KR101462232B1 - Method and system for computing management criteria of warships using genetic algorithm - Google Patents

Abstract

An embodiment of the present invention relates to a method and system for estimating optimized operational criteria of a ship using genetic algorithms, comprising the steps of: receiving data from outside; receiving at least one And a step of outputting information about the calculated optimal solution to the outside, and the step of calculating the optimal solution includes a step of calculating an optimum solution using the genetic algorithm and a simulation model of the trap operation, And calculating a trap operation standard including the step of estimating the trap operation standard.

Description

TECHNICAL FIELD The present invention relates to a method and system for estimating a trap operation standard using a genetic algorithm,

An embodiment of the present invention relates to a method and system for estimating a trap operation standard using a genetic algorithm, and more particularly, to a method and system for estimating an optimal operation standard of a trap using a genetic algorithm.

The ROK Navy has recently hosted seminars on traps related technologies by inviting warship experts and weapons experts from domestic and foreign organizations. The main topic of the seminar is the strategy for the development of trap technology and the role of each institution for improving the quality of the trap.

In order to operate the trap efficiently, it is necessary to maintain the performance of the trap under various constraints and to reduce the operation cost. In addition, criteria may be needed to quantitatively assess the performance of the ship.

Traps used by navies or seaports can be made up of multiple parts. Traps can include expensive components with the latest technology applied and can be designed to perform at least two functions. The structure of traps is becoming increasingly complex and the number of parts required is increasing. As a result, not only the development costs but also the operation costs are increasing.

Therefore, it is important to design a structure with high performance considering the operating environment and cost from the development stage of the traps. However, it can be very difficult to quantitatively evaluate the performance of a trap with multiple components and a complex structure, and reflect this in the design.

Korean Patent Laid-Open No. 10-2003-0012274 (published on February 12, 2003)

Embodiments of the present invention can provide a method and system for calculating a trap operation standard that can calculate an average average failure interval and an average repair time of a part included in a warship.

Embodiments of the present invention can provide a trap operation standard calculation method and system capable of calculating an average failure interval and an average repair time of parts satisfying the reliability, availability, and maintenance of a ship.

The embodiment of the present invention can provide a method and system for calculating trap operation criterion that can more accurately calculate an optimal operation standard of a trap.

The problems to be solved by the embodiments of the present invention are not limited to the above-mentioned problems, and other matters not mentioned can be clearly understood by those skilled in the art from the following description. There will be.

The method of calculating a trap operation standard according to an embodiment of the present invention includes the steps of receiving data from outside, calculating an optimal solution for an operation criterion of at least one component included in the trap, based on the received data, And outputting information on the estimated optimal solution to the outside, wherein the optimal solution calculating step may include calculating the optimal solution using a genetic algorithm and a simulation model for the operation of the trap.

The trap operation standard calculation system according to an embodiment of the present invention includes a data receiver for receiving data from the outside, a solution calculator for calculating a solution to the operation criterion of at least one component included in the trap based on the received data, A simulation unit for performing simulation using the calculated solution and calculating the availability of the trap corresponding to the calculated solution and the life cycle cost of the trap, and a simulation unit for calculating, based on the calculated availability and the life cycle cost And a data output unit for outputting information about the calculated solution to the outside, wherein the solution computing unit computes the solution using a genetic algorithm, and the simulation unit computes the solution using the genetic algorithm, And a simulation model for the operation of the vessel, The can be performed.

In addition, the optimal solution may include mean time between failures (MTBF) and mean time to repair (MTTR) of each of the components.

In addition, the average failure interval or the average repair time included in the optimal solution may be represented by at least one binary character.

The optimal solution calculation step may include a solution generation step of generating at least two solutions to the operation criterion, a first fitness evaluation step of evaluating the fitness of the generated solutions, a crossing step of performing cross calculation using the solution, A second fitness evaluation step of evaluating a fitness of the solution in which the mutation operation is performed; a second fitness evaluation step of using the solution before the crossing operation is performed; A solution selecting step of selecting at least two solutions out of the solution in which the crossing operation is performed and the solution in which the mutation operation is performed; and a determination step of determining whether or not the repetition ending condition is satisfied, And repeating the cross-calculation step or the solution selection step using the selected solution.

In addition, the solution generation step may include generating at least two solution objects including a region where one solution is stored, selecting an arbitrary first solution object among the generated solution objects, Selecting an area corresponding to an arbitrary first part of the area included in the object, inputting a value in the selected area, determining whether the first part and the second part Selecting the corresponding region and inputting a value in the selected region, and selecting the solicited object for both the first solicited object and the other solicited object among the generated solicited objects And repeating the repeating step.

In addition, the step of inputting the value in the selected area may include generating a random number, and inputting a random value in the selected area according to the generated random number.

The first fitness evaluation step may include a simulation step of calculating the first availability degree of the ship using the generated solution and the simulation model, the simulation using the generated solution and the calculated first availability, Calculating a first life cycle cost of the trap, and evaluating the fitness of the generated solution using the calculated first life cycle cost.

The crossover operation step may further include selecting a first solution and a second solution different from the first solution and the second solution, the second solution being greater than or equal to 2 and smaller than the total length of the characters included in the first solution Generating a random number in the same range, replacing the generated random number after character in the first solution with the generated random number after the random number in the second solution, And repeating the step of selecting two solutions or the replacing step a predetermined number of times.

The mutation operation step may further include the steps of: selecting any one of the solutions from which the crossing operation is performed; changing at least one character among the characters included in the selected solution; And repeating the step of selecting the solution or the step of changing the character a predetermined number of times.

The step of changing the character may further include the steps of: selecting any one of the characters included in the selected solution; generating a random number; comparing the generated random number with a reference value; Changing the value of the selected character and repeating the step of selecting the character for each of the characters other than the selected character among the characters included in the selected solution or changing the value of the selected character can do.

The second fitness evaluation step may include a simulation step of calculating the second availability of the trap using the solution and the simulation model in which the mutation operation has been performed, the solution in which the mutation operation is performed, Calculating a second life cycle cost of the vessel using a second availability, and evaluating a fitness of the solution in which the mutation operation was performed using the calculated second life cycle cost have.

