Railroad maintenance support system, and railroad maintenance support method

Abstract

To make a part order plan in which surplus stock of parts is reduced.SOLUTION: A railroad maintenance support system manages a stock volume and an ordering time of a railroad device. The railroad maintenance support system comprises: a failure probability estimation unit which estimates a failure probability using an operation history of a railroad vehicle; a railroad device necessary quantity calculation unit which calculates a necessary quantity of the same type railroad device using the failure probability of the railroad device; a railroad device ordering plan generation unit which receives a probabilistic railroad device necessary quantity and generates an ordering plan on the basis of an operation failure risk of an operation failure occurring due to inventory shortage, costs due to overstock, and a delivery date and a price of the railroad device; and an input / output unit which outputs the generated ordering plan.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a railway maintenance support system and a railway maintenance support method. [Background technology]

[0002] Traditionally, railway operators have generally performed time-based maintenance, which involves periodic maintenance of equipment. However, in recent years, there has been a shift to condition-based maintenance or predictive maintenance, which detects signs of abnormalities or failures based on the deterioration status of equipment and performs maintenance only on equipment that requires it.

[0003] Generally, when managing the inventory of replacement parts for equipment, orders are placed when the stock level falls below a threshold. In particular, railway companies tend to use custom-made equipment and parts rather than general-purpose products, so there is a high risk of inventory shortages if the ordering lead time is long, and it is expected that preventing this will result in excess inventory, which will increase costs.

[0004] Against the above background, Patent Document 1 describes a maintenance management system that determines whether to replace parts based on lifespan information of parts installed on railway vehicles, and processes orders for replacement parts based on delivery date prediction information for the replacement parts. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] WO2021 / 100125 publication Summary of the Invention [Problem to be solved by the invention]

[0006] In Patent Document 1, lifespan information is estimated deterministically. However, lifespan information is merely an estimated value and is essentially probabilistic information. Therefore, it does not take into account the risk of early failure with a low probability.

[0007] Furthermore, in Patent Document 1, parts are replaced and ordered when they reach a certain threshold lifespan. If this threshold is set to a value with some leeway, the risk of a part failure can be reduced, but more parts will need to be replaced, resulting in increased costs due to excess fixed assets and inventory. In other words, Patent Document 1 does not take into consideration the creation of an ordering plan based on a quantitative evaluation of the risk of part failure and the cost of preventing failure.

[0008] The present invention has been made in consideration of the above-mentioned problems, and its purpose is to provide a technology for calculating the probabilistic required quantity of replacement parts and generating a replacement parts ordering plan that balances the risk of failure with the cost of preventing failure. [Means for solving the problem]

[0009] The above-mentioned object can be achieved by a railway maintenance support system that manages the inventory and ordering timing of railway equipment, and that includes a failure probability estimation unit that estimates the probability of failure from the operation history of railway vehicles, a railway equipment required quantity calculation unit that calculates the required quantity of the same type of railway equipment from the failure probability of the railway equipment, a railway equipment ordering plan generation unit that receives the probabilistic railway equipment required quantity and generates an ordering plan based on the risk of operational disruption caused by inventory shortages that will result in operational disruption, the cost of excess inventory, and the delivery date and price of the railway equipment, and an input / output unit that outputs the generated ordering plan. [Effects of the Invention]

[0010] According to the present invention, it is possible to formulate a parts ordering plan that reduces excess inventory of parts. [Brief explanation of the drawings]

