various features in Excel, including adding or removing tools like Solver. Follow the instructions below to open this menu.

• For Excel 2016 or later:
  • Open Excel.
  • Click on the File tab in the ribbon.
  • In the left-hand sidebar, select Options. This opens the Excel Options window.
• For Excel 2013 and earlier:
  • Click on the File tab or the Office button (depending on the version).
  • From the dropdown, select Options.

Once you’re in Excel Options, you can configure the settings for your workbook, including enabling add-ins like Solver.

Activating Solver from the Add-Ins Menu

After accessing the Excel Options menu, the next step is to activate Solver. This is done through the Add-ins section of the Options window. Follow the steps below to make Solver available in Excel.

• In the Excel Options window, select Add-ins from the left panel.
• You will see a dropdown menu at the bottom of the window labelled Manage. From the dropdown list, choose Excel Add-ins.
• Click on the Go button next to the dropdown.
• A new dialog box will appear listing all available add-ins. Look for Solver Add-in in the list.
• Check the box next to Solver Add-in.
• Click OK.

Once you click OK, Solver will be activated in Excel and found in the Data tab under the Analysis group. You are now ready to use Solver for optimisation tasks.

Troubleshooting: Solver Not Showing in the Add-Ins List

Sometimes, you may find that the Solver Add-In is unavailable in the Add-ins list. If this happens, there are a few troubleshooting steps to follow. These solutions can help you resolve any issues preventing Solver from appearing in Excel.

Check for Office Updates

If Solver is missing, it’s a good idea to ensure your version of Excel is up to date. Some features may be unavailable in older versions. To update Excel, click the File tab, click Account, and select Update Options.

Repair Excel

If Solver is still not showing up, you may need to repair your Excel installation. Navigate to the Control Panel, select Programs, find Microsoft Office, and choose Repair.

Check Add-in Location

If Solver isn’t showing after following the steps above, try manually locating the add-in. Go to the Manage dropdown in the Add-ins window and select COM Add-ins. Then, browse to the location of the Solver Add-in file and add it.

By following these troubleshooting tips, you should be able to resolve any issues with activating the Solver Add-In and begin using it effectively.

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Understanding the Solver Interface

Whether you’re maximising profit, minimising costs, or finding the best resource allocation, the Solver interface is where the magic happens. To harness Solver’s full potential, it’s crucial to understand how the Solver dialog box functions, what each section does, and how to define and implement the problem correctly.

Overview of the Solver Dialog Box

When you open the Solver Add-In, you will be greeted with the Solver dialogue box. This is where you set up your optimisation problem and provide Solver with the necessary inputs. The dialogue box is divided into distinct sections, each serving a specific purpose:

• Set Objective: This is where you define the target or goal of your optimisation.
• By Changing Variable Cells: Here, you select the decision variables that Solver will adjust to find the optimal solution.
• Subject to the Constraints: This section allows you to set limitations or conditions that must be respected during optimisation.

Understanding these areas will help you configure Solver correctly and get the best results.

Objective Cell: What It Is and How It Works

The Objective Cell is the focal point of your optimisation model. It represents the goal Solver seeks to optimise—maximising or minimising its value. For example, if you’re trying to maximise profit, the objective cell could contain a formula calculating total profit, such as a sum of revenue minus costs.

When you set up the Solver, you’ll point to this cell and specify whether you want to maximise, minimise, or set it to a specific value. 

The solver then uses the values in the decision variables (the ones you’ll define next) to adjust the parameters until the objective cell reaches the best possible outcome, given any constraints you’ve set.

Variable Cells: Defining Decision Variables

The Variable Cells are the heart of your optimisation model. These cells hold the decision variables, which the Solver will adjust to optimise the objective. 

For instance, in a manufacturing scenario, these could represent the number of units to produce each product. Solver will determine the optimal number of units to produce to achieve the goal (maximise profit, minimise cost, etc.).

When defining your variable cells, choosing the cells where the decision variables will be placed is essential. These are typically input cells where the Solver tries different values during optimisation. These values can be constrained within certain ranges, depending on the specific problem you’re solving.

