Reliability Importance and Optimized Reliability Allocation (Analytical) - Revision history

Lisa Hacker: Replaced content with '{{Template:bsbook|5}} =Component Reliability Importance= {{:Reliability Importance}} =Reliability Allocation= {{:Reliability Allocation}}'

2012-09-10T17:22:40Z

Replaced content with '{{Template:bsbook|5}} =Component Reliability Importance= {{:Reliability Importance}} =Reliability Allocation= {{:Reliability Allocation}}'

Show changes

Richard House: /* Quantifying the Cost/Penalty Function */

2012-09-04T20:16:11Z

Quantifying the Cost/Penalty Function

← Older revision		Revision as of 20:16, 4 September 2012
Line 74:		Line 74:
	*The function should be asymptotic to the maximum achievable reliability.<br>		*The function should be asymptotic to the maximum achievable reliability.<br>

	The following default cost function (also used in BlockSim) adheres to all of these conditions and acts like a penalty function for increasing a component's reliability. Furthermore, an exponential behavior for the cost is assumed since it should get exponentially more difficult to increase the reliability. See [[Appendix_B:_References \| ~~Mettas~~ [21]]].		The following default cost function (also used in BlockSim) adheres to all of these conditions and acts like a penalty function for increasing a component's reliability. Furthermore, an exponential behavior for the cost is assumed since it should get exponentially more difficult to increase the reliability. See Mettas [[Appendix_B:_References \| [21]]].

	::<math>{{C}_{i}}({{R}_{i}})={{e}^{(1-f)\cdot \tfrac{{{R}_{i}}-{{R}_{\min ,i}}}{{{R}_{\max ,i}}-{{R}_{i}}}}}\ </math>		::<math>{{C}_{i}}({{R}_{i}})={{e}^{(1-f)\cdot \tfrac{{{R}_{i}}-{{R}_{\min ,i}}}{{{R}_{\max ,i}}-{{R}_{i}}}}}\ </math>

Lisa Hacker: /* Other Notes on User-Defined Cost Functions */

2012-08-17T06:04:03Z

Other Notes on User-Defined Cost Functions

← Older revision		Revision as of 06:04, 17 August 2012
Line 179:		Line 179:
	This methodology could also be used to arrive at initial specifications for a set of components. In the prior examples, we assumed a current reliability for the components. One could repeat these steps by choosing an arbitrary (lower) initial reliability for each component, thus allowing the algorithm to travel up to the target. When doing this, it is important to keep in mind the fact that both the distance from the target (the distance from the initial arbitrary value and the target value) for each component is also a significant contributor to the final results, as presented in the prior example. If one wishes to arrive at the results using only the cost functions then it may be advantageous to set equal initial reliabilities for all components.		This methodology could also be used to arrive at initial specifications for a set of components. In the prior examples, we assumed a current reliability for the components. One could repeat these steps by choosing an arbitrary (lower) initial reliability for each component, thus allowing the algorithm to travel up to the target. When doing this, it is important to keep in mind the fact that both the distance from the target (the distance from the initial arbitrary value and the target value) for each component is also a significant contributor to the final results, as presented in the prior example. If one wishes to arrive at the results using only the cost functions then it may be advantageous to set equal initial reliabilities for all components.

	=Other Notes on User-Defined Cost Functions=		==Other Notes on User-Defined Cost Functions==
	The optimization method in BlockSim is a very powerful tool for allocating reliability to the components of a system while minimizing an overall cost of improvement. The default cost function in BlockSim was derived in order to model a general relationship between the cost and the component reliability. However, if actual cost information is available, then one can use the cost data instead of using the default function. Additionally, one can also view the feasibility in the default function as a measure of the difficulty in increasing the reliability of the component relative to the rest of the components to be optimized, assuming that they also follow the same cost function with the corresponding feasibility values. If fault tolerance is a viable option, a reliability cost function for adding parallel units can be developed as demonstrated previously.		The optimization method in BlockSim is a very powerful tool for allocating reliability to the components of a system while minimizing an overall cost of improvement. The default cost function in BlockSim was derived in order to model a general relationship between the cost and the component reliability. However, if actual cost information is available, then one can use the cost data instead of using the default function. Additionally, one can also view the feasibility in the default function as a measure of the difficulty in increasing the reliability of the component relative to the rest of the components to be optimized, assuming that they also follow the same cost function with the corresponding feasibility values. If fault tolerance is a viable option, a reliability cost function for adding parallel units can be developed as demonstrated previously.