[0011] [Figure 1] 1 is a block diagram of a railway maintenance support system according to an embodiment of the present invention (first embodiment); [Figure 2] Example of hardware configuration of railway maintenance support system according to an embodiment of the present invention [Figure 3] 1 is a flowchart illustrating a process for generating a replacement part ordering plan according to an embodiment of the present invention; [Figure 4] FIG. 2 is a diagram illustrating the configuration and processing of a failure probability estimation unit. [Figure 5] Diagram explaining the reduction in failure probability through maintenance [Figure 6] Diagram explaining the failure probability of railway equipment parts [Figure 7] Diagram explaining the required quantity of replacement parts for railway equipment [Figure 8] 1. Example of a replacement parts ordering plan generation screen of the railway maintenance support system according to the embodiment of the present invention [Figure 9] Diagram explaining the probability of replacement parts being out of stock [Figure 10] 1 is a flowchart illustrating an example of an order plan optimization process for a railway maintenance support system according to an embodiment of the present invention. [Figure 11] 1. Example of a replacement parts ordering plan adjustment screen of a railway maintenance support system according to an embodiment of the present invention [Figure 12] Diagram explaining the required quantity of replacement parts when the timing of part replacement is changed [Figure 13] 1 is a diagram illustrating a configuration of a railway maintenance support system according to an embodiment of the present invention (Embodiment 2); [Figure 14] 1 is a diagram illustrating a configuration of a railway maintenance support system according to an embodiment of the present invention (Embodiment 3). [Figure 15] Diagram explaining the failure probability of a single train of railway vehicles [Figure 16] Diagram explaining the failure probability of an entire train [Figure 17] Diagram explaining how to generate a railcar ordering plan [Figure 18] An example of a flowchart showing a railway vehicle order plan optimization process of a railway maintenance support system according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0012] Hereinafter, embodiments of the present invention will be described with reference to the drawings. In each drawing for explaining the embodiments, the same components are given the same names and reference numerals as much as possible, and repeated explanations thereof will be omitted.

[0013] The present invention is not limited to the following examples, and includes various modifications and equivalent configurations within the spirit and scope of the appended claims. For example, the above examples have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to those including all of the described configurations.

[0014] Furthermore, the processing units and processing modules described in the embodiments may be realized in part or in whole in hardware, for example by designing them as integrated circuits, or may be realized in software by having a processor interpret and execute a program that realizes each function.

[0015] The information explained in the embodiment may be a table, a database (DB), or data stored in the main memory. [Example]

[0016] <Outline of the Example> The railway maintenance support system in this embodiment is a system that estimates and aggregates the failure probability for each component that makes up a railway vehicle, calculates the probabilistic replacement requirement for all components of the same type, and generates an ordering plan that takes into account risk and cost based on this probabilistic replacement requirement.

[0017] In the present invention, railroad vehicle parts include wheels, brake shoes, side sliding doors, etc., which are stocked as spare parts in preparation for replacement. Note that the parts listed here are merely examples and are not limiting. Furthermore, since parts of the same type can be replaced, this embodiment is directed to an ordering plan for parts of the same type. <Railway maintenance support system configuration> 1 is a diagram showing an example of the configuration of a railway maintenance support system according to an embodiment of the present invention (embodiment 1). The system includes a railway maintenance support system 10, a traffic control system 20, a parts manufacturing information system 30, a financial control system 40, and an inventory control system 50.

[0018] The railway maintenance support system 10 includes a failure probability estimation unit 11, a replacement part required quantity calculation unit 12, a failure impact cost calculation unit 13, an order plan generation unit 14, an order plan adjustment unit 15, an input / output unit 16, vehicle equipment information 101, maintenance history 102, a maintenance plan 103, maintenance effects 104, and replacement standard information 105.

[0019] The operation management system 20 includes an operation history 21, an operation plan 22, a user history 23, and route information 24.

[0020] The parts manufacturing information system 30 includes delivery date forecast information 31 and price forecast information 32 .

[0021] The financial management system 40 includes budget information 41 .

[0022] The inventory management system 50 includes inventory information 51 and an ordering unit 52 .

[0023] In this embodiment, the units of maintenance are described as parts such as wheels, brake shoes, individual doors, lighting, air conditioning, ATS devices, and other on-board equipment, as well as ground facilities such as switches, crossing gates, and ticket gates, but the units of maintenance can also be vehicles or train formations, and can also be applied to railway equipment in general, which is equipment used to carry out railway operations.

[0024] Therefore, when applied to railway equipment, the replacement part required quantity calculation unit 12 is appropriately called a railway equipment required quantity calculation unit, and the ordering plan generation unit 14 is appropriately called a railway equipment ordering plan generation unit.

[0025] FIG. 2 shows an example of the hardware configuration of a railway maintenance support system according to an embodiment of the present invention.

