# Difference: OperationsResearchMethodology (1 vs. 12)

#### Revision 122009-10-09 - MichaelOSullivan

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# Operations Research Methodology

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1. Describing the Problem The aim of this step is to come up with a formal, rigorous model description. Usually you start an optimisation project with an abstract description of a problem and some data. Often you need to spend some time talking with the person providing the problem (usually known as the client). By talking with the client and considering the data available you can come up with a more rigorous model description required for formulation. Sometimes not all the data will be relevant or you will need to ask the client if they can provide some other data. Sometimes the limitations of the available data may significantly change your model description and subsequent formulation.
2. Formulating the OR Model The aim of this step is to translate the problem description into a valid OR model. The implementation of this step may be quite different depending on the OR model you are using. For example, if you are using linear programming to solve your problem, then formulating an OR model involves translating your problem into a linear programme. If you are using simulation to solve your problem, then formulating an OR model entails breaking down the behaviour of the system being simulated into a sequence of events and determining the random variables that "drive" the simulation.
3. Solving the OR Model The aim of this step is to solve your OR model. Just as the formulation step depended on the OR model being used, this solution step depends on your OR model. Additionally, there may be more than one solution method for a particular OR model. For example, solving a linear programme may be done using the Revised Simplex Method or an interior point method. Often, in practice, OR models may not be solved completely due to time constraints. Other algorithms may partially solve OR models (for optimisation models, these algorithms are known as heuristics and terminate with a "good" solution that is not necessarily optimal).
Changed:
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1. Performing analysis of the solution Often there is uncertainty in the problem description (either with the accuracy of the data provided, or with the value(s) of data in the future). In this situation the robustness of our solution to the OR model can be examined using analysis. Analysis involves identifying how the solution would change under various changes to the problem data (for example, what would be the effect of a given cost increasing, or a particular machine failing?). This sort of analysis can also be useful for making tactical or strategic decisions (for example, if we invested in opening another factory, what effect would this have on our revenue?). Another important consideration in this step (and the next) is the validation of the OR model's solution. You should carefully consider what the solution means in terms of the original problem description. Make sure it makes sense to you and, more importantly, to your client. Hence, the next step, presenting the solution and analysis is very important.
>
>
1. Performing analysis of the solution Often there is uncertainty in the problem description (either with the accuracy of the data provided or with the value(s) of data in the future). In this situation the robustness of our solution to the OR model can be examined using analysis. Analysis involves identifying how the solution would change under various changes to the problem data (for example, what would be the effect of a given cost increasing, or a particular machine failing?). This sort of analysis can also be useful for making tactical or strategic decisions (for example, if we invested in opening another factory, what effect would this have on our revenue?). Another important consideration in this step (and the next) is the validation of the OR model's solution. You should carefully consider what the solution means in terms of the original problem description. Make sure it makes sense to you and, more importantly, to your client. Hence, the next step, presenting the solution and analysis is very important.

1. Presenting the solution and analysis A crucial step in the optimisation process is the presentation of the solution and any analysis. The translation from an OR model's solution back into a concise and comprehensible summary is as important as the translation from the problem description into the OR model. In the case studies throughout this TWiki, we encourage the use of management summaries to present solutions and analysis from OR models. Key observations and/or decisions generated via OR must be presented in a way that is easy for the client or project stakeholders to understand. Your presentation is also a crucial first step in the implementation of any decisions generated by your OR model. If the results of your OR model and their consequences are not presented clearly and intelligently these results will never be used. This step is also your chance to suggest future work possibilities. This could include:
• Periodic monitoring of the validity of your OR Model;
• Further analysis of your solution, looking for other benefits for your client;

#### Revision 112009-06-26 - CameronWalker

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 META TOPICPARENT name="Trash.OpsRes_ForumWebHome"
`<-- Ready to Review - done - Lauren-->`

# Operations Research Methodology

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Let's consider the five steps of the OR methodology in more detail:

1. Describing the Problem The aim of this step is to come up with a formal, rigorous model description. Usually you start an optimisation project with an abstract description of a problem and some data. Often you need to spend some time talking with the person providing the problem (usually known as the client). By talking with the client and considering the data available you can come up with a more rigorous model description required for formulation. Sometimes not all the data will be relevant or you will need to ask the client if they can provide some other data. Sometimes the limitations of the available data may significantly change your model description and subsequent formulation.
Changed:
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<
1. Formulating the OR Model The aim of this step is to translate the problem description into a valid OR model. The implementation of this step may be quite different depending on the OR model you are using. For example, if you are using linear programming to solve your problem, then formulating an OR model involves translating your problem into a linear programme. If you are using simulation Fix link - Lauren to solve your problem, then formulating an OR model entails breaking down the behaviour of the system being simulated into a sequence of events and determining the random variables that "drive" the simulation.
>
>
1. Formulating the OR Model The aim of this step is to translate the problem description into a valid OR model. The implementation of this step may be quite different depending on the OR model you are using. For example, if you are using linear programming to solve your problem, then formulating an OR model involves translating your problem into a linear programme. If you are using simulation to solve your problem, then formulating an OR model entails breaking down the behaviour of the system being simulated into a sequence of events and determining the random variables that "drive" the simulation.

1. Solving the OR Model The aim of this step is to solve your OR model. Just as the formulation step depended on the OR model being used, this solution step depends on your OR model. Additionally, there may be more than one solution method for a particular OR model. For example, solving a linear programme may be done using the Revised Simplex Method or an interior point method. Often, in practice, OR models may not be solved completely due to time constraints. Other algorithms may partially solve OR models (for optimisation models, these algorithms are known as heuristics and terminate with a "good" solution that is not necessarily optimal).
2. Performing analysis of the solution Often there is uncertainty in the problem description (either with the accuracy of the data provided, or with the value(s) of data in the future). In this situation the robustness of our solution to the OR model can be examined using analysis. Analysis involves identifying how the solution would change under various changes to the problem data (for example, what would be the effect of a given cost increasing, or a particular machine failing?). This sort of analysis can also be useful for making tactical or strategic decisions (for example, if we invested in opening another factory, what effect would this have on our revenue?). Another important consideration in this step (and the next) is the validation of the OR model's solution. You should carefully consider what the solution means in terms of the original problem description. Make sure it makes sense to you and, more importantly, to your client. Hence, the next step, presenting the solution and analysis is very important.
3. Presenting the solution and analysis A crucial step in the optimisation process is the presentation of the solution and any analysis. The translation from an OR model's solution back into a concise and comprehensible summary is as important as the translation from the problem description into the OR model. In the case studies throughout this TWiki, we encourage the use of management summaries to present solutions and analysis from OR models. Key observations and/or decisions generated via OR must be presented in a way that is easy for the client or project stakeholders to understand. Your presentation is also a crucial first step in the implementation of any decisions generated by your OR model. If the results of your OR model and their consequences are not presented clearly and intelligently these results will never be used. This step is also your chance to suggest future work possibilities. This could include:

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`<-- Ready to Review - done - Lauren-->`

# Operations Research Methodology

#### Revision 92008-03-09 - LaurenJackson

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 META TOPICPARENT name="WebHome"
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`<-- Ready to Review -->`
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`<-- Ready to Review - done - Lauren-->`

# Operations Research Methodology

Solving an Operations Research (OR) problem is not a linear process, but the process can be broken down into five general steps:

Line: 18 to 18
Let's consider the five steps of the OR methodology in more detail:

1. Describing the Problem The aim of this step is to come up with a formal, rigorous model description. Usually you start an optimisation project with an abstract description of a problem and some data. Often you need to spend some time talking with the person providing the problem (usually known as the client). By talking with the client and considering the data available you can come up with a more rigorous model description required for formulation. Sometimes not all the data will be relevant or you will need to ask the client if they can provide some other data. Sometimes the limitations of the available data may significantly change your model description and subsequent formulation.
Changed:
<
<
1. Formulating the OR Model The aim of this step is to translate the problem description into a valid OR model. The implementation of this step may be quite different depending on the OR model you are using. For example, if you are using linear programming to solve your problem, then formulating an OR model involves translating your problem into a linear programme. If you are using simulation to solve your problem, then formulating an OR model entails breaking down the behaviour of the system being simulated into a sequence of events and determining the random variables that "drive" the simulation.
>
>
1. Formulating the OR Model The aim of this step is to translate the problem description into a valid OR model. The implementation of this step may be quite different depending on the OR model you are using. For example, if you are using linear programming to solve your problem, then formulating an OR model involves translating your problem into a linear programme. If you are using simulation Fix link - Lauren to solve your problem, then formulating an OR model entails breaking down the behaviour of the system being simulated into a sequence of events and determining the random variables that "drive" the simulation.

1. Solving the OR Model The aim of this step is to solve your OR model. Just as the formulation step depended on the OR model being used, this solution step depends on your OR model. Additionally, there may be more than one solution method for a particular OR model. For example, solving a linear programme may be done using the Revised Simplex Method or an interior point method. Often, in practice, OR models may not be solved completely due to time constraints. Other algorithms may partially solve OR models (for optimisation models, these algorithms are known as heuristics and terminate with a "good" solution that is not necessarily optimal).
2. Performing analysis of the solution Often there is uncertainty in the problem description (either with the accuracy of the data provided, or with the value(s) of data in the future). In this situation the robustness of our solution to the OR model can be examined using analysis. Analysis involves identifying how the solution would change under various changes to the problem data (for example, what would be the effect of a given cost increasing, or a particular machine failing?). This sort of analysis can also be useful for making tactical or strategic decisions (for example, if we invested in opening another factory, what effect would this have on our revenue?). Another important consideration in this step (and the next) is the validation of the OR model's solution. You should carefully consider what the solution means in terms of the original problem description. Make sure it makes sense to you and, more importantly, to your client. Hence, the next step, presenting the solution and analysis is very important.
3. Presenting the solution and analysis A crucial step in the optimisation process is the presentation of the solution and any analysis. The translation from an OR model's solution back into a concise and comprehensible summary is as important as the translation from the problem description into the OR model. In the case studies throughout this TWiki, we encourage the use of management summaries to present solutions and analysis from OR models. Key observations and/or decisions generated via OR must be presented in a way that is easy for the client or project stakeholders to understand. Your presentation is also a crucial first step in the implementation of any decisions generated by your OR model. If the results of your OR model and their consequences are not presented clearly and intelligently these results will never be used. This step is also your chance to suggest future work possibilities. This could include:

#### Revision 82008-03-02 - MichaelOSullivan

Line: 1 to 1

 META TOPICPARENT name="WebHome"
`<-- Ready to Review -->`

# Operations Research Methodology

Line: 20 to 20

1. Describing the Problem The aim of this step is to come up with a formal, rigorous model description. Usually you start an optimisation project with an abstract description of a problem and some data. Often you need to spend some time talking with the person providing the problem (usually known as the client). By talking with the client and considering the data available you can come up with a more rigorous model description required for formulation. Sometimes not all the data will be relevant or you will need to ask the client if they can provide some other data. Sometimes the limitations of the available data may significantly change your model description and subsequent formulation.
2. Formulating the OR Model The aim of this step is to translate the problem description into a valid OR model. The implementation of this step may be quite different depending on the OR model you are using. For example, if you are using linear programming to solve your problem, then formulating an OR model involves translating your problem into a linear programme. If you are using simulation to solve your problem, then formulating an OR model entails breaking down the behaviour of the system being simulated into a sequence of events and determining the random variables that "drive" the simulation.
3. Solving the OR Model The aim of this step is to solve your OR model. Just as the formulation step depended on the OR model being used, this solution step depends on your OR model. Additionally, there may be more than one solution method for a particular OR model. For example, solving a linear programme may be done using the Revised Simplex Method or an interior point method. Often, in practice, OR models may not be solved completely due to time constraints. Other algorithms may partially solve OR models (for optimisation models, these algorithms are known as heuristics and terminate with a "good" solution that is not necessarily optimal).
Changed:
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<
1. Performing analysis of the solution Often there is uncertainty in the problem description (either with the accuracy of the data provided, or with the value(s) of data in the future). In this situation the robustness of our solution to the OR model is the solution to the OR model or to the OR problem> - Lauren can be examined using analysis. Analysis involves identifying how the solution would change under various changes to the problem data (for example, what would be the effect of a given cost increasing, or a particular machine failing?). This sort of analysis can also be useful for making tactical or strategic decisions (for example, if we invested in opening another factory, what effect would this have on our revenue?). Another important consideration in this step (and the next) is the validation of the OR model's solution. You should carefully consider what the solution means in terms of the original problem description. Make sure it makes sense to you and, more importantly, to your client. Hence, the next step, presenting the solution and analysis is very important.
>
>
1. Performing analysis of the solution Often there is uncertainty in the problem description (either with the accuracy of the data provided, or with the value(s) of data in the future). In this situation the robustness of our solution to the OR model can be examined using analysis. Analysis involves identifying how the solution would change under various changes to the problem data (for example, what would be the effect of a given cost increasing, or a particular machine failing?). This sort of analysis can also be useful for making tactical or strategic decisions (for example, if we invested in opening another factory, what effect would this have on our revenue?). Another important consideration in this step (and the next) is the validation of the OR model's solution. You should carefully consider what the solution means in terms of the original problem description. Make sure it makes sense to you and, more importantly, to your client. Hence, the next step, presenting the solution and analysis is very important.

1. Presenting the solution and analysis A crucial step in the optimisation process is the presentation of the solution and any analysis. The translation from an OR model's solution back into a concise and comprehensible summary is as important as the translation from the problem description into the OR model. In the case studies throughout this TWiki, we encourage the use of management summaries to present solutions and analysis from OR models. Key observations and/or decisions generated via OR must be presented in a way that is easy for the client or project stakeholders to understand. Your presentation is also a crucial first step in the implementation of any decisions generated by your OR model. If the results of your OR model and their consequences are not presented clearly and intelligently these results will never be used. This step is also your chance to suggest future work possibilities. This could include:
• Periodic monitoring of the validity of your OR Model;
• Further analysis of your solution, looking for other benefits for your client;

#### Revision 72008-03-02 - MichaelOSullivan

Line: 1 to 1

 META TOPICPARENT name="WebHome"
`<-- Ready to Review -->`

# Operations Research Methodology

Solving an Operations Research (OR) problem is not a linear process, but the process can be broken down into five general steps:

Changed:
<
<
1. Getting the problem description; Can you think of a more elegant word than "Getting"? - Lauren
>
>
1. Describing the problem;

1. Formulating the OR model;
2. Solving the OR model;
3. Performing some analysis of the solution;
Line: 17 to 17
Let's consider the five steps of the OR methodology in more detail:
Changed:
<
<
1. Getting Obtaining? Describing the Problem? Composing the Problem Description? - Lauren the Problem Description The aim of this step is to come up with a formal, rigorous model description. Usually you start an optimisation project with an abstract description of a problem and some data. Often you need to spend some time talking with the person providing the problem (usually known as the client). By talking with the client and considering the data available you can come up with a more rigorous model description required for formulation. Sometimes not all the data will be relevant or you will need to ask the client if they can provide some other data. Sometimes the limitations of the available data may I would write "significantly" here and cut it from the end of the sentence - Lauren change your model description and subsequent formulation significantly.
>
>
1. Describing the Problem The aim of this step is to come up with a formal, rigorous model description. Usually you start an optimisation project with an abstract description of a problem and some data. Often you need to spend some time talking with the person providing the problem (usually known as the client). By talking with the client and considering the data available you can come up with a more rigorous model description required for formulation. Sometimes not all the data will be relevant or you will need to ask the client if they can provide some other data. Sometimes the limitations of the available data may significantly change your model description and subsequent formulation.