Constraints: Setting Limitations and Conditions

Constraints are essential in defining the boundaries of your optimisation problem. These limitations specify the conditions that must be met while Solver finds the optimal solution. 

Constraints might be related to resources, such as time, budget, or raw materials, or they might be limits on the variables themselves (e.g., the number of products cannot be negative or exceed a certain limit).In the Solver dialogue box, you can add constraints for each variable or for the objective cell. 

For example, you might set a constraint to ensure that the number of products is at least 10 but no more than 100. You can define these constraints using formulas or fixed values, depending on the problem.

The Different Solver Models

Excel’s Solver provides three distinct solving methods, each tailored to different optimisation problems. Understanding the different Solver models is key to choosing the right one for your problem. These methods are designed to handle linear, nonlinear, and complex issues, each with its approach and application.

Simplex LP

The Simplex method is used for linear optimisation problems, where both the objective function and the constraints are linear (i.e., they involve straight-line relationships). This method is efficient and reliable for problems like maximising profits in a linear system or minimising transportation costs.

GRG Nonlinear

The Generalised Reduced Gradient (GRG) method is designed for nonlinear optimisation problems. This solver is useful when the relationships in the model are not linear, such as when a production process involves diminishing returns or non-linear cost structures.

Evolutionary

The Evolutionary Solver is ideal for complex, non-smooth problems or when other methods fail. It works by mimicking the process of natural evolution, iterating through possible solutions to find an optimal or near-optimal result. This method is often used for problems with discontinuities or other ineffective methods.

Each solver model has strengths and weaknesses, so it’s crucial to choose the one that best fits the nature of your problem.

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How to Use the Solver Add-In

In this section, we’ll go through two examples: one for maximising profit in a linear problem and another for maximising efficiency in a nonlinear problem. We’ll also cover setting constraints, adjusting parameters, and interpreting Solver’s results.

Example 1: Solving a Simple Linear Optimisation Problem (Maximising Profit)

Let’s say you own a business producing two products. Each product has a different profit margin and requires specific resources. Your goal is to maximise profit while staying within your resource limits.

• Define Your Objective: First, create a cell that calculates the total profit based on the quantity of each product produced. This cell will be your “objective cell” in Solver.
• Set Decision Variables: Next, assign cells for the quantity of each product. These cells are your “variable cells” since Solver will adjust these quantities to maximise profit.
• Add Constraints: Define resource constraints in other cells, such as labour hours or material availability. In Solver, add these constraints by specifying that the resource cells must be less than or equal to their available limits.
• Run Solver: Open Solver, set your objective cell to maximise, select your variable cells, and apply your constraints. Choose the “Simplex LP” solving method for linear problems, and click “Solve.”
• Interpret the Results: Solver will either find an optimal solution or state if no feasible solution exists. If successful, Solver will display the quantities of each product that maximise profit within your constraints.

Example 2: Solving a Nonlinear Optimisation Problem (Maximising Efficiency in Production)

Suppose you aim to maximise production efficiency by balancing energy use, time, and output. This problem involves nonlinear relationships, so it requires a different Solver method.

• Define the Objective: Use a cell to calculate the efficiency score based on different input variables. This is your objective cell.
• Set Variable Cells: Create cells for variables like machine speed, worker shifts, or material usage, which Solver will adjust.
• Apply Constraints: Set limits on energy consumption, shift hours, or other resources. Add these as constraints in Solver to prevent exceeding capacity.
• Run Solver with Nonlinear Settings: In the Solver dialog, set the objective cell to maximise, select your variable cells, add constraints, and choose the “GRG Nonlinear” solving method. Click “Solve” to allow Solver to find an optimal balance of variables for maximum efficiency.
• Review the Solution: Solver will provide an efficiency-maximising combination of inputs within your constraints. You can save or further refine this solution as needed.

Setting Constraints and Adjusting Parameters

Constraints are critical in Solver to ensure your solution stays realistic. In the Solver dialog, specify each constraint by selecting the relevant cells, defining operators (≤, =, or ≥), and setting values. Adjust parameters such as maximum time or tolerance level to control Solver’s performance.