[0026] The hardware includes a railway maintenance support device 200, an operation management device 201, a parts manufacturing information device 202, a financial management device 203, and an inventory management device 204. These devices include a calculation device, a storage device, and a communication device. Furthermore, the railway maintenance support device 200 includes an input device 205 and an output device 206 in addition to the calculation device, storage device, and communication device. Each of the devices 200, 201, 202, 203, and 204 is realized by a computer connected via a network.

[0027] In this embodiment, a hardware configuration will be described in which each device is implemented as a standalone computer and connected via a network, but multiple devices may be implemented as a single computer, or one device may be implemented as multiple computers.

[0028] It may also be realized using a cloud system that provides computer resources.

[0029] 2 is an example of the hardware configuration, and is not intended to be limiting. Alternatively, for example, the devices 200, 201, 202, 203, and 204 may be arranged in the same device as a railway maintenance support system. <Process flow for generating replacement parts ordering plans> 3 is an example of a flowchart of a replacement parts ordering plan generation process in an embodiment of the present invention. The flow of the process will be explained using this flowchart and the overall configuration shown in FIG. <Failure probability estimation> First, the failure probability of each railcar part is estimated in process 301. Process 301 is executed by the failure probability estimation unit 11.

[0030] A failure probability model is used to estimate the failure probability of a part in process 301. The failure probability model is a model that estimates the failure probability of a target part by inputting the operating time of the target part and the load accumulated on the target part due to the operation of the railway vehicle.

[0031] 4 is a diagram illustrating the configuration and processing of the failure probability estimation unit. The processing of process 301 is executed by the failure probability estimation unit 11. The failure probability estimation unit 11 includes an operating time calculation unit 401, a failure probability model 402, and a maintenance effect calculation unit 404.

[0032] The operation history 21 and operation plan 22 are input to the operation time calculation unit 401. The operation time calculation unit 401 calculates the operation time of the target part to be input to a failure probability model for estimating the failure probability. Note that instead of the operation time, the load accumulated on the target part may also be calculated.

[0033] After calculating the operating time, the operating time (accumulated load) is input into a failure probability model 402 to calculate a failure probability 403. Note that various models are possible for the failure probability model, and for example, the Weibull distribution function described in Japanese Patent Application Laid-Open No. 2023-157092 is used. Note that the failure probability model 402 is not limited to the Weibull distribution function, and other failure probability models may also be used.

[0034] Furthermore, the actual reduction in operating time due to maintenance can be calculated, and the reduction in failure probability can be calculated from the calculated reduction in actual operating time. The reduction in failure probability can be reflected in the failure probability, resulting in a reduction in failure probability.

[0035] Generally speaking, maintenance can range from minor maintenance work such as periodic repairs to large-scale maintenance work such as overhauls. Depending on the scale of the maintenance work, the effect of rejuvenating the equipment lifespan of the target parts is taken into consideration.

[0036] Figure 5 is a diagram that explains the reduction in failure probability through maintenance. For example, if the failure probability due to the operating time up to now is at 501, it will move to position 502 due to the rejuvenation of railway equipment through maintenance, i.e., the effect of reducing the actual operating time. Therefore, the effect of reducing the failure probability through maintenance can be calculated.

[0037] The maintenance history 102 and maintenance plan 103 are input to a maintenance effect calculation unit 404 to calculate the actual reduction in operating time due to maintenance. The maintenance effect calculation unit 404 calculates the actual reduction in operating time according to the scale of maintenance work from the maintenance effect 104.

[0038] This reduction in actual operating time is added to the operating time of the operating time calculation unit 401 and input to the failure probability model 402. Note that although the actual operating time reduced by maintenance has been used as an example, when inputting the accumulated load to the failure probability model, the reduction in the accumulated load may also be used.

[0039] The failure probability 403 is calculated for each component.

[0040] Figure 6 is a diagram explaining the failure probability of railway equipment parts. It shows the failure probability 403 of each part, taking into account the reduction in failure probability through maintenance. The failure probability up to the present time is calculated based on the operation history 21 and maintenance history 102.

[0041] On the other hand, the future failure probability is calculated by the operation plan 22 and the maintenance plan 103. Since the future failure probability is not deterministic and variance occurs, it has a confidence interval 601. The confidence interval 601 is represented by a dashed line, for example, a confidence interval of 5% to 95%.