1. Formulating the OR Model The aim of this step is to translate the problem description into a valid OR model. The implementation of this step may be quite different depending on the OR model you are using. For example, if you are using linear programming to solve your problem, then formulating an OR model involves translating your problem into a linear programme. If you are using simulation to solve your problem, then formulating an OR model entails breaking down the behaviour of the system being simulated into a sequence of events and determining the random variables that "drive" the simulation.
2. Solving the OR Model The aim of this step is to solve your OR model. Just as the formulation step depended on the OR model being used, this solution step depends on your OR model. Additionally, there may be more than one solution method for a particular OR model. For example, solving a linear programme may be done using the Revised Simplex Method or an interior point method. Often, in practice, OR models may not be solved completely due to time constraints. Other algorithms may partially solve OR models (for optimisation models, these algorithms are known as heuristics and terminate with a "good" solution that is not necessarily optimal).
Changed:
<
<
1. Performing analysis of the solution Often there is uncertainty in the problem description (either with the accuracy of the data provided, or with the value(s) of data in the future). In this situation the robustness of our solution to the OR model is the solution to the OR model or to the OR problem> - Lauren can be examined using analysis. Analysis involves identifying how the solution would change under various changes to the problem data (for example, what would be the effect of a given cost increasing, or a particular machine failing?). This sort of analysis can also be useful for making tactical or strategic decisions (for example, if we invested in opening another factory, what effect would this have on our revenue?). Another important consideration in this step (and the next) is the validation of the OR model's solution. You should carefully consider what the solution means in terms of the original problem description. Make sure it makes sense to you and, more importantly, to - Lauren your client (which is why the next step, presenting the solution and analysis is very important). Bracketed text seems clumsy here - would be better as a stand-alone sentence - Lauren
2. Presenting the solution and analysis A crucial step in the optimisation process is the presentation of the solution and any analysis. The translation from an OR model's solution back into a concise and comprehensible summary is as important as the translation from the problem description into the OR model. In the case studies throughout this TWiki, we encourage the use of management summaries to present solutions and analysis from OR models. Key observations and/or decisions generated via OR must be presented in a way that is easy for the client or project stakeholders to understand - Lauren an easily understandable way for the client or project stakeholders. Your presentation is also a crucial first step in the implementation of any decisions generated by your OR model. If the results of your OR model and their consequences are not presented clearly and intelligently these results will never be used. This step is also your chance to suggest future work possibilities - Lauren other work in the future. This could include:
>
>
1. Performing analysis of the solution Often there is uncertainty in the problem description (either with the accuracy of the data provided, or with the value(s) of data in the future). In this situation the robustness of our solution to the OR model is the solution to the OR model or to the OR problem> - Lauren can be examined using analysis. Analysis involves identifying how the solution would change under various changes to the problem data (for example, what would be the effect of a given cost increasing, or a particular machine failing?). This sort of analysis can also be useful for making tactical or strategic decisions (for example, if we invested in opening another factory, what effect would this have on our revenue?). Another important consideration in this step (and the next) is the validation of the OR model's solution. You should carefully consider what the solution means in terms of the original problem description. Make sure it makes sense to you and, more importantly, to your client. Hence, the next step, presenting the solution and analysis is very important.
2. Presenting the solution and analysis A crucial step in the optimisation process is the presentation of the solution and any analysis. The translation from an OR model's solution back into a concise and comprehensible summary is as important as the translation from the problem description into the OR model. In the case studies throughout this TWiki, we encourage the use of management summaries to present solutions and analysis from OR models. Key observations and/or decisions generated via OR must be presented in a way that is easy for the client or project stakeholders to understand. Your presentation is also a crucial first step in the implementation of any decisions generated by your OR model. If the results of your OR model and their consequences are not presented clearly and intelligently these results will never be used. This step is also your chance to suggest future work possibilities. This could include:

• Periodic monitoring of the validity of your OR Model;
• Further analysis of your solution, looking for other benefits for your client;
• Identification of future OR opportunities.
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#### Revision 62008-02-25 - LaurenJackson

Line: 1 to 1

 META TOPICPARENT name="WebHome"
`<-- Ready to Review -->`

# Operations Research Methodology

Solving an Operations Research (OR) problem is not a linear process, but the process can be broken down into five general steps:

Changed:
<
<
1. Getting the problem description;
>
>
1. Getting the problem description; Can you think of a more elegant word than "Getting"? - Lauren

1. Formulating the OR model;
2. Solving the OR model;
3. Performing some analysis of the solution;
Line: 17 to 17
Let's consider the five steps of the OR methodology in more detail:
Changed:
<
<
1. Getting the Problem Description The aim of this step is to come up with a formal, rigourous model description. Usually you start an optimisation project with an abstract description of a problem and some data. Often you need to spend some time talking with the person providing the problem (usually known as the client). By talking with the client and considering the data available you can come up with a more rigourous model description required for formulation. Sometimes not all the data will be relevant or you will need to ask the client if they can provide some other data. Sometimes the limitations of the available data may change your model description and subsequent formulation significantly.
>
>
1. Getting Obtaining? Describing the Problem? Composing the Problem Description? - Lauren the Problem Description The aim of this step is to come up with a formal, rigorous model description. Usually you start an optimisation project with an abstract description of a problem and some data. Often you need to spend some time talking with the person providing the problem (usually known as the client). By talking with the client and considering the data available you can come up with a more rigorous model description required for formulation. Sometimes not all the data will be relevant or you will need to ask the client if they can provide some other data. Sometimes the limitations of the available data may I would write "significantly" here and cut it from the end of the sentence - Lauren change your model description and subsequent formulation significantly.