In addition, the repetition end condition may be related to the number of times the cross calculation step or the solution selection step is repeated.

The step of repeating may include the steps of: determining whether the number of times the cross calculation step or the solution selection step is repeated is greater than or equal to a specific number; and repeating the cross calculation step until the number of iterations exceeds the predetermined number And repeating the above selection step.

Also, the iteration termination condition may be related to whether the second life cycle cost has converged.

The repeating may further include storing a second life cycle cost of the lowest value among the calculated second life cycle costs, and during the repeating of the crossing calculation step and the solution selection step, Repeating the crossing operation step or the solution selection step until the stored second life cycle cost is not updated while the crossing operation step or the solution selection step is repeated a predetermined number of times . ≪ / RTI >

Also, the iteration termination condition may be related to the number of times the crossing operation step or the solution selection step is repeated and whether the second life cycle cost has converged.

The repeating may further include storing a second life cycle cost of the lowest value among the calculated second life cycle costs, and during the repeating of the crossing calculation step and the solution selection step, Updating the periodic cost, determining whether the number of repetitions of the intersection operation step or the solution selection step is equal to or greater than a first number, and if the number of repetitions is equal to or greater than the first number, And repeating the cross-calculation step or the solution selection step until the stored second life-cycle cost is not updated while the solution selection step is repeated a second number of times.

Also, the solution calculation unit generates at least two or more solutions to the operation criterion, and the solution calculation unit performs a cross calculation using the generated solution, and the solution calculation unit calculates the solution to which the cross calculation is performed Wherein the simulation unit evaluates the fitness of the solution in which the mutation operation is performed, and the control unit calculates the solution before the crossing operation is performed, the solution in which the crossing operation is performed, and the mutation Wherein the controller selects at least two solutions out of the solution in which the calculation is performed, and the control unit determines whether or not the iteration condition is satisfied, and the control unit causes the solution calculation unit to perform the crossing operation or the mutation operation It is possible to control whether to repeat or not.

According to embodiments of the present invention, an optimized average failure interval and an average repair time of the components included in the traps can be calculated.

According to the embodiment of the present invention, it is possible to calculate an average failure interval and an average repair time of parts satisfying the reliability, availability, and maintenance of a ship.

According to the embodiment of the present invention, it is possible to more accurately calculate the operational standard of the trap.

1 is a block diagram showing a configuration of a trap operation standard calculation system according to an embodiment of the present invention.
FIG. 2 is a flowchart showing a process of calculating a trap operation standard according to an embodiment of the present invention.
3 is a reference diagram for explaining a solution method according to an embodiment of the present invention.
4 is a flowchart illustrating a process of calculating an optimal solution of a trap operation standard calculation method according to an embodiment of the present invention.
5 is a flowchart illustrating a process of generating a solution according to an embodiment of the present invention.
6 is a flowchart illustrating a process of performing a first fitness evaluation step according to an embodiment of the present invention.
7 is a flowchart illustrating a process of performing a simulation step according to an embodiment of the present invention.
8 is a flowchart illustrating a process of performing a simulation step according to an embodiment of the present invention.
9 is a flowchart illustrating a process of performing a crossover operation according to an embodiment of the present invention.
10 is a reference diagram for explaining a crossover operation step according to an embodiment of the present invention.
11 is a flowchart illustrating a process of performing a mutation operation step according to an embodiment of the present invention.
12 is a reference diagram for explaining a mutation operation step according to an embodiment of the present invention.
FIG. 13 is a flowchart illustrating a process of selecting a solution according to an embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. It is to be understood, however, that the appended drawings illustrate the present invention in order to more easily explain the present invention, and the scope of the present invention is not limited thereto. You will know.

In addition, throughout the specification, when a part is referred to as being "connected" to another part, it is not only referred to as being "directly connected" but also "indirectly connected" And the like. In addition, when a part is referred to as including an element, it is not meant to exclude other elements unless specifically stated otherwise, but may include other elements.

Hereinafter, a method of calculating a trap operation standard using a genetic algorithm according to an embodiment of the present invention and a trap operation standard calculation system 100 will be described in detail with reference to the accompanying drawings.

1 is a block diagram showing a configuration of a trap operation standard calculation system 100 according to an embodiment of the present invention. Referring to FIG. 1, a trap operation standard calculation system 100 according to an embodiment of the present invention includes a data receiver 110 for receiving data from outside, at least one component included in a trap, A simulation unit 130 for performing a simulation using the calculated solution and evaluating the value of the calculated solution, a processor 130 for calculating the estimated value of the solution, A control unit 140 for controlling the solution computing unit 120 and the simulation unit 130 according to the calculated solution and a data output unit 150 for outputting the calculated solution information to the outside.

The data receiving unit 110 can receive data from the outside. The data receiving unit 110 may be connected to a communication network. The data receiving unit 110 may be, for example, an Ethernet card, a wireless LAN card, a Bluetooth module, an RFID module, a 3G module, an LTE module, or other communication device. The data receiving unit 110 may not be connected to the communication network. The data receiving unit 110 may receive data directly from the user. The data receiving unit 110 may be, for example, a keyboard, a mouse, a tablet, a touch screen, or other input device.

The data receiving unit 110 can receive data required for the solution calculating unit 120 to calculate the solution from the outside.

The solution computing unit 120 may calculate a solution to the operation standard of the trap based on the received data. Reliability, availability, and maintainability of the various components included in the vessel may be considered as operational criteria for the vessel. Reliability, Availability and Maintainability (RAM) analysis methods can be a way to quantitatively evaluate the performance of a particular system.

Reliability may indicate the probability that the system or a component included in the system can function correctly without failure for a certain period of time under certain conditions. The availability may indicate the probability that the system or a component included in the system is in a state capable of performing the required function at any point in time. The degree of maintenance may indicate the probability that the system or the components included in the system can be restored to a normal state within a specified period of time under certain conditions. The availability can be calculated on the basis of reliability and maintenance. The availability can be a criterion for evaluating the performance of the system.