[0042] The reduction in failure probability due to maintenance is expressed as 602. As shown in Figure 6, the effect of maintenance is expressed as the reduction in actual operating time. In other words, when maintenance is performed, the failure of a part will result in a discontinuous reduction in failure probability at that point. <Calculating the required number of replacement parts> In process 302, the required quantity of replacement parts is calculated by aggregating the failure probabilities of each part estimated in process 301. Process 302 is executed by the replacement part required quantity calculation unit 12 using the failure probability of each part and replacement standard information 105 as input.

[0043] The failure probability of each part is aggregated over time to predict the required number of replacement parts. For example, if wheel AA1 has a 20% failure probability and wheel AA2 has a 10% failure probability, the expected value can be calculated by adding the failure probabilities of both and calculating the required number of spare parts as 0.3 units. By performing a similar calculation, the total required number of replacement parts for the same type of part can be calculated.

[0044] Figure 7 explains the required quantity of replacement parts for railway equipment. As shown in Figure 6, the probability of future failure has variance. In other words, the required quantity of replacement parts also has variance and can be calculated with a confidence interval.

[0045] Furthermore, the target part is replaced when a certain failure probability is reached according to the replacement standard information 105. The transition of the required quantity of replacement parts is calculated taking into consideration future part replacement timings. <Generating replacement part ordering plans> The generation of an ordering plan for replacement parts will now be described. An optimal ordering plan is generated taking into consideration the required quantity of replacement parts calculated in process 302, the cost of excess inventory, the risk of operational disruption due to parts shortages, and the delivery date and price fluctuations of replacement parts.

[0046] In process 303, various parameters and constraints for executing the ordering plan optimization calculation are set. Parameters refer to, for example, the cost coefficient of the objective function described below. Constraints refer to, for example, the budget for ordering parts. The parameters and constraints are not limited to those described here, and other necessary ones may be added.

[0047] Next, an ordering plan optimization calculation is performed in process 304. Process 304 is executed by the ordering plan generation unit 14. The required replacement part quantity calculated by the required replacement part quantity calculation unit 12, the failure impact cost calculated by the failure impact cost calculation unit 13, delivery date prediction information 31, price prediction information 32, budget information 41, and inventory information 51 are used as input to calculate a parts ordering plan.

[0048] The parts ordering plan including the obtained order quantity and ordering time is output to the screen from input / output unit 16 and is approved by the user. Once approved, ordering plan adjustment unit 15 instructs ordering unit 52 to place an order.

[0049] 8 shows an example of a replacement parts ordering plan generation screen of the railway maintenance support system in an embodiment of the present invention. This screen is used in the ordering plan optimization calculation in process 304. The ordering plan generation screen 800 is displayed on the output device 206, and accepts operations from the user via the input device 205.

[0050] The ordering plan means the timing of ordering replacement parts and the order quantity for each order. When the ordering timing and order quantity are determined for the required quantity of replacement parts, a solid line of the inventory quantity 801 of replacement parts is output.

[0051] In this example, the optimization problem is separated into two: optimization of order timing and optimization of order quantity. Optimization of order timing is calculated as combinatorial optimization that determines whether to place an order from among periodic order timing candidates. Optimization of order quantity is calculated as linear optimization. This decomposition of the optimization problem is merely an example, and it may also be calculated simultaneously as a mixed integer programming problem, for example.

[0052] Furthermore, although the decision variables are the order quantity and the order timing, other variables may be added or the number of decision variables may be reduced. For example, if the order timing is fixed periodically, only the order quantity may be formulated as a decision variable. The user may select a decision variable in the decision variable selection field 803 using the input device 205.

[0053] The order planning optimization calculation is formulated as a minimization problem of the objective function of the following equation 1, for example. The order quantity and order timing that minimize this objective function are determined. Note that the objective function is not limited to the one described here, and other indicators may be included.