1. Formulating the OR Model The aim of this step is to translate the problem description into a valid OR model. The implementation of this step may be quite different depending on the OR model you are using. For example, if you are using linear programming to solve your problem, then formulating an OR model involves translating your problem into a linear programme. If you are using simulation to solve your problem, then formulating an OR model entails breaking down the behaviour of the system being simulated into a sequence of events and determining the random variables that "drive" the simulation.
2. Solving the OR Model The aim of this step is to solve your OR model. Just as the formulation step depended on the OR model being used, this solution step depends on your OR model. Additionally, there may be more than one solution method for a particular OR model. For example, solving a linear programme may be done using the Revised Simplex Method or an interior point method. Often, in practice, OR models may not be solved completely due to time constraints. Other algorithms may partially solve OR models (for optimisation models, these algorithms are known as heuristics and terminate with a "good" solution that is not necessarily optimal).
Changed:
<
<
1. Performing analysis of the solution Often there is uncertainty in the problem description (either with the accuracy of the data provided, or with the value(s) of data in the future). In this situation the robustness of our solution to the OR model can be examined using analysis. Analysis involves identifying how the solution would change under various changes to the problem data (for example, what would be the effect of a given cost increasing, or a particular machine failing?). This sort of analysis can also be useful for making tactical or strategic decisions (for example, if we invested in opening another factory, what effect would this have on our revenue?). Another important consideration in this step (and the next) is the validation of the OR model's solution. You should carefully consider what the solution means in terms of the original problem description. Make sure it makes sense to you and, more importantly, your client (which is why the next step, presenting the solution and analysis is very important).
2. Presenting the solution and analysis A crucial step in the optimisation process is the presentation of the solution and any analysis. The translation from an OR model's solution back into a concise and comprehensible summary is as important as the translation from the problem description into the OR model. In the case studies throughout this TWiki, we encourage the use of management summaries to present solutions and analysis from OR models. Key observations and/or decisions generated via OR must be presented in an easily understandable way for the client or project stakeholders. Your presentation is also a crucial first step in the implementation of any decisions generated by your OR model. If the results of your OR model and their consequences are not presented clearly and intelligently these results will never be used. This step is also your chance to suggest other work in the future. This could include:
>
>
1. Performing analysis of the solution Often there is uncertainty in the problem description (either with the accuracy of the data provided, or with the value(s) of data in the future). In this situation the robustness of our solution to the OR model is the solution to the OR model or to the OR problem> - Lauren can be examined using analysis. Analysis involves identifying how the solution would change under various changes to the problem data (for example, what would be the effect of a given cost increasing, or a particular machine failing?). This sort of analysis can also be useful for making tactical or strategic decisions (for example, if we invested in opening another factory, what effect would this have on our revenue?). Another important consideration in this step (and the next) is the validation of the OR model's solution. You should carefully consider what the solution means in terms of the original problem description. Make sure it makes sense to you and, more importantly, to - Lauren your client (which is why the next step, presenting the solution and analysis is very important). Bracketed text seems clumsy here - would be better as a stand-alone sentence - Lauren
2. Presenting the solution and analysis A crucial step in the optimisation process is the presentation of the solution and any analysis. The translation from an OR model's solution back into a concise and comprehensible summary is as important as the translation from the problem description into the OR model. In the case studies throughout this TWiki, we encourage the use of management summaries to present solutions and analysis from OR models. Key observations and/or decisions generated via OR must be presented in a way that is easy for the client or project stakeholders to understand - Lauren an easily understandable way for the client or project stakeholders. Your presentation is also a crucial first step in the implementation of any decisions generated by your OR model. If the results of your OR model and their consequences are not presented clearly and intelligently these results will never be used. This step is also your chance to suggest future work possibilities - Lauren other work in the future. This could include:

• Periodic monitoring of the validity of your OR Model;
• Further analysis of your solution, looking for other benefits for your client;
• Identification of future OR opportunities.
\ No newline at end of file

#### Revision 52008-02-21 - MichaelOSullivan

Line: 1 to 1

 META TOPICPARENT name="WebHome"
>
>
`<-- Ready to Review -->`

# Operations Research Methodology

Solving an Operations Research (OR) problem is not a linear process, but the process can be broken down into five general steps:

Changed:
<
<
1. Getting the problem description;
2. Formulating the OR model;
3. Solving the OR model;
4. Performing some analysis of the solution;
5. Presenting the solution and analysis.
>
>
1. Getting the problem description;
2. Formulating the OR model;
3. Solving the OR model;
4. Performing some analysis of the solution;
5. Presenting the solution and analysis.
However, there are often "feedback loops" within this process. For example, after modelling and solving an OR problem, you will often want to consider the validity of your solution (often consulting with the person who provided the problem description). If your solution is invalid you may need to alter or update your formulation to incorporate your new understanding of the actual problem.
Line: 16 to 17
Let's consider the five steps of the OR methodology in more detail:
Changed:
<
<
1. Getting the Problem Description The aim of this step is to come up with a formal, rigourous model description. Usually you start an optimisation project with an abstract description of a problem and some data. Often you need to spend some time talking with the person providing the problem (usually known as the client). By talking with the client and considering the data available you can come up with a more rigourous model description required for formulation. Sometimes not all the data will be relevant or you will need to ask the client if they can provide some other data. Sometimes the limitations of the available data may change your model description and subsequent formulation significantly.
2. Formulating the OR Model The aim of this step is to translate the problem description into a valid OR model. The implementation of this step may be quite different depending on the OR model you are using. For example, if you are using linear programming to solve your problem, then formulating an OR model involves translating your problem into a linear programme. If you are using simulation to solve your problem, then formulating an OR model entails breaking down the behaviour of the system being simulated into a sequence of events and determining the random variables that "drive" the simulation.
3. Solving the OR Model The aim of this step is to solve your OR model. Just as the formulation step depended on the OR model being used, this solution step depends on your OR model. Additionally, there may be more than one solution method for a particular OR model. For example, solving a linear programme may be done using the Revised Simplex Method or an interior point method. Often, in practice, OR models may not be solved completely due to time constraints. Other algorithms may partially solve OR models (for optimisation models, these algorithms are known as heuristics and terminate with a "good" solution that is not necessarily optimal).
4. Performing analysis of the solution Often there is uncertainty in the problem description (either with the accuracy of the data provided, or with the value(s) of data in the future). In this situation the robustness of our solution to the OR model can be examined using analysis. Analysis involves identifying how the solution would change under various changes to the problem data (for example, what would be the effect of a given cost increasing, or a particular machine failing?). This sort of analysis can also be useful for making tactical or strategic decisions (for example, if we invested in opening another factory, what effect would this have on our revenue?). Another important consideration in this step (and the next) is the validation of the OR model's solution. You should carefully consider what the solution means in terms of the original problem description. Make sure it makes sense to you and, more importantly, your client (which is why the next step, presenting the solution and analysis is very important).
5. Presenting the solution and analysis A crucial step in the optimisation process is the presentation of the solution and any analysis. The translation from an OR model's solution back into a concise and comprehensible summary is as important as the translation from the problem description into the OR model. In the case studies throughout this TWiki, we encourage the use of management summaries to present solutions and analysis from OR models. Key observations and/or decisions generated via OR must be presented in an easily understandable way for the client or project stakeholders. Your presentation is also a crucial first step in the implementation of any decisions generated by your OR model. If the results of your OR model and their consequences are not presented clearly and intelligently these results will never be used. This step is also your chance to suggest other work in the future. This could include:
>
>
1. Getting the Problem Description The aim of this step is to come up with a formal, rigourous model description. Usually you start an optimisation project with an abstract description of a problem and some data. Often you need to spend some time talking with the person providing the problem (usually known as the client). By talking with the client and considering the data available you can come up with a more rigourous model description required for formulation. Sometimes not all the data will be relevant or you will need to ask the client if they can provide some other data. Sometimes the limitations of the available data may change your model description and subsequent formulation significantly.
2. Formulating the OR Model The aim of this step is to translate the problem description into a valid OR model. The implementation of this step may be quite different depending on the OR model you are using. For example, if you are using linear programming to solve your problem, then formulating an OR model involves translating your problem into a linear programme. If you are using simulation to solve your problem, then formulating an OR model entails breaking down the behaviour of the system being simulated into a sequence of events and determining the random variables that "drive" the simulation.
3. Solving the OR Model The aim of this step is to solve your OR model. Just as the formulation step depended on the OR model being used, this solution step depends on your OR model. Additionally, there may be more than one solution method for a particular OR model. For example, solving a linear programme may be done using the Revised Simplex Method or an interior point method. Often, in practice, OR models may not be solved completely due to time constraints. Other algorithms may partially solve OR models (for optimisation models, these algorithms are known as heuristics and terminate with a "good" solution that is not necessarily optimal).
4. Performing analysis of the solution Often there is uncertainty in the problem description (either with the accuracy of the data provided, or with the value(s) of data in the future). In this situation the robustness of our solution to the OR model can be examined using analysis. Analysis involves identifying how the solution would change under various changes to the problem data (for example, what would be the effect of a given cost increasing, or a particular machine failing?). This sort of analysis can also be useful for making tactical or strategic decisions (for example, if we invested in opening another factory, what effect would this have on our revenue?). Another important consideration in this step (and the next) is the validation of the OR model's solution. You should carefully consider what the solution means in terms of the original problem description. Make sure it makes sense to you and, more importantly, your client (which is why the next step, presenting the solution and analysis is very important).
5. Presenting the solution and analysis A crucial step in the optimisation process is the presentation of the solution and any analysis. The translation from an OR model's solution back into a concise and comprehensible summary is as important as the translation from the problem description into the OR model. In the case studies throughout this TWiki, we encourage the use of management summaries to present solutions and analysis from OR models. Key observations and/or decisions generated via OR must be presented in an easily understandable way for the client or project stakeholders. Your presentation is also a crucial first step in the implementation of any decisions generated by your OR model. If the results of your OR model and their consequences are not presented clearly and intelligently these results will never be used. This step is also your chance to suggest other work in the future. This could include:

• Periodic monitoring of the validity of your OR Model;
• Further analysis of your solution, looking for other benefits for your client;
• Identification of future OR opportunities.
\ No newline at end of file

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# Operations Research Methodology

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Let's consider the five steps of the OR methodology in more detail:

1. Getting the Problem Description The aim of this step is to come up with a formal, rigourous model description. Usually you start an optimisation project with an abstract description of a problem and some data. Often you need to spend some time talking with the person providing the problem (usually known as the client). By talking with the client and considering the data available you can come up with a more rigourous model description required for formulation. Sometimes not all the data will be relevant or you will need to ask the client if they can provide some other data. Sometimes the limitations of the available data may change your model description and subsequent formulation significantly.
Changed:
<
<
1. Formulating the OR Model The aim of this step is to translate the problem description into a valid OR model. The implementation of this step may be quite different depending on the OR model you are using. For example, if you are using [LinearProgramming linear programming] to solve your problem, then formulating an OR model involves translating your problem into a linear programme. If you are using SimulationModelling to solve your problem, then formulating an OR model entails breaking down the behaviour of the system being simulated into a sequence of events and determining the random variables that "drive" the simulation.
2. Solving the OR Model The aim of this step is to solve your OR model. Just as the formulation step depended on the OR model being used, this solution step depends on your OR model. Additionally, there may be more than one solution method for a particular OR model. For example, solving a Linear Programming may be done using the Revised Simplex Method or an interior point method. Often, in practice, OR models may not be solved completely due to time constraints. Other algorithms may partially solve OR models (for optimisation models, these algorithms are known as heuristics and terminate with a "good" solution that is not necessarily optimal).
>
>
1. Formulating the OR Model The aim of this step is to translate the problem description into a valid OR model. The implementation of this step may be quite different depending on the OR model you are using. For example, if you are using linear programming to solve your problem, then formulating an OR model involves translating your problem into a linear programme. If you are using simulation to solve your problem, then formulating an OR model entails breaking down the behaviour of the system being simulated into a sequence of events and determining the random variables that "drive" the simulation.
2. Solving the OR Model The aim of this step is to solve your OR model. Just as the formulation step depended on the OR model being used, this solution step depends on your OR model. Additionally, there may be more than one solution method for a particular OR model. For example, solving a linear programme may be done using the Revised Simplex Method or an interior point method. Often, in practice, OR models may not be solved completely due to time constraints. Other algorithms may partially solve OR models (for optimisation models, these algorithms are known as heuristics and terminate with a "good" solution that is not necessarily optimal).

1. Performing analysis of the solution Often there is uncertainty in the problem description (either with the accuracy of the data provided, or with the value(s) of data in the future). In this situation the robustness of our solution to the OR model can be examined using analysis. Analysis involves identifying how the solution would change under various changes to the problem data (for example, what would be the effect of a given cost increasing, or a particular machine failing?). This sort of analysis can also be useful for making tactical or strategic decisions (for example, if we invested in opening another factory, what effect would this have on our revenue?). Another important consideration in this step (and the next) is the validation of the OR model's solution. You should carefully consider what the solution means in terms of the original problem description. Make sure it makes sense to you and, more importantly, your client (which is why the next step, presenting the solution and analysis is very important).
Changed:
<
<
1. Presenting the solution and analysis A crucial step in the optimisation process is the presentation of the solution and any analysis. The translation from an OR model's solution back into a concise and comprehensible summary is as important as the translation from the problem description into the OR model. In the case studies throughout this TWiki, we encourage the use of ManagementSummary to present solutions and analysis from OR models. Key observations and/or decisions generated via OR must be presented in an easily understandable way for the client or project stakeholders. Your presentation is also a crucial first step in the implementation of any decisions generated by your OR model. If the results of your OR model and their consequences are not presented clearly and intelligently these results will never be used. This step is also your chance to suggest other work in the future. This could include:
>
>
1. Presenting the solution and analysis A crucial step in the optimisation process is the presentation of the solution and any analysis. The translation from an OR model's solution back into a concise and comprehensible summary is as important as the translation from the problem description into the OR model. In the case studies throughout this TWiki, we encourage the use of management summaries to present solutions and analysis from OR models. Key observations and/or decisions generated via OR must be presented in an easily understandable way for the client or project stakeholders. Your presentation is also a crucial first step in the implementation of any decisions generated by your OR model. If the results of your OR model and their consequences are not presented clearly and intelligently these results will never be used. This step is also your chance to suggest other work in the future. This could include:

• Periodic monitoring of the validity of your OR Model;
• Further analysis of your solution, looking for other benefits for your client;
• Identification of future OR opportunities.

#### Revision 32008-02-20 - MichaelOSullivan

Line: 1 to 1

 META TOPICPARENT name="WebHome"

# Operations Research Methodology

Solving an Operations Research (OR) problem is not a linear process, but the process can be broken down into five general steps:

Changed:
<
<
1. Getting the problem description;
2. Formulating an OR model;
3. Solving the OR model;
4. Performing some analysis of the solution;
5. Presenting the solution and analysis.
>
>
1. Getting the problem description;
2. Formulating the OR model;
3. Solving the OR model;
4. Performing some analysis of the solution;
5. Presenting the solution and analysis.
However, there are often "feedback loops" within this process. For example, after modelling and solving an OR problem, you will often want to consider the validity of your solution (often consulting with the person who provided the problem description). If your solution is invalid you may need to alter or update your formulation to incorporate your new understanding of the actual problem.

All the case studies on this TWiki (should!) follow the OR methodology. The Problem Description and Problem Formulation sections correspond to steps 1. and 2. respectively. The Computational Model section shows how some OR software was used to solve the problem, the Results section contains the solution and the analysis and the Conclusion section presents the solution and analysis.