In order to operate the trap efficiently, the target value for the reliability, availability and maintenance of the trap can be set from the design stage of the trap. The target value may be a target to be achieved when the trap is operated. When the target value is set at the design stage of the vessel, the conditions such as the technology level and the cost can be reflected in the design. In addition, a countermeasure against all predictable potential faults can be prepared.

Based on the received data, the solution computing unit 120 may calculate a solution to the operation criterion of each of at least one or more components included in the trap. (MTTR) and mean time to repair (MTTR) of each of the components, which can satisfy the target value of the availability of the trap, ) Can be calculated. The solution computing unit 120 can calculate the solution using a genetic algorithm. A concrete method of calculating the solution by the solution computing unit 120 will be described later with reference to FIG. 5 and the following.

The simulation unit 130 may perform a simulation using the computed solution and evaluate the value of the computed solution. The simulation unit 130 may perform a simulation of the operation of the trap using the calculated solution. The simulation unit 130 can calculate the availability of the trap and the life cycle cost according to the calculated solution through the simulation. A concrete method of performing the simulation by the simulation unit 130 will be described later with reference to FIG.

The control unit 140 may control the solution calculation unit 120 and the simulation unit 130 according to the evaluated value of the calculated solution. The controller 140 may compare the value of the calculated solution with the value of the solution calculated previously. As a result of the comparison, the control unit 140 can determine which solution is the better solution from the calculated solution and the previously calculated solution.

The control unit 140 can determine whether or not the calculated availability satisfies the target value. Also, the control unit 140 can store the life cycle cost of the lowest value among the life cycle costs calculated using the solution in which the availability is determined to satisfy the target value. Hereinafter, even though it is referred to as the life cycle cost of only the lowest value, the life cycle cost refers to the life cycle cost of the lowest value among the life cycle costs calculated using the solution in which the availability is determined to satisfy the target value Let's assume.

If the life cycle cost calculated using the new solution has a lower value than the life cycle cost of the lowest value, the controller 140 stores the lowest value of the life cycle cost, Lifecycle costs can be renewed.

The control unit 140 can determine whether or not the predetermined repetition end condition is satisfied and can control whether or not the solution computing unit 120 computes a new solution again according to the determined result. In addition, the control unit 140 may control whether the simulation unit 130 performs the simulation again using the new solution according to the determined result.

For example, the predetermined iteration termination condition may be related to the number of times the solution has been computed. The control unit 140 can determine whether the solution calculation unit 120 has calculated a solution a predetermined number of times. The control unit 140 may cause the solution calculation unit 120 to calculate a new solution again when the determination result does not reach the predetermined number of times. The control unit 140 may cause the simulation unit 130 to perform the simulation again using the new solution when the determination result does not reach the predetermined number of times.

According to another embodiment, the predetermined iteration termination condition may be related to whether the life cycle cost calculated using the solution has converged. If the life cycle cost of the lowest value among the life cycle costs calculated using the solution for which the availability is determined to satisfy the target value has been updated for a specific recent number of times past the target value, 120 may again calculate a new solution.

In other words, if the life cycle cost of the lowest value is not updated for the last certain number of times, the control unit 140 may cause the solution calculation unit 120 to end the repetition of the operation. In other words, the life cycle cost can be determined to have converged if the lowest value life cycle cost has not been updated for the last certain number of times.

The controller 140 may cause the simulation unit 130 to perform the simulation again using the new solution when the life cycle cost of the lowest value has been updated for a specific recent number of times. In other words, if the life cycle cost of the lowest value is not updated for the last certain number of recent past, the control unit 140 may cause the simulation unit 130 to end the repetition of the simulation.

According to another embodiment, the predetermined iteration termination condition may be related to the number of times the solution is calculated and whether the life cycle cost calculated using the solution has converged. When the solution computing unit 120 computes the first number of the anomaly solutions and the life cycle cost of the lowest value is not updated for the last second number of the past years, the control unit 140 causes the solution computing unit 120 to repeat the operation It can be terminated. In other words, when the number of times the solution has been calculated is less than the first number of times or the life-cycle cost of the lowest value has been updated for the last second number of times past, the control unit 140 causes the solution calculation unit 120 to re- It is possible to calculate a new solution.

In addition, when the solution computing unit 120 computes the first number of the anomaly solutions and the life cycle cost of the lowest value is not updated during the last second number of times past the last, the control unit 140 controls the simulation unit 130 It is possible to end the repetition. In other words, when the number of times the solution has been calculated is less than the first number of times or the life-cycle cost of the lowest value has been updated for the last second number of times past, the control unit 140 determines that the simulation unit 130 The simulation can be performed again using the new solution.

The data output unit 150 may output the calculated solution information to the outside. The data output unit 150 may output the calculated solution, information calculated using the calculated solution, or information converted from the calculated solution to the outside.

The data output unit 150 may be connected to a communication network. The data output unit 150 may be, for example, an Ethernet card, a wireless LAN card, a Bluetooth module, an RFID module, a 3G module, an LTE module, or other communication device. The data output unit 150 may not be connected to the communication network. The data output unit 150 may be, for example, a display device, a printer, or other output device.

FIG. 2 is a flowchart showing a process of calculating a trap operation standard according to an embodiment of the present invention.

Referring to FIG. 2, the trap operation standard calculation method according to the embodiment of the present invention may be performed in step S100 in which the trap operation standard calculation system 100 receives data from the outside. The data receiving unit 110 of the trap operation standard calculation system 100 can receive the data required for the solution calculation unit 120 to calculate the solution from the outside.

Next, an optimal solution calculation step (S200) may be performed in which the trap operation standard calculation system 100 calculates an optimal solution to the operation criterion of at least one part included in the trap, based on the received data. 3 is a reference diagram for explaining a solution method according to an embodiment of the present invention.

According to an embodiment of the present invention, the solution to the operating criterion may include a mean time between failure (MTBF) and an average repair time (MTTR) of each of the components that can satisfy a target value of the availability of the trap, Mean Time To Repair). The optimal solution to the operating criterion may be the lowest life cycle cost calculated using the solution in the solution.

