Simple Multi-Attribute Rating Technique (SMART) - Revision history

ChristopherBurgdorf: /* Abstract */

2021-02-28T22:40:12Z

Abstract

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	=Abstract=		=Abstract=
	Simple Multi-Attribute Rating technique (SMART) is a method for conducting Multi-Criteria Decision Analysis (MCDA), in which assessment and selection of the best project alternative, amongst several different alternatives, is based on a list of relevant criteria. MCDA is a relatively new method for assessing alternatives or projects and it stems from the science of operations research. MCDA differs from traditional evaluations methods, like the cost-benefit-analysis (CBA), in multiple ways. Where a traditional CBA compares costs and benefits on a monetary scale, MCDA allows the assessment of alternatives on both a monetary as well as non-monetary scale [1]. SMART is considered as one of the main techniques for MCDA and the overall purpose of SMART is generally to assist the decision maker when trying to choose the best option amongst several alternatives. SMART is based on a linear additive model, which means that a performance score for all individual alternatives can be calculated as the sum of the relative performance of each alternative on each identified evaluation criterion multiplied by the relative importance of that specific criterion. The technique therefore consist of three distinct elements: A set of alternatives, a set of criteria that the alternatives are to be evaluated on and the relative importance of these criteria, called the criteria weights. The best alternative is thus found by calculating a total performance score for each alternative and then selecting the one that reveals the highest total performance score. SMART provides a simple and intuitive method for supporting the decision maker. The method has become fairly popular in recent years and is used throughout many areas of application such as transportation and logistics, problem planning, project selection and manufacturing. SMART can be used as a supplement to other decision tools, such as CBA. [2]		Simple Multi-Attribute Rating technique (SMART) is a method for conducting Multi-Criteria Decision Analysis (MCDA), in which assessment and selection of the best project alternative, amongst several different alternatives, is based on a list of relevant criteria. MCDA is a relatively new method for assessing alternatives or projects and it stems from the science of operations research. MCDA differs from traditional evaluations methods, like the cost-benefit-analysis (CBA), in multiple ways. Where a traditional CBA compares costs and benefits on a monetary scale, MCDA allows the assessment of alternatives on both a monetary as well as non-monetary scale [1]. SMART is considered as one of the main techniques for MCDA and the overall purpose of SMART is generally to assist the decision maker when trying to choose the best option amongst several alternatives. SMART is based on a linear additive model, which means that a performance score for all individual alternatives can be calculated as the sum of the relative performance of each alternative on each identified evaluation criterion multiplied by the relative importance of that specific criterion. The technique therefore consist of three distinct elements: A set of alternatives, a set of criteria that the alternatives are to be evaluated on and the relative importance of these criteria, called the criteria weights. The best alternative is thus found by calculating a total performance score for each alternative and then selecting the one that reveals the highest total performance score. SMART provides a simple and intuitive method for supporting the decision maker [5]. The method has become fairly popular in recent years and is used throughout many areas of application such as transportation and logistics, problem planning, project selection and manufacturing. SMART can be used as a supplement to other decision tools, such as CBA. [2]

	=Big idea=		=Big idea=

ChristopherBurgdorf: /* Cons of SMART */

2021-02-28T22:39:55Z

Cons of SMART

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	=== Cons of SMART ===		=== Cons of SMART ===
	The assessment of value functions when evaluating the performance of alternatives over the criteria or the assignment of swing weights can be difficult in practice. This can result in an inaccurate model for the decision support or a model that does not correctly reflect the preferences of the decision maker. A general concern of MCDA, and not just SMART, is that the method cannot provide the decision maker with a definitive solution. The tool should bee seen as support for the decision making rather than a model teaching for a hidden truth. SMART is also quite a time and resource intensive, which can be frustrating. Besides this, there might arise multiple problems when assigning the evaluation criteria their weights and the decision maker should be aware that his or her elicitation of importance weights can change the solution significantly. Finally, it should be noted that the additive model has certain limitations and this means that SMART is not suitable for all decision problems. The model is not suitable when there is dependencies or interactions between the weights of some of the criteria.		The assessment of value functions when evaluating the performance of alternatives over the criteria or the assignment of swing weights can be difficult in practice. This can result in an inaccurate model for the decision support or a model that does not correctly reflect the preferences of the decision maker. A general concern of MCDA, and not just SMART, is that the method cannot provide the decision maker with a definitive solution. The tool should bee seen as support for the decision making rather than a model teaching for a hidden truth. SMART is also quite a time and resource intensive, which can be frustrating. Besides this, there might arise multiple problems when assigning the evaluation criteria their weights and the decision maker should be aware that his or her elicitation of importance weights can change the solution significantly. Finally, it should be noted that the additive model has certain limitations and this means that SMART is not suitable for all decision problems. The model is not suitable when there is dependencies or interactions between the weights of some of the criteria [2].