Lisa Hacker: /* Implementing the Optimization */

2012-08-17T06:03:49Z

Implementing the Optimization

← Older revision		Revision as of 06:03, 17 August 2012
Line 155:		Line 155:
	[[Image:6.14.png\|center\|700px\|thumb\|Setting a user-defined feasibility function in BlockSim. Any user-defined equation can be entered as a function of ''R.'']]		[[Image:6.14.png\|center\|700px\|thumb\|Setting a user-defined feasibility function in BlockSim. Any user-defined equation can be entered as a function of ''R.'']]

	=Implementing the Optimization=		==Implementing the Optimization==
	As was mentioned earlier, there are two different methods of implementing the changes suggested by the reliability optimization routine: fault tolerance and fault avoidance. When the optimized component reliabilities have been determined, it does not matter which of the two methods is employed to realize the optimum reliability for the component in question. For example, suppose we have determined that a component must have its reliability for a certain mission time raised from 50% to 75%. The engineer must now decide how to go about implementing the increase in reliability. If the engineer decides to do this via fault avoidance, another component must be found (or the existing component must be redesigned) so that it will perform the same function with a higher reliability. On the other hand, if the engineer decides to go the fault tolerance route, the optimized reliability can be achieved merely by placing a second identical component in parallel with the first one.		As was mentioned earlier, there are two different methods of implementing the changes suggested by the reliability optimization routine: fault tolerance and fault avoidance. When the optimized component reliabilities have been determined, it does not matter which of the two methods is employed to realize the optimum reliability for the component in question. For example, suppose we have determined that a component must have its reliability for a certain mission time raised from 50% to 75%. The engineer must now decide how to go about implementing the increase in reliability. If the engineer decides to do this via fault avoidance, another component must be found (or the existing component must be redesigned) so that it will perform the same function with a higher reliability. On the other hand, if the engineer decides to go the fault tolerance route, the optimized reliability can be achieved merely by placing a second identical component in parallel with the first one.

Lisa Hacker: /* Setting Specifications */

2012-08-17T06:02:50Z

Setting Specifications

← Older revision		Revision as of 06:02, 17 August 2012
Line 176:		Line 176:
	{{:BlockSim_Allocation_Analysis_Example}}		{{:BlockSim_Allocation_Analysis_Example}}

	=Setting Specifications=		==Setting Specifications==
	This methodology could also be used to arrive at initial specifications for a set of components. In the prior examples, we assumed a current reliability for the components. One could repeat these steps by choosing an arbitrary (lower) initial reliability for each component, thus allowing the algorithm to travel up to the target. When doing this, it is important to keep in mind the fact that both the distance from the target (the distance from the initial arbitrary value and the target value) for each component is also a significant contributor to the final results, as presented in the prior example. If one wishes to arrive at the results using only the cost functions then it may be advantageous to set equal initial reliabilities for all components.		This methodology could also be used to arrive at initial specifications for a set of components. In the prior examples, we assumed a current reliability for the components. One could repeat these steps by choosing an arbitrary (lower) initial reliability for each component, thus allowing the algorithm to travel up to the target. When doing this, it is important to keep in mind the fact that both the distance from the target (the distance from the initial arbitrary value and the target value) for each component is also a significant contributor to the final results, as presented in the prior example. If one wishes to arrive at the results using only the cost functions then it may be advantageous to set equal initial reliabilities for all components.

Lisa Hacker: /* Examples */

2012-08-17T06:02:19Z

Examples

← Older revision		Revision as of 06:02, 17 August 2012
Line 172:		Line 172:
	::<math>C(R)=0.3756\cdot {{e}^{3.1972\cdot R}}</math>		::<math>C(R)=0.3756\cdot {{e}^{3.1972\cdot R}}</math>

	==Examples==		==Reliability Allocation Examples==

	{{:BlockSim_Allocation_Analysis_Example}}		{{:BlockSim_Allocation_Analysis_Example}}