Referring to FIG. 3, one solution to the operating criteria may include MTBF and MTTR of the parts sequentially listed for each part. The trap operation standard calculation system 100 can perform an operation using one entity including one solution. Hereinafter, an object including one solution will be referred to as a solution object. A solver object may include at least one region. Each region may correspond to each component. In other words, one area may include the mean time between failures (MTBF) and average repair time (MTTR) of one part.

The solution to the operating criterion can be expressed as a string. The mean time between failures (MTBF) and average repair time (MTTR) of each part included in a trap can be expressed in strings. The mean time between failures (MTBF) and mean time to repair (MTTR) of each part can be represented by binary characters. The mean time between failures (MTBF) of each component can be represented by five binary characters. In addition, the average repair time (MTTR) of each component can be represented by five binary characters.

Therefore, as shown in Fig. 3, in the solution to the operating criterion, the average failure interval (MTBF) of the first component, the average repair time of the first component, the MTBF of the second component, The average repair time, ..., the average failure interval of the n-th component (MTBF), and the average repair time of the n-th component can be sequentially listed.

The mean time between failures (MTBF) or average repair time (MTTR) of a part expressed in binary characters can be converted to decimal by a specific function. For example, the Mean Time Between Failure (MTBF) or Mean Time to Repair (MTTR) of a part represented by a binary character can be converted to a decimal number by the following equation (1).

는 평균 고장 간격(MTBF) 또는 평균 수리 시간(MTTR)을 나타내는 이진문자 내에서의 인덱스를 나타낼 수 있다. 또한,

은 평균 고장 간격(MTBF) 또는 평균 수리 시간(MTTR)을 나타내는 이진문자에 포함된 총 문자수를 나타낼 수 있다. 또한,

는 상기 이진문자 내에서의

번째 문자의 값을 나타낼 수 있다. 또한,

는 평균 고장 간격(MTBF) 또는 평균 수리 시간(MTTR)의 상한값을 나타낼 수 있다. 또한,

는 평균 고장 간격(MTBF) 또는 평균 수리 시간(MTTR)의 하한값을 나타낼 수 있다.In Equation (1)

May represent an index within a binary character indicating an average failure interval (MTBF) or an average repair time (MTTR). Also,

May represent the total number of characters included in the binary character indicating the mean time between failure (MTBF) or average repair time (MTTR). Also,

Lt; RTI ID = 0.0 >

The value of the second character can be represented. Also,

(MTBF) or the upper limit of the mean time to repair (MTTR). Also,

(MTBF) or the lower limit of the mean time to repair (MTTR).

4 is a flowchart illustrating a process of calculating an optimal solution (S200) of the trap operation standard calculation method according to an embodiment of the present invention.

Referring to FIG. 4, in step S200 of calculating an optimal solution, a solution generation step S210 may be performed in which the trap operation standard calculation system 100 generates at least two solutions to the operation standard. The generation step S210 may be performed by the solution computing unit 120 of the trap operation standard calculation system 100. [ 5 is a flowchart illustrating a process of generating a solution according to an embodiment of the present invention (S210).

Referring to FIG. 5, in step S210, the trap operation standard calculation system 100 may generate at least two or more solution objects (S211). The data receiving unit 110 of the trap operation standard calculation system 100 can receive the number of objects to be generated when the genetic algorithm is executed. The number of the solved objects may be at least two or more. The trap operation standard calculation system 100 can generate a solution object by the number of the received solution objects.

Next, the trap operation standard calculation system 100 may select an arbitrary first solution object from among the generated solution objects (S212). Any one solution object among the generated solution objects may be arbitrarily selected.

Next, a step S213 may be performed in which the trap operation standard calculation system 100 selects an area corresponding to an arbitrary first part among at least one area included in the first solution object. As described above, the solution object may include an area corresponding to each component, and an area corresponding to any one component selected arbitrarily.

Next, step S214 may be performed in which the trap operation standard calculation system 100 inputs a value in the selected area. As described above, the selected area may include an MTBF (Mean Time Between Failure) and MTTR (Mean Time to Repair) of the part. The solution computing unit 120 can generate a random number. The calculation unit 120 may input a random average failure interval (MTBF) and a random average repair time (MTTR) in the selected area according to the generated random number.

For example, the solution computing unit 120 may generate a random number less than 1 and less than 1. If the generated random number is equal to or greater than 0.5, the solution computing unit 120 may input 1 in the first place of the average failure interval (MTBF) of 5-digit binary characters. If the generated random number is less than 0.5, the solution calculating unit 120 may input 0 in the first place of the average failure interval (MTBF).

The smoothing unit 120 may repeat the above process even after the second digit of the average failure interval (MTBF). In other words, the solution computing unit 120 generates a random number, and can input 0 or 1 in k (where 1 <= k <= 5) th digit of the average failure interval (MTBF) according to the generated random number . Also, the solution calculating unit 120 may repeat the above process for the mean repair time (MTTR). In other words, the solution computing unit 120 generates a random number and inputs 0 or 1 to k (where 1 <= k <= 5) th digit of the average repair time (MTTR) according to the generated random number .

Next, the trap operation standard calculation system 100 repeats the step of selecting the corresponding region (S213) for each of the first and second parts and the step of entering a value (S214) in the selected region Step S215 may be performed. For example, if it is assumed that an area corresponding to each of n parts is included in the first solution object, the process may be repeated for both the first part and the other (n - 1) parts .

In other words, an area corresponding to an arbitrary second part different from the first part among the areas included in the first solution object may be selected. Also, the solution computing unit 120 generates a random number, and can input a value in a region corresponding to the second component according to the generated random number. The above process can be repeated for any of the first part and any third part other than the second part. The above process can be repeated until a value is input in the area corresponding to the n-th part.

In operation S216, the trap operation standard calculation system 100 repeats steps S212 through S215 for selecting the solution object for all of the first and second solution objects, Can be performed. For example, if it is assumed that the number of the solution objects generated in the step S211 of generating the solution object is m, the process is repeated for both the first solution object and the (m - 1) .

In other words, an arbitrary second solution object other than the first solution object may be selected from among the generated solution objects. An area corresponding to an arbitrary first part of at least one area included in the second solution object may be selected. The solution computing unit 120 generates a random number and can input a value in a region corresponding to the first component according to the generated random number.