Changed:
<
<
You may have encountered parts of the OR methodology in introductory OR classes ( link: ??? Mike to link to ENGSCI 255/STATS 255 ??? ). The Modelling Process
>
>
Let's consider the five steps of the OR methodology in more detail:

Changed:
<
<
TheYou may have already seen This process is
shown in the _Operations Research Methodology Diagram_.
Note that we have altered the original diagram (from STATS 255) to reflect the use of AMPL.

In ENGSCI 255 and STATS 255 you are taught mostly about the _Modeling Process_.
The modeling process starts with a well-defined model description, then uses mathematics to formulate a mathematical programme.
Next, the modeler enters the mathematical programme into some solver software, e.g., Excel or Storm, and solves the model. Finally,
the solution is translated into a decision in terms of the original model description.

Using AMPL (or another mathematical programming language) gives you a "shortcut" through the modeling process. By formulating
the mathematical programme in AMPL you have already put it into a form that can be used easily by many solvers, e.g., CPLEX, MINOS,
so you don't need to enter the mathematical programme into the solver software. However, you usually don't put any "hard" numbers
into your formulation, instead you "populate" your model using data files, so there is some work involved in creating the appropriate data
file. The advantage of using data files is that the same model may used many times with different data sets (see
The AMPL Process for more detail).

### The Modeling Process

The modeling process is a "neat and tidy" simplification of the optimisation process. Let's consider the five steps of the optimisation
process in more detail:
1. Getting the Problem Description The aim of this step is to come up with a formal, rigourous model description.
Usually you start an optimisation project with an abstract description of a problem and some data. Often you need to spend some time
talking with the person providing the problem (usually known as the client). By talking with the client and considering the data
available you can come up with the more rigourous model description you are used to. Sometimes not all the data will be relevant or you
will need to ask the client if they can provide some other data. Sometimes the limitations of the available data may change your model
description and subsequent formulation significantly.
2. Formulating the mathematical programme In this step we identify the key quantifiable decisions, restrictions and goals from the problem description, and capture their interdependencies in a mathematical model. We can break the formulation process into 4 key steps:
1. Identify the Decision Variables paying particular attention to units (for example: we need to decide how many hours per week each process will run for).
2. Formulate the Objective Function using the decision variables, we can construct a minimise or maximise objective function. The objective function typically reflects the total cost, or total profit, for a given value of the decision variables.
3. Formulate the Constraints, either logical (for example, we cannot work for a negative number of hours), or explicit to the problem description. Again, the constraints are expressed in terms of the decision variables.
4. Identify the Data needed for the objective function and constraints. To solve your mathematical programme you will need to have some "hard numbers" as variable bounds and/or variable coefficients in your objective function and/or constraints.

1. Solving the mathematical programme For relatively simple or well understood problems the mathematical model can often be solved to optimality (i.e., the best possible solution is identified). This is done using algorithms such as the Revised Simplex Method (see ENGSCI 391) or Interior Point Methods (see ENGSCI 768). However, many industrial problems would take too long to solve to optimality using these techniques, and so are solved using heuristic methods (such as Tabu search and Simulated Annealing - see ENGSCI 760) which do not guarantee optimality.
2. Performing some post-optimal analysis Often there is uncertainty in the problem description (either with the accuracy of the data provided, or with the value(s) of data in the future). In this situation the robustness of our solution can be examined by performing post-optimal analysis. This involves identifying how the optimal solution would change under various changes to the formulation (for example, what would be the effect of a given cost increasing, or a particular machine failing?). This sort of analysis can also be useful for making tactical or strategic decisions (for example, if we invested in opening another factory, what effect would this have on our revenue?).

Another important consideration in this step (and the next) is the validation of the mathematical programme's solution. You should carefully consider what the solution's variable values mean in terms of the original problem description. Make sure they make sense to you and, more importantly, your client (which is why the next step, presenting the solution and analysis is important).
3. Presenting the solution and analysis A crucial step in the optimisation process is the presentation of the solution and any post-optimal analysis. The translation from a mathematical programme's solution back into a concise and comprehensible summary is as important as the translation from the problem description into the mathematical programme. Key observations and decisions generated via optimisation must be presented in an easily understandable way
for the client or project stakeholders.

Your presentation is a crucial first step in the implementation of the decisions generated by your mathematical programme. If the decisions and their consequences
(often determined by the mathematical programme constraints) are not presented clearly and intelligently your optimal decision will never be used.

This step is also your chance to suggest other work in the future. This could include:
• Periodic monitoring of the validity of your mathematical programme;
>
>
1. Getting the Problem Description The aim of this step is to come up with a formal, rigourous model description. Usually you start an optimisation project with an abstract description of a problem and some data. Often you need to spend some time talking with the person providing the problem (usually known as the client). By talking with the client and considering the data available you can come up with a more rigourous model description required for formulation. Sometimes not all the data will be relevant or you will need to ask the client if they can provide some other data. Sometimes the limitations of the available data may change your model description and subsequent formulation significantly.
2. Formulating the OR Model The aim of this step is to translate the problem description into a valid OR model. The implementation of this step may be quite different depending on the OR model you are using. For example, if you are using [LinearProgramming linear programming] to solve your problem, then formulating an OR model involves translating your problem into a linear programme. If you are using SimulationModelling to solve your problem, then formulating an OR model entails breaking down the behaviour of the system being simulated into a sequence of events and determining the random variables that "drive" the simulation.
3. Solving the OR Model The aim of this step is to solve your OR model. Just as the formulation step depended on the OR model being used, this solution step depends on your OR model. Additionally, there may be more than one solution method for a particular OR model. For example, solving a Linear Programming may be done using the Revised Simplex Method or an interior point method. Often, in practice, OR models may not be solved completely due to time constraints. Other algorithms may partially solve OR models (for optimisation models, these algorithms are known as heuristics and terminate with a "good" solution that is not necessarily optimal).
4. Performing analysis of the solution Often there is uncertainty in the problem description (either with the accuracy of the data provided, or with the value(s) of data in the future). In this situation the robustness of our solution to the OR model can be examined using analysis. Analysis involves identifying how the solution would change under various changes to the problem data (for example, what would be the effect of a given cost increasing, or a particular machine failing?). This sort of analysis can also be useful for making tactical or strategic decisions (for example, if we invested in opening another factory, what effect would this have on our revenue?). Another important consideration in this step (and the next) is the validation of the OR model's solution. You should carefully consider what the solution means in terms of the original problem description. Make sure it makes sense to you and, more importantly, your client (which is why the next step, presenting the solution and analysis is very important).
5. Presenting the solution and analysis A crucial step in the optimisation process is the presentation of the solution and any analysis. The translation from an OR model's solution back into a concise and comprehensible summary is as important as the translation from the problem description into the OR model. In the case studies throughout this TWiki, we encourage the use of ManagementSummary to present solutions and analysis from OR models. Key observations and/or decisions generated via OR must be presented in an easily understandable way for the client or project stakeholders. Your presentation is also a crucial first step in the implementation of any decisions generated by your OR model. If the results of your OR model and their consequences are not presented clearly and intelligently these results will never be used. This step is also your chance to suggest other work in the future. This could include:
• Periodic monitoring of the validity of your OR Model;

• Further analysis of your solution, looking for other benefits for your client;
Changed:
<
<
• Identification of future optimisation opportunities.

-- MichaelOSullivan - 16 Feb 2008

>
>
• Identification of future OR opportunities.