[0054] The user may select the items to be included in the objective function in an objective function selection field 804 from the input device 205 via the input / output unit 16. Examples of objective functions are as follows:

[0055] [Number 1] (Fixed property tax + inventory carrying cost) × (inventory quantity) + (operational disruption impact amount) × (probability of inventory shortage) + (parts price) × (order quantity) × (order timing) (Equation 1) The first term represents the cost of excess inventory caused by holding spare parts in stock. The second term represents the impact when there is a shortage of spare parts in stock, rendering the affected railcar unusable and causing operational disruption, i.e., the risk of inventory shortages. The third term represents the ordering cost incurred when ordering replacement parts.

[0056] There is a trade-off between the cost of excess inventory and the risk of inventory shortages. For example, if you reduce the inventory of spare parts to reduce the cost of excess inventory, the risk of inventory shortages increases.

[0057] On the other hand, if you hold a large amount of inventory to reduce the risk of inventory shortages, the cost of excess inventory increases.By balancing the cost of excess inventory and the risk of inventory shortages while minimizing ordering costs, we can generate an optimal replacement parts ordering plan.

[0058] The risk of operational disruption is calculated as the impact amount based on the number of affected passengers and the importance of the target line when the target railway vehicle cannot operate due to a shortage of parts.

[0059] Although the present embodiment is described as optimization, it does not necessarily mean that the most efficient maintenance plan is obtained, but rather that a rational maintenance plan is obtained when actually performing maintenance.

[0060] Although the optimal maintenance plan may not necessarily be achieved due to factors such as revisions to the maintenance plan, fluctuations in parts prices, and damage to railway equipment due to disasters, the resulting maintenance plan is more rational than a maintenance plan based on manual experience and intuition.

[0061] Fig. 9 is a diagram for explaining the probability of a shortage of replacement parts inventory. Fig. 9 is an example in which the horizontal axis shows the required quantity of replacement parts and the vertical axis shows the probability density of the required quantity of replacement parts when a certain time cross section 802 in Fig. 8 is cut out.

[0062] This probability density means that, for example, if 200 replacement parts are needed on average, and if, by bad luck, multiple part failures occur, there is a 5% chance that 300 replacement parts will be needed; conversely, if, by good luck, few part failures occur, there is a 5% chance that only 100 replacement parts will be needed.

[0063] When inventory is calculated from the decision variables of order timing and order quantity, the area enclosed by the probability density of the required replacement parts and the line of inventory quantity indicates the probability of an inventory shortage. For example, if the inventory quantity is 260 units, the probability that the required replacement parts will exceed 260 units and cause an inventory shortage is calculated to be 15%.

[0064] 10 is a flowchart showing an example of the order plan optimization process of the railway maintenance support system according to the embodiment of the present invention, illustrating the calculation process in process 304.

[0065] First, optimization of order timing is performed in process 1001. In the first calculation, the decision variable for the order quantity is fixed at an arbitrary value, and the order timing that minimizes the objective function of (Equation 1) is determined.

[0066] Next, in process 1002, the order timing determined in process 1001 is fixed, and the order quantity is optimized to minimize the objective function of (Equation 1).

[0067] After the order quantity is determined, optimization is repeated with this order quantity as the fixed value of the order quantity in process 1001. That is, optimization is performed while alternating between fixing the order timing and order quantity in processes 1001 and 1002. In process 1003, the convergence conditions for this repeated calculation are confirmed. For example, when the difference in order timing and order quantity between loops becomes sufficiently small, the repeated calculation is terminated.

[0068] Finally, in step 1004, the obtained optimal solution for order timing and order quantity is output as an order plan, and the process ends.

[0069] Changes in order timing and order quantity may be specified by the user or generated by this system. <Adjustment of replacement parts ordering plan> In process 305, after generating the optimal ordering plan, the ordering plan is adjusted depending on the situation. The ordering plan is merely a solution that minimizes the value of the set objective function. Adjustments by the user are included to determine the final ordering plan.

[0070] FIG. 11 shows an example of a replacement parts ordering plan adjustment screen of the railway maintenance support system in the embodiment of the present invention.

[0071] The order plan adjustment screen 1100 displays the required quantity of replacement parts and the inventory plan based on the optimal order plan, and the user refers to this information to adjust the order plan. The order plan adjustment screen 1100 is displayed on the output device 206 via the input / output unit 16, and the user performs operations using the input device 205. The input / output unit 16 accepts user input from the input device 205, and the order plan generation unit 14 recalculates the order plan.