Also, the above process can be repeated for any second part other than the first part. The above process can be repeated until a value is input in the area corresponding to the n-th part. Also, the process may be repeated for an arbitrary third solution object different from the first solution object and the second solution object. This process can be repeated until a value is input in the area corresponding to the n-th part of the object to be imaged.

Referring again to FIG. 4, a first fitness evaluation step (S220) may be performed in which the trap operation standard calculation system 100 evaluates the fitness of the generated solution.

FIG. 6 is a flowchart illustrating a process of performing a first fitness evaluation step (S220) according to an embodiment of the present invention. Referring to FIG. 6, the first fitness evaluation step S220 is a simulation in which the trap operation standard calculation system 100 calculates the first availability of the trap using the generated solution and the simulation model of the trap Step S221 may be performed. The simulation step S221 may be performed by the simulation unit 130 of the trap operation standard calculation system 100. [

FIGS. 7 and 8 are flowcharts illustrating a process of performing the simulation step S221 according to the embodiment of the present invention. Since at least two solutions may be generated by the solution generation step S210, the simulation step S221 may be repeatedly performed as many times as the number of solutions generated. The simulation model of the trap may be stored in advance in the trap operation standard calculation system 100.

The simulation model of the ship is composed of the operational scenarios of the ship, the required mission of the ship, the reliability structure expressed by the tree structure, the types and parameters of the probability distribution following the failure time of the parts included in the ship, the kind of probability distribution A model showing the state of the component after repair of the component, a probability of recovery of the component through repair of the failed component, an interval at which the preventive maintenance is performed, a time at which the preventive maintenance is performed, And may include at least one or more variables.

As a result of the simulation step S221, the simulation unit 130 determines whether or not the operation time of the trap, the downtime of the trap, the total number of failures of the trap, the availability of the trap, the average failure interval of the trap, Time, failure to perform the essential mission normally due to a breakdown of the pit, number of missed breaks during the required mission, availability of the required mission, average failure interval during the required mission, At least one of the cost, the investment cost of the parts, and the repair cost of the parts can be calculated.

The operating time of the trap can indicate the time the trap was operating normally during the simulation. The downtime of a trap can indicate the time that the trap did not operate normally due to a failure during the simulation. The availability of a trap can be a value obtained by dividing the operation time of the trap by the value obtained by adding the operating time of the trap and the downtime of the trap.

At least two solutions can be generated by the solution generating step S210 and the availability of the trap corresponding to each of the solutions can be calculated by the simulation step S221. Therefore, the availability of the trap can be calculated by the same number as the number of generated solutions.

Referring again to FIG. 6, next, the trap operation standard calculation system 100 calculates the first life cycle cost of the trap using the generated solution and the calculated first availability of the trap (S222 ) Can be performed. The step S222 of calculating the first life cycle cost of the trap can be performed by the control unit 140 of the trap operation standard calculation system 100. [

The life cycle cost of a trap can be calculated by using the cost of developing the trap, the cost of the trap, the cost of repairing the trap, and the penalty cost of the trap. The life cycle cost of a trap can be calculated by the following equation (2).

는 부품의 인덱스를 나타낼 수 있다. 또한,

는 해의 인덱스를 나타낼 수 있다. 또한,

는 부품

의 평균 고장 간격을 나타낼 수 있다. 또한,

는 부품

의 평균 수리 시간을 나타낼 수 있다. 또한,

는 부품

의 개발 비용을 나타낼 수 있다. 또한,

는 부품

의 투자 비용을 나타낼 수 있다. 또한,

는 부품

의 수리 비용을 나타낼 수 있다. 또한,

는

번째 해의 패널티 비용을 나타낼 수 있다. 또한,

는

번째 해에 대응되는 함정의 수명주기비용을 나타낼 수 있다. 또한,

는 함정의 목표 가용도를 나타낼 수 있다. 또한,

는

번째 해에 대응되는 함정의 가용도를 나타낼 수 있다. 또한,

는 가중치를 나타낼 수 있다.

는 1보다 크거나 같은 값을 가질 수 있다.In Equation 2,

Can represent the index of the part. Also,

Can represent the index of the solution. Also,

Parts

Can be expressed as the average time interval of failure. Also,

Parts

And the average repair time of the first and second tires. Also,

Parts

Of the total cost of development. Also,

Parts

Of the total investment. Also,

Parts

Repair cost of the vehicle. Also,

The

The penalty cost of the second year. Also,

The

The life cycle cost of the trap corresponding to the second year. Also,

Can indicate the target availability of the trap. Also,

The

The availability of a trap corresponding to the second year can be shown. Also,

Can represent a weight.

May have a value equal to or greater than one.

는 부품

의 개발 비용을 나타낼 수 있다. 개발 비용은 함정의 목표 가용도를 만족시키고 부품의 신뢰도를 개선하기 위해 필요한 개발에 소요되는 비용일 수 있다. 부품

의 개발 비용은 다음의 수학식 3과 같이 표현될 수 있다. 수학식 3에서

,

및

는 상수이며, 부품별로 서로 다른 값을 가질 수 있다.In Equation (2)

Parts

Of the total cost of development. Development costs can be the cost of development needed to meet the target availability of the pit and improve the reliability of the part. part

Can be expressed by the following equation (3). In Equation 3,

,

And

Is a constant and can have different values for each part.

는 부품

의 투자 비용을 나타낼 수 있다. 투자 비용은 함정의 목표 가용도를 만족시키고 부품의 정비도를 개선하기 위한 정비시설 확충에 소요되는 비용일 수 있다. 부품

의 투자 비용은 다음의 수학식 4와 같이 표현될 수 있다. 수학식 4에서

,

및

는 상수이며, 부품별로 서로 다른 값을 가질 수 있다.In Equation (2)

Parts

Of the total investment. The investment cost may be the expense of expanding the maintenance facilities to meet the target availability of the traps and to improve the maintenance of the parts. part

Can be expressed as Equation (4). &Quot; (4) &quot; In Equation 4,

,

And

Is a constant and can have different values for each part.