	=Annotated bibliography=		=Annotated bibliography=

ChristopherBurgdorf: /* Axioms */

2021-02-28T22:39:41Z

Axioms

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	=Limitations=		=Limitations=
	=== Axioms ===		=== Axioms ===
	This section concerns some of the limitations of SMART and MCDA as well as the application area. First of all, it is important to state that SMART makes use of a set of axioms and assumptions considering the preferences of the decision maker. The first axiom states that it is assumed that the decision maker is able to decide between two options, given he prefers one more than the other. This axiom is called decidability and concerns the decision maker's ability to make a decision between the alternatives. The next axiom is called transitivity. This states that if A is preferred to B and B is preferred to C, then A must be preferred to C as well. The third axiom is summation and it implies that if the decision maker prefers A over B and he prefers B over C, then the strength of preference for A over C is larger than that of A over B. The forth axiom is solvability and it concerns the measure of improvements in regard to the value functions. The fifth axiom is that there should be finite upper and lower bounds for the values assigned the alternatives. Besides this, mutual preference independence is assumed to exist between the defined evaluation criteria.		This section concerns some of the limitations of SMART and MCDA as well as the application area. First of all, it is important to state that SMART makes use of a set of axioms and assumptions considering the preferences of the decision maker. The first axiom states that it is assumed that the decision maker is able to decide between two options, given he prefers one more than the other. This axiom is called decidability and concerns the decision maker's ability to make a decision between the alternatives. The next axiom is called transitivity. This states that if A is preferred to B and B is preferred to C, then A must be preferred to C as well. The third axiom is summation and it implies that if the decision maker prefers A over B and he prefers B over C, then the strength of preference for A over C is larger than that of A over B. The forth axiom is solvability and it concerns the measure of improvements in regard to the value functions. The fifth axiom is that there should be finite upper and lower bounds for the values assigned the alternatives [2]. Besides this, mutual preference independence is assumed to exist between the defined evaluation criteria [1].

	=== Cons of SMART ===		=== Cons of SMART ===

ChristopherBurgdorf: /* 8: Sensitivity analysis and final decision */

2021-02-28T22:39:15Z

8: Sensitivity analysis and final decision

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	==8: Sensitivity analysis and final decision==		==8: Sensitivity analysis and final decision==
	Sensitivity analysis is importance in order to check the robustness of the obtained solution. Robustness of a solution describes how sensitive the solution is to minor changes of the decision variables, such as the weights assigned for each of the criteria. If the model proves to be relatively robust against changes made to some of the parameters, a conclusion might be drawn. However, if the solution differes from minor adjustments to some of the model parameters, a revision is necessary before any conclusion can be drawn.		Sensitivity analysis is importance in order to check the robustness of the obtained solution. Robustness of a solution describes how sensitive the solution is to minor changes of the decision variables, such as the weights assigned for each of the criteria. If the model proves to be relatively robust against changes made to some of the parameters, a conclusion might be drawn. However, if the solution differes from minor adjustments to some of the model parameters, a revision is necessary before any conclusion can be drawn [1].

	=Limitations=		=Limitations=

ChristopherBurgdorf: /* 7: Making a provisional decision based on calculated performance scores */

2021-02-28T22:39:02Z

7: Making a provisional decision based on calculated performance scores

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	==7: Making a provisional decision based on calculated performance scores==		==7: Making a provisional decision based on calculated performance scores==
	When the total performance has been calculated for all the alternatives, a provisional decision can be made. Here the performance of the different alternatives can be compared against one another and maybe adjustments to the model can be made if necessary.		When the total performance has been calculated for all the alternatives, a provisional decision can be made. Here the performance of the different alternatives can be compared against one another and maybe adjustments to the model can be made if necessary [2].