[0072] The graph shows 1104 the inventory amount based on the ordering plan, 1105 the average amount of replacement parts required, 1106 the maximum amount of replacement parts required, and 1107 the minimum amount of replacement parts required.

[0073] For example, if the probability of inventory shortage below the maximum required quantity of replacement parts in part 1101 of the inventory plan is unacceptable, adjustments are made such as increasing the order quantity in 1102 to reduce the probability of inventory shortage at time 1101.

[0074] This adjustment is performed by the user moving the part where he / she wishes to change the order quantity via the input device 205. Furthermore, it is also possible to set rules for the probability of stock shortages, etc., so that the adjustment is automatic.

[0075] When the ordering plan is changed, the change in the objective function before and after the plan adjustment is displayed in 1103. In the example of Figure 11, increasing the order quantity increases the overstock cost and ordering cost, while decreasing the inventory shortage cost. However, when compared with the optimal ordering plan obtained by optimization calculation, the total objective function value has increased. The user can confirm these values ​​while deciding on the final ordering plan.

[0076] Next, in step 306, after adjusting the ordering plan, it is determined whether to add new constraints and change the ordering plan again. If constraints are added and recalculation is to be performed, the process is repeated from step 303. For example, a constraint on the inventory shortage probability is added and recalculation is performed.

[0077] The steps 305 and 306 are executed by the ordering plan adjustment unit 15.

[0078] Finally, the adjusted ordering plan is determined as the final plan and output in process 307. This ordering plan is input to ordering unit 52, where actual parts ordering is carried out.

[0079] As described above, with this embodiment, the maintenance department that manages the timing of parts replacement for railway vehicles can quantitatively evaluate the risk of operational disruptions, reduce excess parts inventory while ensuring the reliability of the railway system, and develop rational parts ordering plans. [Example]

[0080] <Outline of the Example> In the second embodiment, the railway maintenance support system in the first embodiment is configured by adding additional processing to the method of calculating the required quantity of replacement parts and the method of generating a replacement part ordering plan.

[0081] FIG. 12 is a diagram for explaining the required quantity of replacement parts when the timing of part replacement is changed.

[0082] In the calculation of the required quantity of replacement parts in the first embodiment, parts are replaced when a certain failure probability is reached according to the replacement standard information 105. Here, the replacement standard for parts is fixed, and the usage amount of parts is also calculated according to the fixed standard. In other words, as shown in FIG. 12, the required quantity of replacement parts 1201 once calculated does not change.

[0083] On the other hand, if the part replacement judgment is changed for each part, the quantity of parts used changes, and the required amount of replacement parts also changes each time. For example, as shown in 1202 in Figure 12, the required amount of replacement parts increases or decreases compared to the required amount of replacement parts 1201 when the replacement standard is constant.

[0084] Part replacement should also be determined based on risk. Therefore, part replacement decisions are incorporated into the optimization calculation of replacement part ordering plans. Part replacement decisions involve adjusting the timing of part replacement.

[0085] FIG. 13 is a configuration diagram of a railway maintenance support system according to an embodiment of the present invention (embodiment 2).

[0086] The difference from the configuration diagram of the first embodiment is the parts usage calculation unit 1301. The replacement timing of each part is also incorporated into the optimization problem in the ordering plan generation unit 14. That is, in addition to determining the ordering timing and order amount, the replacement timing of each part is also determined.

[0087] When the maintenance target is railway equipment instead of parts, the part usage amount calculation unit 1301 may be called a railway equipment usage amount calculation unit.

[0088] The timing of part replacement is adjusted by balancing the cost of preparing replacement parts with the risk of operational disruption. For example, delaying the timing of part replacement increases the probability of failure, but has cost advantages because fewer replacement parts are used.

[0089] If the part has little impact even if it fails, it is possible to take more risks and reduce costs. On the other hand, although the number of replacement parts required increases, it is possible to reduce the risk of operational disruption by speeding up the replacement timing and replacing them with a lower failure probability.