는 부품

의 수리 비용을 나타낼 수 있다. 부품의 신뢰도가 개선되면 부품의 단가가 상승할 수 있다. 수리 비용은 상승된 단가의 고장난 부품의 수리에 소요되는 비용일 수 있다. 부품

의 수리 비용은 다음의 수학식 5와 같이 표현될 수 있다. 수학식 5에서

,

, 및

은 상수이며, 부품별로 서로 다른 값을 가질 수 있다.In Equation (2)

Parts

Repair cost of the vehicle. If the reliability of the parts is improved, the unit price of the parts may be increased. Repair costs can be the cost of repairing faulty parts at an increased unit price. part

Can be expressed as the following equation (5). &Quot; (5) &quot; In Equation (5)

,

, And

Is a constant and can have different values for each part.

는

번째 해의 패널티 비용을 나타낼 수 있다.

번째 해의 패널티 비용은 다음의 수학식 6에 의해 산출될 수 있다.In Equation (2)

The

The penalty cost of the second year.

The penalty cost for the second year can be calculated by the following equation (6).

At least two solutions can be generated by the solution generation step S210, and the life cycle cost of the trap corresponding to each solution can be calculated by calculating the life cycle cost (S222). Therefore, the life cycle cost of the trap can be calculated by the same number as the number of generated solutions.

Next, a step S223 of evaluating the fitness of the generated solution using the first life cycle cost of the trap calculated by the trap operation standard calculation system 100 may be performed. The control unit 140 may store the life cycle cost of the lowest value among the life cycle costs of the calculated trap.

If there is no life cycle cost having a value lower than the life cycle cost previously stored in the control unit 140 among the life cycle costs of the trap calculated, the generated solution may be evaluated as having a low fitness have. If there is a life cycle cost having a value lower than the life cycle cost previously stored in the control unit 140 among the life cycle costs of the trap calculated, the generated solution may be evaluated as having a high fitness . At this time, the life cycle cost stored by the control unit 140 can be updated with the life cycle cost having the lower value.

Since the solution can be defined as the earliest generated solution, the life cycle cost previously stored by the control unit 140 does not exist, and thus the generated solution can be evaluated as having high fitness.

Referring again to FIG. 4, a crossing operation step (S230) in which the trap operation standard calculation system 100 performs a cross operation using the solution may be performed. 9 is a flowchart illustrating a process of performing a crossover operation S230 according to an embodiment of the present invention. 10 is a reference diagram for explaining a crossover operation step S230 according to an embodiment of the present invention. The crossing operation step S230 may be performed by the solution computing unit 120 of the trap operation standard calculation system 100. [

Referring to FIG. 9, in the crossing operation step S230, the trap operation standard calculation system 100 may select two arbitrary solutions in the solution (S231). The solution computing unit 120 may select any first solution and the first solution and a different second solution from among the solutions.

Next, a step S232 of generating a random number in a range in which the trap operation standard calculation system 100 is equal to or greater than 2 and less than or equal to the total length of characters included in the first solution may be performed. For example, assuming that the total length of characters included in the solution is 20 as shown in FIG. 10, the solution computing unit 120 can generate a random number in a range of 2 to 20 inclusive.

Referring again to FIG. 9, next, the trap operation standard calculation system 100 replaces the generated random number after character in the first solution with the generated random number after character in the second solution Step S233 may be performed. For example, it is assumed that the generated random number is 11. Referring to FIG. 10, the 11th to 20th characters of the first solution may be replaced with the 11th to 20th characters of the second solution.

Referring again to FIG. 9, a step S234 of repeating the steps S231 to S233 of selecting the two solutions by the trap operation standard calculation system 100 may be performed . The data receiving unit 110 may receive the cross rate from the outside. The solution computing unit 120 may repeat the steps S231 to S233 for selecting the two solutions by repeating the number of the generated solutions * have.

Referring again to FIG. 4, a mutation operation step (S240) may be performed in which the trap operation standard calculation system 100 performs a mutation operation using the solution in which the intersection operation is performed. 11 is a flowchart showing a process of performing a mutation operation step (S240) according to an embodiment of the present invention. 12 is a reference diagram for explaining a mutation operation step (S240) according to an embodiment of the present invention. The mutation operation step S240 may be performed by the solution computing unit 120 of the trap operation standard calculation system 100. [

Referring to FIG. 11, in operation S 240, a step S 241 may be performed in which the trap operation standard calculation system 100 selects any one of the solutions in which the intersection operation is performed . The solution computing unit 120 can select any solution from among the solutions.

Next, the trap operation standard calculation system 100 may change at least one character among the characters included in the selected solution (S242). The data receiving unit 110 may receive the mutation rate from the outside. The smoothing unit 120 may select the first character among the strings of binary characters included in the selected solution. The smoothing unit 120 can generate a random number in the range of 0 to 1.

If the generated random number is smaller than or equal to the received mutation rate, the solution calculating unit 120 may change the value of the selected character. For example, when the selected character is 0, the solution calculator 120 may change the selected character to 1. When the selected character is 1, the solution calculator 120 may change the selected character to 0.

The smoothing unit 120 may repeat the above process for the second and subsequent characters among the strings included in the selected solution. In the embodiment shown in FIG. 12, the sixth, tenth, fifteenth, and nineteenth characters out of the twenty characters included in the selected solution were changed.

Next, a step 243 of repeating the steps S241 to S242 of selecting the one arbitrary solution by the trap operation standard calculation system 100 may be performed. The smoothing unit 120 may change the step S241 of selecting any one of the solutions and the step of changing the character S242 to the number of the solutions in which the crossing operation is performed * 1} times.

Referring again to FIG. 4, a second fitness evaluation step (S250) may be performed in which the trap operation standard calculation system 100 evaluates the fitness of the solution in which the mutation operation is performed. The second fitness evaluation step S250 may be performed similarly to the first fitness evaluation step S220 shown in Figs. 6 and 7.