Lisa Hacker: /* Defining a Feasibility Policy in BlockSim */

2012-08-17T06:01:51Z

Defining a Feasibility Policy in BlockSim

← Older revision		Revision as of 06:01, 17 August 2012
Line 137:		Line 137:
	With the cost equation defined, then the optimum values for <math>{{R}_{1}}\,\!</math> , <math>{{R}_{2}}\,\!</math> and <math>{{R}_{3}}\,\!</math> are the values that satisfy the reliability requirement, the second equation above, at the minimum cost, the last equation above. BlockSim uses this methodology during the optimization task.		With the cost equation defined, then the optimum values for <math>{{R}_{1}}\,\!</math> , <math>{{R}_{2}}\,\!</math> and <math>{{R}_{3}}\,\!</math> are the values that satisfy the reliability requirement, the second equation above, at the minimum cost, the last equation above. BlockSim uses this methodology during the optimization task.

	==Defining a Feasibility Policy in BlockSim==		===Defining a Feasibility Policy in BlockSim===
	In BlockSim, you can choose to use the default feasibility function, as defined by <math>{{C}_{i}}({{R}_{i}})={{e}^{(1-f)\cdot \tfrac{{{R}_{i}}-{{R}_{\min ,i}}}{{{R}_{\max ,i}}-{{R}_{i}}}}}\ </math>, or use your own function. The first picture below illustrates the use of the default values using the slider control. The second figure shows the use of an associated feasibility policy to create a user-defined cost function. When defining your own cost function, you should be aware of/adhere to the following guidelines:		In BlockSim, you can choose to use the default feasibility function, as defined by <math>{{C}_{i}}({{R}_{i}})={{e}^{(1-f)\cdot \tfrac{{{R}_{i}}-{{R}_{\min ,i}}}{{{R}_{\max ,i}}-{{R}_{i}}}}}\ </math>, or use your own function. The first picture below illustrates the use of the default values using the slider control. The second figure shows the use of an associated feasibility policy to create a user-defined cost function. When defining your own cost function, you should be aware of/adhere to the following guidelines:

Lisa Hacker: /* Determining the Optimum Allocation Scheme */

2012-08-17T06:01:35Z

Determining the Optimum Allocation Scheme

← Older revision		Revision as of 06:01, 17 August 2012
Line 131:		Line 131:
	::<math>0.90={{R}_{1}}\cdot {{R}_{2}}\cdot {{R}_{3}}\ </math>		::<math>0.90={{R}_{1}}\cdot {{R}_{2}}\cdot {{R}_{3}}\ </math>

	The objective now is to solve for <math>{{R}_{1}}\,\!</math> , <math>{{R}_{2}}\,\!</math> and <math>{{R}_{3}}\,\!</math> so that the equality in equation above is satisfied. To obtain an optimum solution, we also need to use our cost functions (i.e., define the total allocation costs) as:		The objective now is to solve for <math>{{R}_{1}}\,\!</math> , <math>{{R}_{2}}\,\!</math> and <math>{{R}_{3}}\,\!</math> so that the equality in the equation above is satisfied. To obtain an optimum solution, we also need to use our cost functions (i.e., define the total allocation costs) as:

	::<math>{{C}_{T}}={{C}_{1}}({{R}_{1}})+{{C}_{2}}({{R}_{2}})+{{C}_{3}}({{R}_{3}})\ </math>		::<math>{{C}_{T}}={{C}_{1}}({{R}_{1}})+{{C}_{2}}({{R}_{2}})+{{C}_{3}}({{R}_{3}})\ </math>

Lisa Hacker: /* Cost/Penalty Function */

2012-08-17T06:00:21Z

Cost/Penalty Function

← Older revision		Revision as of 06:00, 17 August 2012
Line 111:		Line 111:
	[[Image:6_12.png\|center\|400px\|Effect of the maximum achievable reliability on the cost function.]]		[[Image:6_12.png\|center\|400px\|Effect of the maximum achievable reliability on the cost function.]]