Running Solver and Interpreting Results

Once you’ve set the objective, variables, constraints, and solving method, click “Solve.” Solver will either present an optimal solution or notify you if it can’t find one. Examine the solution values in your variable cells and the final objective value in the results window. 

Solver also offers reports (like Sensitivity or Limits) for a deeper analysis. Use these to understand how changes in constraints might impact the solution, helping you refine your model and achieve even better results.

This structured approach to using Solver allows you to optimise outcomes effectively, whether for profit, efficiency, or other goals.

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Advanced Solver Features

Once you understand Excel’s Solver Add-In, exploring its advanced features can help you tackle even more complex optimisation problems. Solver’s advanced capabilities are designed to fine-tune your optimisation models, accommodating simple and highly intricate scenarios. 

Below, we’ll discuss Solver’s three primary methods and explore how to customise parameters and conduct sensitivity analysis to interpret Solver’s results effectively.

Simplex LP: Linear Optimisation

The Simplex LP method is ideal for linear optimisation problems with linear relationships between variables. This method finds the optimal solution for scenarios with straightforward constraints and a clear objective function, such as maximising profit or minimising cost. It’s beneficial in business applications involving resource allocation or financial planning, where relationships remain proportional.

To use Simplex LP, set up your objective function and constraints linearly in the Solver interface, then select “Simplex LP” as the solving method. Solver then iterates through potential values within the given constraints to find the best solution. This efficient method provides quick results, especially for larger, linear models.

GRG Nonlinear: Nonlinear Optimisation

For problems involving nonlinear relationships, the GRG (Generalised Reduced Gradient) Nonlinear method is the go-to choice. Nonlinear problems are those where variable changes do not produce proportional changes in outcomes. For example, if you’re optimising production levels with diminishing returns or facing exponential growth rates, GRG Nonlinear is more appropriate than Simplex LP.

To implement GRG Nonlinear, configure your objective and constraint cells in a nonlinear format—meaning the values do not change uniformly. Select “GRG Nonlinear” in the Solver settings. This method searches for the most optimal solution by iteratively adjusting variables within nonlinear constraints. While effective, converging can require more time, especially in complex models.

Evolutionary: Complex or Non-Smooth Problems

The Evolutionary method is designed to handle particularly complex, non-smooth optimisation problems. Non-smooth problems contain variables that change in discrete steps or have no fixed linear pattern, making them unsuitable for Simplex LP or GRG Nonlinear. 

Evolutionary Solver uses a genetic algorithm, iterating over generations to find increasingly better solutions. It’s perfect for problems with many potential solutions, complex constraints, or other methods that fail to provide feasible results.

When using Evolutionary Solver, define your objective and constraint variables with wide possible ranges, then select “Evolutionary” as the method. The process may take longer due to the algorithm’s complexity, but it’s often the only solution for models with discontinuous or difficult-to-predict relationships.

Customising Solver Parameters

Solver allows parameter customisation to improve performance and accuracy. You can adjust options like the maximum time for solving, the number of iterations, and tolerance levels. These settings control how much time and computing power Solver invests in finding a solution. 

For instance, lowering tolerance values can yield more precise results, though it may increase processing time.

Fine-tuning these parameters helps achieve the best balance between accuracy and efficiency, especially for large, complex models where Solver may need more guidance to reach optimal solutions.

Sensitivity Analysis and Interpreting Solver Reports

After Solver finds a solution, conducting sensitivity analysis helps you understand how changes in input variables affect the output. Solver’s sensitivity report shows information like shadow prices and reduced costs, which reveal how much objective function would change if there were slight alterations in the constraints. 

This analysis is particularly useful for decision-making, as it lets you explore the flexibility of your solution.

Solver reports also include results and limits reports, further clarifying how constraints and variables interact in your model. By examining these, you gain insights into the strength and reliability of your solution and areas where adjustments could further optimise results.If you want to discover how to create an Excel MIS report, click on the link.

Common Errors and Solutions

Using the Solver Add-In in Excel can be powerful, but it’s not without its challenges. Understanding common errors and their solutions will help you get the most out of Solver and streamline your optimisation process. Below, we’ll explore users’ most frequent issues and provide actionable steps to resolve them efficiently.