The second fitness evaluation step S250 includes a simulation step of calculating a second availability of the trap using the simulation model of the trap and the solution in which the trap operation criterion calculation system 100 performed the mutation operation S251) may be performed. The simulation step S251 may be performed by the simulation unit 130 of the trap operation standard calculation system 100. [

Next, the trap operation standard calculation system 100 calculates the second life cycle cost of the trap using the solution obtained by performing the mutation operation and the calculated second availability of the trap (S252) . The step S252 of calculating the second life cycle cost of the trap can be performed by the control unit 140 of the trap operation standard calculation system 100. [

Next, a step S253 of evaluating the fitness of the solution in which the mutation operation is performed may be performed using the second life cycle cost of the trap calculated by the trap operation standard calculation system 100. [

If there is no life cycle cost having a value lower than the life cycle cost previously stored in the control unit 140 among the life cycle costs of the trap calculated, the solution in which the mutation operation is performed has a low fitness . If there is a life cycle cost having a value lower than the life cycle cost previously stored in the control unit 140 among the life cycle costs of the trap calculated, the solution in which the mutation operation is performed has a high fitness Can be evaluated. At this time, the life cycle cost stored by the control unit 140 can be updated with the life cycle cost having the lower value.

Referring again to FIG. 4, next, it is assumed that the trap operation standard calculation system 100 determines at least two of the above solution before the intersecting operation is performed, the solution in which the intersecting operation is performed, The selection step S260 may be performed to select the above solutions. FIG. 13 is a flowchart illustrating a process of selecting a solution (S260) according to an embodiment of the present invention. The selection step S260 may be performed by the control unit 140 of the trap operation standard calculation system 100. [

Referring to FIG. 13, the solution selection step (S260) first determines whether the solution before the intersection operation is performed by the trap operation standard calculation system (100), the solution in which the intersection operation is performed and the mutation operation A step S261 of calculating the fitness percentage of each of the above solutions may be performed. The fitness fraction of a particular solution may represent the percentage of the life cycle cost corresponding to the solution to the sum of the life cycle costs corresponding to each solution.

Next, a step S262 may be performed in which the trap operation standard calculation system 100 generates a random number in a range of 0 to less than 1.

Next, a step S263 may be performed in which the trap operation standard calculation system 100 selects the k-th solution satisfying the following equation.

Next, the step S262 of generating the random number by the trap operation standard calculation system 100 or the step S263 of selecting the solution may be performed (S264). The control unit 140 repeats the step S264 of generating the random number (S262) to the solution (S263) (the number of the solutions before the crossing operation is performed) - 1} .

Referring again to FIG. 4, next, the trap operation standard calculation system 100 determines whether or not the repeat end condition is satisfied, and the intersection operation step S230 (S260) may be performed at step S270.

The control unit 140 can determine whether or not the repeat end condition is satisfied. The iteration termination condition may be related to the number of times the intersection operation step S230 to the solution selection step S260 are repeated. The control unit 140 may determine whether the process has been repeated a predetermined number of times. The control unit 140 may repeat the process if the determination result does not reach the predetermined number of times.

According to another embodiment, the iteration termination condition may be related to whether the life cycle cost corresponding to the solution has converged. The control unit 140 can store the life cycle cost of the lowest value among the life cycle costs corresponding to the solution. The life cycle cost stored by the control unit 140 can be updated with a life cycle cost having a lower value. If the stored life cycle cost has been updated while repeating the process for the last predetermined number of times, the control unit 140 may repeat the process. In other words, if the stored life cycle cost is not updated during the repetition of the above-described specific number of times, the control unit 140 may terminate the repetition of the above process.

According to another embodiment, the iteration termination condition may be related to the number of times the process is repeated and whether the life cycle cost corresponding to the solution has converged. If the stored life cycle cost is not updated during the above-described second number of times, the controller 140 may repeat the above process . In other words, if the stored life cycle cost has been updated while the number of times the process is repeated is less than the first number, or while the process is repeated for the last second number of times, Can be repeated again.

The controller 140 may output a solution corresponding to the life cycle cost of the lowest value stored in the controller 140 as the optimal solution.

Referring again to FIG. 2, a step S300 of outputting information about the calculated optimal solution to the outside can be performed by the trap operation standard calculation system 100. FIG. The data output unit 150 of the trap operation standard calculation system 100 can output the calculated optimal solution, information calculated using the calculated optimal solution, or information converted from the calculated optimum solution to the outside.

According to the embodiment of the present invention described above, the optimized average failure interval and the average repair time of the parts included in the traps can be calculated. In addition, the average failure interval and the average repair time of the parts that satisfy the reliability, availability, and maintenance of the traps can be calculated. In addition, the optimized operational criteria of the ship can be estimated more accurately.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments, but, on the contrary, It will be appreciated that many variations and applications not illustrated above are possible. For example, each component specifically shown in the embodiments of the present invention can be modified and implemented. It is to be understood that all changes and modifications that come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.

100: Calculation system of trap operation standard
110: Data receiving unit
120:
130:
140:
150: Data output section

Claims (20)