	====Cost Function====		===Cost Function===
	Once the cost functions for the individual components have been determined, it becomes necessary to develop an expression for the overall system cost. This takes the form of:		Once the cost functions for the individual components have been determined, it becomes necessary to develop an expression for the overall system cost. This takes the form of:

Line 122:		Line 122:
	BlockSim employs a nonlinear programming technique to minimize the system cost function. The system has a minimum (current) and theoretical maximum reliability value that is defined by the minimum and maximum reliabilities of the components and by the way the system is configured. That is, the structural properties of the system are accounted for in the determination of the optimum solution. For example, the optimization for a system of three units in series will be different from the optimization for a system consisting of the same three units in parallel. The optimization occurs by varying the reliability values of the components within their respective constraints of maximum and minimum reliability in a way that the overall system goal is achieved. Obviously, there can be any number of different combinations of component reliability values that might achieve the system goal. The optimization routine essentially finds the combination that results in the lowest overall system cost.		BlockSim employs a nonlinear programming technique to minimize the system cost function. The system has a minimum (current) and theoretical maximum reliability value that is defined by the minimum and maximum reliabilities of the components and by the way the system is configured. That is, the structural properties of the system are accounted for in the determination of the optimum solution. For example, the optimization for a system of three units in series will be different from the optimization for a system consisting of the same three units in parallel. The optimization occurs by varying the reliability values of the components within their respective constraints of maximum and minimum reliability in a way that the overall system goal is achieved. Obviously, there can be any number of different combinations of component reliability values that might achieve the system goal. The optimization routine essentially finds the combination that results in the lowest overall system cost.

	====Determining the Optimum Allocation Scheme====		==Determining the Optimum Allocation Scheme==
	To determine the optimum reliability allocation, the analyst first determines the system reliability equation (the objective function). As an example, and again for a trivial system with three components in series, this would be:		To determine the optimum reliability allocation, the analyst first determines the system reliability equation (the objective function). As an example, and again for a trivial system with three components in series, this would be:

Line 137:		Line 137:
	With the cost equation defined, then the optimum values for <math>{{R}_{1}}\,\!</math> , <math>{{R}_{2}}\,\!</math> and <math>{{R}_{3}}\,\!</math> are the values that satisfy the reliability requirement, the second equation above, at the minimum cost, the last equation above. BlockSim uses this methodology during the optimization task.		With the cost equation defined, then the optimum values for <math>{{R}_{1}}\,\!</math> , <math>{{R}_{2}}\,\!</math> and <math>{{R}_{3}}\,\!</math> are the values that satisfy the reliability requirement, the second equation above, at the minimum cost, the last equation above. BlockSim uses this methodology during the optimization task.

	====Defining a Feasibility Policy in BlockSim====		==Defining a Feasibility Policy in BlockSim==
	In BlockSim you can choose to use the default feasibility function, as defined by <math>{{C}_{i}}({{R}_{i}})={{e}^{(1-f)\cdot \tfrac{{{R}_{i}}-{{R}_{\min ,i}}}{{{R}_{\max ,i}}-{{R}_{i}}}}}\ </math>, or use your own function. The first picture below illustrates the use of the default values using the slider control. The second figure shows the use of an associated feasibility policy to create a user-defined cost function. When defining your own cost function, you should be aware of/adhere to the following guidelines:		In BlockSim, you can choose to use the default feasibility function, as defined by <math>{{C}_{i}}({{R}_{i}})={{e}^{(1-f)\cdot \tfrac{{{R}_{i}}-{{R}_{\min ,i}}}{{{R}_{\max ,i}}-{{R}_{i}}}}}\ </math>, or use your own function. The first picture below illustrates the use of the default values using the slider control. The second figure shows the use of an associated feasibility policy to create a user-defined cost function. When defining your own cost function, you should be aware of/adhere to the following guidelines:

	*Because the cost functions are evaluated relative to each other, they should be correlated. In other words, if one function evaluates to 10, <math>{{C}_{i}}({{R}_{i}})=10\,\!</math> for one block and 20 for another, <math>{{C}_{i}}({{R}_{i}})=20\,\!</math> , then the implication is that there is a 1 to 2 cost relation. <br>		*Because the cost functions are evaluated relative to each other, they should be correlated. In other words, if one function evaluates to 10, <math>{{C}_{i}}({{R}_{i}})=10\,\!</math> for one block and 20 for another, <math>{{C}_{i}}({{R}_{i}})=20\,\!</math> , then the implication is that there is a 1 to 2 cost relation. <br>