Unsolvable Problems

Unsolvable problems occur when Solver cannot identify a solution that satisfies all constraints. This often happens if your objective function has no feasible path due to incompatible or overly restrictive constraints.

Review your constraints carefully to resolve this, checking for redundancy or conflicting requirements. Try loosening constraints or testing the model by reducing the number of restrictions to see if Solver can identify any feasible solution.

Infeasible Solutions

An infeasible solution means Solver found no answer that satisfies all constraints. This issue typically arises if the constraints are too restrictive or not logically aligned. 

To address infeasibility, double-check each constraint to ensure it aligns with your objective and that each constraint is realistic and achievable. Removing or adjusting constraints one at a time can help pinpoint the problem. Additionally, ensure the decision variables are within reasonable bounds, as extreme values can contribute to infeasibility.

Unbounded Solutions

An unbounded solution occurs when there’s no limit to the objective function’s value, causing Solver to seek larger values. This usually results from missing constraints that restrict the objective function’s range. 

To fix this, verify that you’ve set constraints for all variables. Adding upper and lower bounds for decision variables can help Solver find a solution within realistic limits.

Tips for Optimising Solver Performance

To optimise Solver’s performance, start by simplifying your model. Remove any unnecessary variables or constraints, as each additional factor adds complexity. Choosing the correct Solver algorithm is also essential: Simplex LP for linear problems, GRG Nonlinear for nonlinear ones, and Evolutionary for more complex or non-smooth problems. 

Finally, adjusting Solver’s iteration and time limits may prevent Solver from running indefinitely without producing a solution.

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Practical Applications of Solver

The Solver Add-In in Excel is widely used across industries for optimising processes, maximising profits, and making data-driven decisions. Solver’s ability to handle constraints and variables makes it ideal for real-world applications, helping professionals solve complex problems efficiently. Here are a few practical ways businesses and individuals use Solver:

Financial Modelling 

In finance, Solver is a powerful tool for portfolio optimisation. It helps investors allocate funds to maximise returns or minimise risk based on constraints, such as budget limits or risk tolerance. It’s also used in loan amortisation to determine optimal payment schedules.

Resource Allocation

Organisations often use Solver to allocate resources efficiently. Solver can optimise production planning in manufacturing by balancing material costs, labour, and time constraints. It also assists workforce scheduling, ensuring staffing levels meet demand without overspending.

Supply Chain Optimisation

Supply chain managers use Solver to reduce shipping costs, optimise inventory levels, and streamline logistics. Solver can find the most cost-effective solutions by setting constraints like delivery deadlines or storage capacities.

Engineering and Science

Engineers apply Solver to optimise designs, minimise waste, or improve energy efficiency. Scientists use it for experimental design, optimising parameters to achieve desired outcomes within set limitations.

Solver’s versatility allows it to solve many problems, making it a valuable tool in data-driven decision-making.

Wrapping up

The Solver Add-In in Excel significantly enhances decision-making capabilities by enabling users to tackle complex optimisation problems. From resource allocation to financial modelling, this versatile tool empowers businesses and individuals alike to maximise outcomes while respecting constraints. Users can leverage Solver for efficient problem-solving across various fields by understanding its functionalities and methods.

Frequently Asked Questions

What is the Solver Add-In in Excel?

The Solver Add-In in Excel is an advanced optimisation tool designed to help users solve complex decision-making problems. It adjusts multiple variables within set constraints to identify the optimal solution for maximising or minimising an objective, making it ideal for resource allocation and financial modelling tasks.

How do I Enable the Solver Add-In in Excel?

To enable the Solver Add-In, first, open Excel and go to the File tab. Then select Options and click on Add-Ins. In the Manage dropdown, choose Excel Add-ins, click Go, check the box next to Solver Add-in, and click OK to activate it in the Data tab.

What Types of Optimisation Problems can Solver Handle?

Solver can effectively manage optimisation problems, including linear, nonlinear, and integer scenarios. It allows users to optimise functions by adjusting decision variables to maximise profits or minimise costs while adhering to specified constraints. This makes it applicable in finance, operations, logistics, and many other fields.