	==8: Sensitivity analysis and final decision==		==8: Sensitivity analysis and final decision==

ChristopherBurgdorf: /* Direct rating */

2021-02-28T22:38:52Z

Direct rating

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	==== Direct rating ====		==== Direct rating ====
	In direct rating, the decision maker is asked to rank the alternatives from most preferred to least preferred. The alternative that is the most preferred one received a.score of 100. The alternative that is least preferred received a score of 0. The rest of the alternatives are given scores between 0 and 100 that represents the relative improvement when moving from one alternative to another. When each of the alternatives have been evaluated on the first criteria, then the process is repeated for the other criteria.		In direct rating, the decision maker is asked to rank the alternatives from most preferred to least preferred. The alternative that is the most preferred one received a.score of 100. The alternative that is least preferred received a score of 0. The rest of the alternatives are given scores between 0 and 100 that represents the relative improvement when moving from one alternative to another. When each of the alternatives have been evaluated on the first criteria, then the process is repeated for the other criteria [1].

	==== Value functions ====		==== Value functions ====

ChristopherBurgdorf: /* The complex nature of decision problems and the use decision support */

2021-02-28T22:38:37Z

The complex nature of decision problems and the use decision support

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	=== What is MCDA? ===		=== What is MCDA? ===
	==== The complex nature of decision problems and the use decision support ====		==== The complex nature of decision problems and the use decision support ====
	Often decision problems involve a large number of objectives and the decision maker might face more information than he can comprehend at once. The decision maker might therefore feel the need to simplify the problem considerable or to make use of various heuristics in order to make a decision. However, this might affect the quality of the decision [2]. Decision analysis can be used to support the decision maker during the decision processes, when he is faced with multiple objectives. The idea behind decision support is to divide the problem into smaller and more tangible parts, analysing the smaller subproblems in detail and then recombining them into a final solution. By splitting the problems into smaller subproblems, the decision maker gains a deeper understanding of the overall problem. The main objective of the decision problem is to assist the decision maker during the decision process and to enable him/her to gain a deeper understanding of the decision problem [3].		Often decision problems involve a large number of objectives and the decision maker might face more information than he can comprehend at once. The decision maker might therefore feel the need to simplify the problem considerable or to make use of various heuristics in order to make a decision [6]. However, this might affect the quality of the decision [2]. Decision analysis can be used to support the decision maker during the decision processes, when he is faced with multiple objectives. The idea behind decision support is to divide the problem into smaller and more tangible parts, analysing the smaller subproblems in detail and then recombining them into a final solution. By splitting the problems into smaller subproblems, the decision maker gains a deeper understanding of the overall problem. The main objective of the decision problem is to assist the decision maker during the decision process and to enable him/her to gain a deeper understanding of the decision problem [3].

	MCDA is a relatively new approach for solving decision-making problems. The method tries to resolve some of the issues related to theoretical evaluation problems by taking an engineering approach, rather than an economics approach [1]. The overall objective of MCDA is to allow the decision maker to break down complex decision problems into smaller and more tangible subproblems, which can be solved independently and then combined again. MCDA generally consist of a number of steps which breaks down the decision problem into three different components: (1) the alternatives, (2) the criteria for evaluating the performance of the alternatives and (3) the relative importance of the defined criteria. The identification of alternatives and the evaluation criteria constitute the objective part of the method, while the criteria weights for modelling the preference structure of the decision maker is considered the subjective part of the analysis. The purpose of MCDA is not to derive some optimal solution to a problem, but rather to assist the decision maker during an often complicated process of making decisions concerning multiple aspects. Some of the most recognised benefits associated with MCDA include helping the decision maker to understand the decision-making problem in further detail, improve the satisfaction of the decision process itself, improve the quality of the decision and to increase productivity during the decision-making process. There is a wide verity of MCDA tool, however one of the more common tools, which is the tool that this article will focus on, is the Simple Multi-Attribute Ranking Technique (SMART) [4].		MCDA is a relatively new approach for solving decision-making problems. The method tries to resolve some of the issues related to theoretical evaluation problems by taking an engineering approach, rather than an economics approach [1]. The overall objective of MCDA is to allow the decision maker to break down complex decision problems into smaller and more tangible subproblems, which can be solved independently and then combined again. MCDA generally consist of a number of steps which breaks down the decision problem into three different components: (1) the alternatives, (2) the criteria for evaluating the performance of the alternatives and (3) the relative importance of the defined criteria. The identification of alternatives and the evaluation criteria constitute the objective part of the method, while the criteria weights for modelling the preference structure of the decision maker is considered the subjective part of the analysis. The purpose of MCDA is not to derive some optimal solution to a problem, but rather to assist the decision maker during an often complicated process of making decisions concerning multiple aspects. Some of the most recognised benefits associated with MCDA include helping the decision maker to understand the decision-making problem in further detail, improve the satisfaction of the decision process itself, improve the quality of the decision and to increase productivity during the decision-making process. There is a wide verity of MCDA tool, however one of the more common tools, which is the tool that this article will focus on, is the Simple Multi-Attribute Ranking Technique (SMART) [4].