Lisa Hacker: /* Improving Reliability */

2012-08-17T05:57:09Z

Improving Reliability

← Older revision		Revision as of 05:57, 17 August 2012
Line 11:		Line 11:
	The reliability optimization process begins with the development of a model that represents the entire system. This is accomplished with the construction of a system reliability block diagram that represents the reliability relationships of the components in the system. From this model, the system reliability impact of different component modifications can be estimated and considered alongside the costs that would be incurred in the process of making those modifications. It is then possible to perform an optimization analysis for this problem, finding the best combination of component reliability improvements that meet or exceed the performance goals at the lowest cost.		The reliability optimization process begins with the development of a model that represents the entire system. This is accomplished with the construction of a system reliability block diagram that represents the reliability relationships of the components in the system. From this model, the system reliability impact of different component modifications can be estimated and considered alongside the costs that would be incurred in the process of making those modifications. It is then possible to perform an optimization analysis for this problem, finding the best combination of component reliability improvements that meet or exceed the performance goals at the lowest cost.

	=Improving Reliability=		==Improving Reliability==
	Reliability engineers are very often called upon to make decisions as to whether to improve a certain component or components in order to achieve a minimum required system reliability. There are two approaches to improving the reliability of a system: fault avoidance and fault tolerance. Fault avoidance is achieved by using high-quality and high-reliability components and is usually less expensive than fault tolerance. Fault tolerance, on the other hand, is achieved by redundancy. Redundancy can result in increased design complexity and increased costs through additional weight, space, etc.		Reliability engineers are very often called upon to make decisions as to whether to improve a certain component or components in order to achieve a minimum required system reliability. There are two approaches to improving the reliability of a system: fault avoidance and fault tolerance. Fault avoidance is achieved by using high-quality and high-reliability components and is usually less expensive than fault tolerance. Fault tolerance, on the other hand, is achieved by redundancy. Redundancy can result in increased design complexity and increased costs through additional weight, space, etc.

Line 44:		Line 44:
	In order to answer these questions, we must introduce another variable into the problem: cost. Cost does not necessarily have to be in dollars. It could be described in terms of non-monetary resources, such as time. By associating cost values to the reliabilities of the system's components, we can find an optimum design that will provide the required reliability at a minimum cost.		In order to answer these questions, we must introduce another variable into the problem: cost. Cost does not necessarily have to be in dollars. It could be described in terms of non-monetary resources, such as time. By associating cost values to the reliabilities of the system's components, we can find an optimum design that will provide the required reliability at a minimum cost.

	===Cost/Penalty Function===		==Cost/Penalty Function==
	There is always a cost associated with changing a design due to change of vendors, use of higher-quality materials, retooling costs, administrative fees, etc. The cost as a function of the reliability for each component must be quantified before attempting to improve the reliability. Otherwise, the design changes may result in a system that is needlessly expensive or overdesigned. Developing the "cost of reliability" relationship will give the engineer an understanding of which components to improve and how to best concentrate the effort and allocate resources in doing so. The first step will be to obtain a relationship between the cost of improvement and reliability.		There is always a cost associated with changing a design due to change of vendors, use of higher-quality materials, retooling costs, administrative fees, etc. The cost as a function of the reliability for each component must be quantified before attempting to improve the reliability. Otherwise, the design changes may result in a system that is needlessly expensive or overdesigned. Developing the "cost of reliability" relationship will give the engineer an understanding of which components to improve and how to best concentrate the effort and allocate resources in doing so. The first step will be to obtain a relationship between the cost of improvement and reliability.

Line 51:		Line 51:
	However, there are many cases where no such information is available. For this reason, a general (default) behavior model of the cost versus the component's reliability was developed for performing reliability optimization in BlockSim. The objective of this function is to model an overall cost behavior for all types of components. Of course, it is impossible to formulate a model that will be precisely applicable to every situation; but the proposed relationship is general enough to cover most applications. In addition to the default model formulation, BlockSim does allow the definition of user-defined cost models.		However, there are many cases where no such information is available. For this reason, a general (default) behavior model of the cost versus the component's reliability was developed for performing reliability optimization in BlockSim. The objective of this function is to model an overall cost behavior for all types of components. Of course, it is impossible to formulate a model that will be precisely applicable to every situation; but the proposed relationship is general enough to cover most applications. In addition to the default model formulation, BlockSim does allow the definition of user-defined cost models.