#### Revision 22008-02-19 - MichaelOSullivan

Line: 1 to 1

 META TOPICPARENT name="WebHome"
Changed:
<
<
Solving an optimisation problem is not a linear process, but the process can be broken down into five general steps:

1. Getting the problem description
2. Formulating the mathematical programme
3. Solving the mathematical programme
4. Performing some post-optimal analysis
5. Presenting the solution and analysis

However, there are often "feedback loops" within this process. For example,
after formulating and solving an optimisation problem, you will often want to consider
the validity of your solution (often consulting with the person who provided the
problem description). If your solution is invalid you may need to alter or update your
formulation to incorporate your new understanding of the actual problem. This process is
shown in the Operations Research Methodology Diagram.
Note that we have altered the original diagram (from STATS 255) to reflect the use of AMPL.

In ENGSCI 255 and STATS 255 you are taught mostly about the Modeling Process.
The modeling process starts with a well-defined model description, then uses mathematics to formulate a mathematical programme.
Next, the modeler enters the mathematical programme into some solver software, e.g., Excel or Storm, and solves the model. Finally,
the solution is translated into a decision in terms of the original model description.

Using AMPL (or another mathematical programming language) gives you a "shortcut" through the modeling process. By formulating
the mathematical programme in AMPL you have already put it into a form that can be used easily by many solvers, e.g., CPLEX, MINOS,
so you don't need to enter the mathematical programme into the solver software. However, you usually don't put any "hard" numbers
into your formulation, instead you "populate" your model using data files, so there is some work involved in creating the appropriate data
file. The advantage of using data files is that the same model may used many times with different data sets (see
The AMPL Process for more detail).

### The Modeling Process

The modeling process is a "neat and tidy" simplification of the optimisation process. Let's consider the five steps of the optimisation
process in more detail:

1. Getting the Problem Description The aim of this step is to come up with a formal, rigourous model description.
Usually you start an optimisation project with an abstract description of a problem and some data. Often you need to spend some time
talking with the person providing the problem (usually known as the client). By talking with the client and considering the data
available you can come up with the more rigourous model description you are used to. Sometimes not all the data will be relevant or you
will need to ask the client if they can provide some other data. Sometimes the limitations of the available data may change your model
description and subsequent formulation significantly.

2. Formulating the mathematical programme In this step we identify the key quantifiable decisions, restrictions and goals from the problem description, and capture their interdependencies in a mathematical model. We can break the formulation process into 4 key steps:

1. Identify the Decision Variables paying particular attention to units (for example: we need to decide how many hours per week each process will run for).

2. Formulate the Objective Function using the decision variables, we can construct a minimise or maximise objective function. The objective function typically reflects the total cost, or total profit, for a given value of the decision variables.

3. Formulate the Constraints, either logical (for example, we cannot work for a negative number of hours), or explicit to the problem description. Again, the constraints are expressed in terms of the decision variables.

4. Identify the Data needed for the objective function and constraints. To solve your mathematical programme you will need to have some "hard numbers" as variable bounds and/or variable coefficients in your objective function and/or constraints.

3. Solving the mathematical programme For relatively simple or well understood problems the mathematical model can often be solved to optimality (i.e., the best possible solution is identified). This is done using algorithms such as the Revised Simplex Method (see ENGSCI 391) or Interior Point Methods (see ENGSCI 768). However, many industrial problems would take too long to solve to optimality using these techniques, and so are solved using heuristic methods (such as Tabu search and Simulated Annealing - see ENGSCI 760) which do not guarantee optimality.

4. Performing some post-optimal analysis Often there is uncertainty in the problem description (either with the accuracy of the data provided, or with the value(s) of data in the future). In this situation the robustness of our solution can be examined by performing post-optimal analysis. This involves identifying how the optimal solution would change under various changes to the formulation (for example, what would be the effect of a given cost increasing, or a particular machine failing?). This sort of analysis can also be useful for making tactical or strategic decisions (for example, if we invested in opening another factory, what effect would this have on our revenue?).

Another important consideration in this step (and the next) is the validation of the mathematical programme's solution. You should carefully consider what the solution's variable values mean in terms of the original problem description. Make sure they make sense to you and, more importantly, your client (which is why the next step, presenting the solution and analysis is important).

5. Presenting the solution and analysis A crucial step in the optimisation process is the presentation of the solution and any post-optimal analysis. The translation from a mathematical programme's solution back into a concise and comprehensible summary is as important as the translation from the problem description into the mathematical programme. Key observations and decisions generated via optimisation must be presented in an easily understandable way
for the client or project stakeholders.

Your presentation is a crucial first step in the implementation of the decisions generated by your mathematical programme. If the decisions and their consequences
(often determined by the mathematical programme constraints) are not presented clearly and intelligently your optimal decision will never be used.

This step is also your chance to suggest other work in the future. This could include:

• Periodic monitoring of the validity of your mathematical programme;
• Further analysis of your solution, looking for other benefits for your client;
• Identification of future optimisation opportunities.

>
>

# Operations Research Methodology

Solving an Operations Research (OR) problem is not a linear process, but the process can be broken down into five general steps:

1. Getting the problem description;
2. Formulating an OR model;
3. Solving the OR model;
4. Performing some analysis of the solution;
5. Presenting the solution and analysis.

However, there are often "feedback loops" within this process. For example, after modelling and solving an OR problem, you will often want to consider the validity of your solution (often consulting with the person who provided the problem description). If your solution is invalid you may need to alter or update your formulation to incorporate your new understanding of the actual problem.

All the case studies on this TWiki (should!) follow the OR methodology. The Problem Description and Problem Formulation sections correspond to steps 1. and 2. respectively. The Computational Model section shows how some OR software was used to solve the problem, the Results section contains the solution and the analysis and the Conclusion section presents the solution and analysis.

You may have encountered parts of the OR methodology in introductory OR classes ( link: ??? Mike to link to ENGSCI 255/STATS 255 ??? ). The Modelling Process

TheYou may have already seen This process is
shown in the _Operations Research Methodology Diagram_.
Note that we have altered the original diagram (from STATS 255) to reflect the use of AMPL.

In ENGSCI 255 and STATS 255 you are taught mostly about the _Modeling Process_.
The modeling process starts with a well-defined model description, then uses mathematics to formulate a mathematical programme.
Next, the modeler enters the mathematical programme into some solver software, e.g., Excel or Storm, and solves the model. Finally,
the solution is translated into a decision in terms of the original model description.

Using AMPL (or another mathematical programming language) gives you a "shortcut" through the modeling process. By formulating
the mathematical programme in AMPL you have already put it into a form that can be used easily by many solvers, e.g., CPLEX, MINOS,
so you don't need to enter the mathematical programme into the solver software. However, you usually don't put any "hard" numbers
into your formulation, instead you "populate" your model using data files, so there is some work involved in creating the appropriate data
file. The advantage of using data files is that the same model may used many times with different data sets (see
The AMPL Process for more detail).

### The Modeling Process

The modeling process is a "neat and tidy" simplification of the optimisation process. Let's consider the five steps of the optimisation
process in more detail:
1. Getting the Problem Description The aim of this step is to come up with a formal, rigourous model description.
Usually you start an optimisation project with an abstract description of a problem and some data. Often you need to spend some time
talking with the person providing the problem (usually known as the client). By talking with the client and considering the data
available you can come up with the more rigourous model description you are used to. Sometimes not all the data will be relevant or you
will need to ask the client if they can provide some other data. Sometimes the limitations of the available data may change your model
description and subsequent formulation significantly.
2. Formulating the mathematical programme In this step we identify the key quantifiable decisions, restrictions and goals from the problem description, and capture their interdependencies in a mathematical model. We can break the formulation process into 4 key steps:
1. Identify the Decision Variables paying particular attention to units (for example: we need to decide how many hours per week each process will run for).
2. Formulate the Objective Function using the decision variables, we can construct a minimise or maximise objective function. The objective function typically reflects the total cost, or total profit, for a given value of the decision variables.
3. Formulate the Constraints, either logical (for example, we cannot work for a negative number of hours), or explicit to the problem description. Again, the constraints are expressed in terms of the decision variables.
4. Identify the Data needed for the objective function and constraints. To solve your mathematical programme you will need to have some "hard numbers" as variable bounds and/or variable coefficients in your objective function and/or constraints.