ChristopherBurgdorf: /* 4: Numerical assessment of performance of each alternative on each criterion */

2021-02-28T22:38:14Z

4: Numerical assessment of performance of each alternative on each criterion

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	==4: Numerical assessment of performance of each alternative on each criterion==		==4: Numerical assessment of performance of each alternative on each criterion==
	Now that the evaluation criteria have been defined, the decision maker must evaluate the performance of each alternative on these criteria. The first step is to identify variables to identify the criteria. For instance, the criteria "Size" from the office example depicted in figure 1 can be represented by the variable square meters. When these variables have been defined, one should see which variables that are quantifiable and which variables that are not quantifiable. If the variables representing the criteria are quantifiable, values functions can be used to derive the benefits of the alternatives. Another approach is called direct rating, and this can be used for both quantifiable and not quantifiable variables.		Now that the evaluation criteria have been defined, the decision maker must evaluate the performance of each alternative on these criteria. The first step is to identify variables to identify the criteria. For instance, the criteria "Size" from the office example depicted in figure 1 can be represented by the variable square meters. When these variables have been defined, one should see which variables that are quantifiable and which variables that are not quantifiable. If the variables representing the criteria are quantifiable, values functions can be used to derive the benefits of the alternatives. Another approach is called direct rating, and this can be used for both quantifiable and not quantifiable variables [1].

	==== Direct rating ====		==== Direct rating ====

ChristopherBurgdorf: /* 3: Identification of relevant evaluation criteria */

2021-02-28T22:38:03Z

3: Identification of relevant evaluation criteria

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	== 3: Identification of relevant evaluation criteria==		== 3: Identification of relevant evaluation criteria==
	We need criteria in order to assess the performance of each of the alternatives that was defined in stage 2. The criteria are used to measure the performance of an alternative in terms of the overall objective of the decision maker. If the overall objective of the decision maker was to choose the infrastructure project that return the largest socio-economic benefits, then the criteria should be defined so that they make it possible to assess each alternative according to this objective. If the overall objective was to find the best possible office location for a company, then we would have to define a set of criteria that makes it possible to assess the different office alternatives. These criteria could be rent, closeness to customer, size etc. The important part of defining the criteria is they need to be assessed on a numeric scale. Often it is helpful to construct a value tree and break down the overall criteria into smaller parts that can be assessed more easily.		We need criteria in order to assess the performance of each of the alternatives that was defined in stage 2. The criteria are used to measure the performance of an alternative in terms of the overall objective of the decision maker. If the overall objective of the decision maker was to choose the infrastructure project that return the largest socio-economic benefits, then the criteria should be defined so that they make it possible to assess each alternative according to this objective. If the overall objective was to find the best possible office location for a company, then we would have to define a set of criteria that makes it possible to assess the different office alternatives. These criteria could be rent, closeness to customer, size etc. The important part of defining the criteria is they need to be assessed on a numeric scale. Often it is helpful to construct a value tree and break down the overall criteria into smaller parts that can be assessed more easily [2].

	[[File:ValueTree.png\|center\|thumb\|550px\|Figure 1: Example of a value tree when defining criteria for selection of a new office location.]]		[[File:ValueTree.png\|center\|thumb\|550px\|Figure 1: Example of a value tree when defining criteria for selection of a new office location.]]

ChristopherBurgdorf: /* 2: Identification of alternatives */

2021-02-28T22:37:47Z

2: Identification of alternatives

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	== 2: Identification of alternatives==		== 2: Identification of alternatives==
	This stage concerns the identification of the different alternatives or projects to be chosen among. This could for instance be different infrastructure projects or alternative office locations. Like stage 1, this stage is also relatively straight forward. However, the alternatives chosen for the analysis should still be chosen with care.		This stage concerns the identification of the different alternatives or projects to be chosen among. This could for instance be different infrastructure projects or alternative office locations. Like stage 1, this stage is also relatively straight forward. However, the alternatives chosen for the analysis should still be chosen with care [2].

	== 3: Identification of relevant evaluation criteria==		== 3: Identification of relevant evaluation criteria==