Receiving the data from the outside by the trap operation standard calculation system;
Estimating an optimal solution for calculating an optimal solution for an operation criterion of at least one component included in a warship based on the received data; And
And the trap operation standard calculation system includes the step of outputting information on the calculated optimal solution to the outside,
The optimal solution calculation step may include:
Generating the at least two solutions to the operational criteria by the trap operation criteria calculation system; And
Estimating the optimal solution using a genetic algorithm and a simulation model for the operation of the trap,
Wherein the generating of the two or more solutions comprises:
Generating at least two solution objects including the area where one solution is stored in the trap operation criterion estimation system;
Selecting an arbitrary first solution object among the generated solution objects by the trap operation standard calculation system;
Selecting a region corresponding to an arbitrary first part among the areas included in the first solution object;
Inputting a value into the selected area by the trap operation criteria calculation system;
Repeating the step of selecting a corresponding area or entering a value in the selected area for each of the first part and the other part among the parts included in the vessel, And
The trap operation standard calculation system repeatedly repeating the step of selecting the solution object for the first solution object and the solution objects for the other solution objects among the generated solution objects,
The method comprising: The method according to claim 1,
Wherein the optimal solution includes Mean Time Between Failure (MTBF) and Mean Time To Repair (MTTR) of each of the components. 3. The method of claim 2,
Wherein the average failure interval or the average repair time included in the optimal solution is represented by at least one binary character. The method according to claim 1,
Wherein the step of estimating the optimal solution using the genetic algorithm and a simulation model for the operation of the trap,
A first fitness evaluation step of evaluating a fitness of the generated solution;
A crossover operation step of performing a crossover operation using the solution;
Performing a mutation operation using the solution in which the crossing operation is performed;
A second fitness evaluation step of evaluating fitness of the solution in which the mutation operation is performed;
A solution selecting step of selecting at least two solutions out of the solution before the crossing operation, the solution in which the crossing operation is performed, and the solution in which the mutation operation is performed; And
Determining whether the repeat end condition is satisfied, and repeating the intersecting operation step or the solution selecting step using the selected solution until the repeat end condition is satisfied
The method comprising: delete The method according to claim 1,
Wherein the step of inputting a value in the selected area comprises:
Generating a random number; And
Inputting a random value in the selected area according to the generated random number
The method comprising: 5. The method of claim 4,
The first fitness evaluation step may include:
A simulation step of calculating the first availability of the ship using the generated solution and the simulation model;
Calculating a first life cycle cost of the vessel using the generated solution and the calculated first availability; And
Evaluating the fitness of the generated solution using the calculated first life cycle cost
The method comprising: 5. The method of claim 4,
Wherein the cross-
Selecting a first solution and a second solution different from the first solution;
Generating a random number in a range that is greater than or equal to 2 and less than or equal to the total length of characters included in the first solution;
Replacing the generated random number and subsequent characters of the first solution with the generated random number and the subsequent characters of the second solution; And
Repeating the step of selecting the first solution and the solution of the second solution and the replacing step a predetermined number of times
The method comprising: 5. The method of claim 4,
The mutation operation step includes:
Selecting any one of the solutions from which the intersection operation is performed;
Changing at least one character among characters included in the selected solution; And
Repeating the step of selecting any one solution or changing the character a predetermined number of times
The method comprising: 10. The method of claim 9,
The method of claim 1,
Selecting any one of the characters included in the selected solution;
Generating a random number;
Comparing the generated random number with a reference value, and changing a value of the selected character according to the comparison result; And
Repeating the step of selecting the character for each of the characters other than the selected character among the characters included in the selected solution or changing the value of the selected character
The method comprising: 5. The method of claim 4,
The second fitness evaluation step may include:
A simulation step of calculating the second availability of the trap using the solution and the simulation model in which the mutation operation is performed;
Calculating a second life cycle cost of the trap using the solution in which the mutation operation is performed and the calculated second availability; And
Evaluating the fitness of the solution in which the mutation operation is performed using the calculated second life cycle cost
The method comprising: 5. The method of claim 4,
Wherein the cyclic termination condition is related to the number of times the cross-calculation step to the solution selection step is repeated. 5. The method of claim 4,
Wherein the repeating comprises:
Determining whether the number of times the cross calculation step or the solution selection step is repeated is more than a specific number of times; And
Repeating the cross-calculation step or the solution selection step until the number of repeated times becomes the predetermined number of times or more
The method comprising: 12. The method of claim 11,
Wherein the iteration termination condition is related to whether the second life cycle cost has converged. 12. The method of claim 11,
Wherein the repeating comprises:
Storing a second life cycle cost of the lowest value among the calculated second life cycle costs;
Updating the stored second life-cycle cost while the cross-calculation step or the solution selection step is repeated; And
Repeating the cross-calculation step or the solution selection step until the stored second life-cycle cost is not updated while the cross-calculation step or the solution selection step is repeated a predetermined number of times
The method comprising: 12. The method of claim 11,
Wherein the cyclic termination condition is related to the number of times the cross-calculation step or the solution selection step is repeated and whether the second life cycle cost has converged. 12. The method of claim 11,
Wherein the repeating comprises:
Storing a second life cycle cost of the lowest value among the calculated second life cycle costs;
Updating the stored second life-cycle cost while the cross-calculation step or the solution selection step is repeated;
Determining whether the number of times the cross calculation step or the solution selection step is repeated is greater than or equal to a first number of times; And
The cross-calculation step or the solution selection step is repeated until the stored second life-cycle cost is not updated while the repeated number is equal to or greater than the first number and the cross-calculation step or the solution selection step is repeated a second number of times Repeat step
The method comprising: A data receiving unit for receiving data from outside;
A solution computing unit for computing a solution to an operation criterion of at least one component included in a warship based on the received data;
A simulation unit that performs simulation using the calculated solution, calculates a availability of the trap corresponding to the calculated solution, and a life cycle cost of the trap;
A control unit for controlling the solution calculation unit and the simulation unit according to the calculated availability and the life cycle cost; And
A data output unit for outputting information on the calculated solution to the outside,
Lt; / RTI &gt;
The operation unit generates at least two or more solutions for the operation criteria, generates at least two solution objects including an area where one solution is stored, and selects any first solution object among the generated solution objects Selecting an area corresponding to an arbitrary first part from among the areas included in the first solution object, inputting a value in the selected area, and selecting, from among the parts included in the trap, Selecting the corresponding region and inputting a value in the selected region, repeating repeating repeating the step of inputting a value in the selected region and selecting the solution object for both the first solution object and the other solution objects among the generated solution objects, Wherein the simulation unit generates two or more solutions by using the genetic algorithm, Calculated by the trap and operated based on estimation system for performing a simulation using a simulation model of the operation of the ship. 19. The method of claim 18,
Wherein the solution includes Mean Time Between Failure (MTBF) and mean time to repair (MTTR) of each of the components. 19. The method of claim 18,
Wherein the solution calculating unit performs a crossing operation using the generated solution and the solution computing unit performs a mutation operation using the solution in which the crossing operation is performed, To evaluate the fitness,
Wherein the control unit selects at least two solutions out of the solution before the crossing operation, the solution in which the crossing operation is performed, and the solution in which the mutation operation is performed,
Wherein the control unit determines whether or not the repeat end condition is satisfied, and the control unit controls whether the solution calculating unit repeats the crossing operation or the mutation operation according to the determined result.

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