	====Quantifying the Cost/Penalty Function====		===Quantifying the Cost/Penalty Function===
	One needs to quantify a cost function for each component, <math>{{C}_{i}}\,\!</math> , in terms of the reliability, <math>{{R}_{i}}\,\!</math> , of each component, or:		One needs to quantify a cost function for each component, <math>{{C}_{i}}\,\!</math> , in terms of the reliability, <math>{{R}_{i}}\,\!</math> , of each component, or:

Line 94:		Line 94:
	*The exponent in the above equation approaches infinity as the component's reliability approaches its maximum achievable value. This means that it is easier to increase the reliability of a component from a lower initial value. For example, it is easier to increase a component's reliability from 70% to 75% than to increase its reliability from 90% to 95%.		*The exponent in the above equation approaches infinity as the component's reliability approaches its maximum achievable value. This means that it is easier to increase the reliability of a component from a lower initial value. For example, it is easier to increase a component's reliability from 70% to 75% than to increase its reliability from 90% to 95%.

	====The Feasibility Term, <math>f</math>====		===The Feasibility Term, <math>f</math>===
	The feasibility term in <math>{{C}_{i}}({{R}_{i}})={{e}^{(1-f)\cdot \tfrac{{{R}_{i}}-{{R}_{\min ,i}}}{{{R}_{\max ,i}}-{{R}_{i}}}}}\ </math> is a constant (or an equation parameter) that represents the difficulty in increasing a component's reliability relative to the rest of the components in the system. Depending on the design complexity, technological limitations, etc., certain components can be very hard to improve. Clearly, the more difficult it is to improve the reliability of the component, the greater the cost. The following figure illustrates the behavior of the function defined in the above equation for different values of <math>f</math> . It can be seen that the lower the feasibility value, the more rapidly the cost function approaches infinity.		The feasibility term in <math>{{C}_{i}}({{R}_{i}})={{e}^{(1-f)\cdot \tfrac{{{R}_{i}}-{{R}_{\min ,i}}}{{{R}_{\max ,i}}-{{R}_{i}}}}}\ </math> is a constant (or an equation parameter) that represents the difficulty in increasing a component's reliability relative to the rest of the components in the system. Depending on the design complexity, technological limitations, etc., certain components can be very hard to improve. Clearly, the more difficult it is to improve the reliability of the component, the greater the cost. The following figure illustrates the behavior of the function defined in the above equation for different values of <math>f</math> . It can be seen that the lower the feasibility value, the more rapidly the cost function approaches infinity.

Line 101:		Line 101:
	Several methods can be used to obtain a feasibility value. Weighting factors for allocating reliability have been proposed by many authors and can be used to quantify feasibility. These weights depend on certain factors of influence, such as the complexity of the component, the state of the art, the operational profile, the criticality, etc. Engineering judgment based on past experience, supplier quality, supplier availability and other factors can also be used in determining a feasibility value. Overall, the assignment of a feasibility value is going to be a subjective process. Of course, this problem is negated if the relationship between the cost and the reliability for each component is known because one can use regression methods to estimate the parameter value.		Several methods can be used to obtain a feasibility value. Weighting factors for allocating reliability have been proposed by many authors and can be used to quantify feasibility. These weights depend on certain factors of influence, such as the complexity of the component, the state of the art, the operational profile, the criticality, etc. Engineering judgment based on past experience, supplier quality, supplier availability and other factors can also be used in determining a feasibility value. Overall, the assignment of a feasibility value is going to be a subjective process. Of course, this problem is negated if the relationship between the cost and the reliability for each component is known because one can use regression methods to estimate the parameter value.

	====Maximum Achievable Reliability====		===Maximum Achievable Reliability===
	For the purposes of reliability optimization, we also need to define a limiting reliability that a component will approach, but not reach. The costs near the maximum achievable reliability are very high and the actual value for the maximum reliability is usually dictated by technological or financial constraints. In deciding on a value to use for the maximum achievable reliability, the current state of the art of the component in question and other similar factors will have to be considered. In the end, a realistic estimation based on engineering judgment and experience will be necessary to assign a value to this input.		For the purposes of reliability optimization, we also need to define a limiting reliability that a component will approach, but not reach. The costs near the maximum achievable reliability are very high and the actual value for the maximum reliability is usually dictated by technological or financial constraints. In deciding on a value to use for the maximum achievable reliability, the current state of the art of the component in question and other similar factors will have to be considered. In the end, a realistic estimation based on engineering judgment and experience will be necessary to assign a value to this input.