1. Solving the mathematical programme For relatively simple or well understood problems the mathematical model can often be solved to optimality (i.e., the best possible solution is identified). This is done using algorithms such as the Revised Simplex Method (see ENGSCI 391) or Interior Point Methods (see ENGSCI 768). However, many industrial problems would take too long to solve to optimality using these techniques, and so are solved using heuristic methods (such as Tabu search and Simulated Annealing - see ENGSCI 760) which do not guarantee optimality.
2. Performing some post-optimal analysis Often there is uncertainty in the problem description (either with the accuracy of the data provided, or with the value(s) of data in the future). In this situation the robustness of our solution can be examined by performing post-optimal analysis. This involves identifying how the optimal solution would change under various changes to the formulation (for example, what would be the effect of a given cost increasing, or a particular machine failing?). This sort of analysis can also be useful for making tactical or strategic decisions (for example, if we invested in opening another factory, what effect would this have on our revenue?).

Another important consideration in this step (and the next) is the validation of the mathematical programme's solution. You should carefully consider what the solution's variable values mean in terms of the original problem description. Make sure they make sense to you and, more importantly, your client (which is why the next step, presenting the solution and analysis is important).
3. Presenting the solution and analysis A crucial step in the optimisation process is the presentation of the solution and any post-optimal analysis. The translation from a mathematical programme's solution back into a concise and comprehensible summary is as important as the translation from the problem description into the mathematical programme. Key observations and decisions generated via optimisation must be presented in an easily understandable way
for the client or project stakeholders.

Your presentation is a crucial first step in the implementation of the decisions generated by your mathematical programme. If the decisions and their consequences
(often determined by the mathematical programme constraints) are not presented clearly and intelligently your optimal decision will never be used.

This step is also your chance to suggest other work in the future. This could include:
• Periodic monitoring of the validity of your mathematical programme;
• Further analysis of your solution, looking for other benefits for your client;
• Identification of future optimisation opportunities.
-- MichaelOSullivan - 16 Feb 2008 \ No newline at end of file

#### Revision 12008-02-16 - MichaelOSullivan

Line: 1 to 1
>
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Solving an optimisation problem is not a linear process, but the process can be broken down into five general steps:

1. Getting the problem description
2. Formulating the mathematical programme
3. Solving the mathematical programme
4. Performing some post-optimal analysis
5. Presenting the solution and analysis

However, there are often "feedback loops" within this process. For example,
after formulating and solving an optimisation problem, you will often want to consider
the validity of your solution (often consulting with the person who provided the
problem description). If your solution is invalid you may need to alter or update your
formulation to incorporate your new understanding of the actual problem. This process is
shown in the Operations Research Methodology Diagram.
Note that we have altered the original diagram (from STATS 255) to reflect the use of AMPL.

In ENGSCI 255 and STATS 255 you are taught mostly about the Modeling Process.
The modeling process starts with a well-defined model description, then uses mathematics to formulate a mathematical programme.
Next, the modeler enters the mathematical programme into some solver software, e.g., Excel or Storm, and solves the model. Finally,
the solution is translated into a decision in terms of the original model description.

Using AMPL (or another mathematical programming language) gives you a "shortcut" through the modeling process. By formulating
the mathematical programme in AMPL you have already put it into a form that can be used easily by many solvers, e.g., CPLEX, MINOS,
so you don't need to enter the mathematical programme into the solver software. However, you usually don't put any "hard" numbers
into your formulation, instead you "populate" your model using data files, so there is some work involved in creating the appropriate data
file. The advantage of using data files is that the same model may used many times with different data sets (see
The AMPL Process for more detail).

### The Modeling Process

The modeling process is a "neat and tidy" simplification of the optimisation process. Let's consider the five steps of the optimisation
process in more detail:

1. Getting the Problem Description The aim of this step is to come up with a formal, rigourous model description.
Usually you start an optimisation project with an abstract description of a problem and some data. Often you need to spend some time
talking with the person providing the problem (usually known as the client). By talking with the client and considering the data
available you can come up with the more rigourous model description you are used to. Sometimes not all the data will be relevant or you
will need to ask the client if they can provide some other data. Sometimes the limitations of the available data may change your model
description and subsequent formulation significantly.

2. Formulating the mathematical programme In this step we identify the key quantifiable decisions, restrictions and goals from the problem description, and capture their interdependencies in a mathematical model. We can break the formulation process into 4 key steps:

1. Identify the Decision Variables paying particular attention to units (for example: we need to decide how many hours per week each process will run for).

2. Formulate the Objective Function using the decision variables, we can construct a minimise or maximise objective function. The objective function typically reflects the total cost, or total profit, for a given value of the decision variables.

3. Formulate the Constraints, either logical (for example, we cannot work for a negative number of hours), or explicit to the problem description. Again, the constraints are expressed in terms of the decision variables.

4. Identify the Data needed for the objective function and constraints. To solve your mathematical programme you will need to have some "hard numbers" as variable bounds and/or variable coefficients in your objective function and/or constraints.

3. Solving the mathematical programme For relatively simple or well understood problems the mathematical model can often be solved to optimality (i.e., the best possible solution is identified). This is done using algorithms such as the Revised Simplex Method (see ENGSCI 391) or Interior Point Methods (see ENGSCI 768). However, many industrial problems would take too long to solve to optimality using these techniques, and so are solved using heuristic methods (such as Tabu search and Simulated Annealing - see ENGSCI 760) which do not guarantee optimality.

4. Performing some post-optimal analysis Often there is uncertainty in the problem description (either with the accuracy of the data provided, or with the value(s) of data in the future). In this situation the robustness of our solution can be examined by performing post-optimal analysis. This involves identifying how the optimal solution would change under various changes to the formulation (for example, what would be the effect of a given cost increasing, or a particular machine failing?). This sort of analysis can also be useful for making tactical or strategic decisions (for example, if we invested in opening another factory, what effect would this have on our revenue?).

Another important consideration in this step (and the next) is the validation of the mathematical programme's solution. You should carefully consider what the solution's variable values mean in terms of the original problem description. Make sure they make sense to you and, more importantly, your client (which is why the next step, presenting the solution and analysis is important).

5. Presenting the solution and analysis A crucial step in the optimisation process is the presentation of the solution and any post-optimal analysis. The translation from a mathematical programme's solution back into a concise and comprehensible summary is as important as the translation from the problem description into the mathematical programme. Key observations and decisions generated via optimisation must be presented in an easily understandable way
for the client or project stakeholders.

Your presentation is a crucial first step in the implementation of the decisions generated by your mathematical programme. If the decisions and their consequences
(often determined by the mathematical programme constraints) are not presented clearly and intelligently your optimal decision will never be used.

This step is also your chance to suggest other work in the future. This could include:

• Periodic monitoring of the validity of your mathematical programme;
• Further analysis of your solution, looking for other benefits for your client;
• Identification of future optimisation opportunities.

-- MichaelOSullivan - 16 Feb 2008

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