[0090] When the replacement timing of each part changes, the replacement frequency is no longer constant, and the amount of parts used also changes. The part usage calculation unit 1301 calculates the amount of parts used using the part replacement decision as input. This amount of parts used is input to the replacement part required calculation unit, and the amount of replacement parts required is updated.

[0091] The required quantity of replacement parts affects the timing and quantity of orders, so the timing and quantity of orders are determined based on the replacement timing of each part and the required quantity of replacement parts that changes depending on the part replacement timing.

[0092] As described above, this embodiment allows for the creation of a rational parts ordering plan while also taking into consideration the timing of parts replacement, thereby ensuring the reliability of the railway system while further reducing excess parts inventory and reducing costs. [Example]

[0093] <Outline of the Example> In the third embodiment, a railway maintenance support system in which the target of ordering planning is a railway vehicle formation will be described in relation to the railway maintenance support system in the first embodiment.

[0094] FIG. 14 is a configuration diagram of a railway maintenance support system according to an embodiment of the present invention (Embodiment 3).

[0095] Since the target is railway vehicle formations, the railway maintenance support system 10 is based on the configuration diagram in Example 1 and is equipped with an order plan generation unit 14, an operable formation number calculation unit 1403, formation vehicle information 1404, and allocated formation number 1405.

[0096] The vehicle manufacturing information system 1410 also includes vehicle delivery date forecast information 1411 and vehicle price forecast information 1412. The order management system 1420 also includes a vehicle ordering unit 1421.

[0097] When the maintenance target is railway equipment, the vehicle manufacturing information system 1410 may be called a railway equipment manufacturing information system, the vehicle delivery date forecast information 1411 may be called railway equipment delivery date forecast information, the vehicle price forecast information 1412 may be called railway equipment price forecast information, and the vehicle ordering unit 1421 may be called a railway equipment ordering unit.

[0098] In this embodiment, when generating an ordering plan, the failure probability for each train set is estimated and the number of operable train sets is calculated.

[0099] Figure 15 is a diagram illustrating the failure probability of a single train set of railway vehicles. The failure probability of a train set increases with age.

[0100] Fig. 16 is a diagram explaining the failure probability of all railway vehicle formations. By estimating the failure probability for each formation for all formations, it is possible to calculate the failure probability for each formation as shown in Fig. 16. In other words, based on the failure probability for each formation, the number of operable formations as a whole can be calculated using (Equation 2).

[0101] [Number 2] (Number of operable trains) = Σ(1 - failure probability) (Equation 2) In general railway vehicle operations, there are train sets used in actual operation (the number of allocated train sets) and spare train sets. In other words, if the number of operable train sets calculated from the failure probability of all train sets meets the number of allocated train sets, even if a failure occurs in a train set used in operation, operation disruptions can be prevented by allocating the spare train set.

[0102] Figure 17 is a diagram explaining how to generate a railway vehicle ordering plan. The ordering plan determines the timing of ordering and removal and the number of train sets to be ordered and removed. Once the timing of ordering and removal and the number of train sets to be ordered and removed are provisionally determined, the total number of train sets 1701 is calculated.

[0103] Furthermore, the number of operable trains 1702 is calculated from the failure probability of each train set by (Equation 2). Since the failure probability can be calculated with a variance, the number of operable trains 1702 also has a variance.

[0104] If the number of operable trains 1702 exceeds the number of allocated trains 1405, the risk of operational disruption is zero, but if there is a possibility that the number is less than the allocated number of trains, an operational disruption risk occurs.

[0105] FIG. 18 is an example of a flowchart showing the process of optimizing an ordering plan for railway cars in the railway maintenance support system according to the embodiment of the present invention.

[0106] The method for generating an ordering plan for railway car sets in this embodiment will be described in accordance with this processing flowchart.

[0107] First, in process 1801, parameters and constraints for the order planning optimization calculation are set. Parameters refer to, for example, the cost coefficient of the objective function described below. Constraints refer to, for example, the number of orders placed, the number of cancellations, the number of train sets to be cancelled at one time, the budget for ordering, etc. The parameters and constraints are not limited to those described here, and other necessary ones may be added.

[0108] Next, in step 1802, the timing of ordering / removal and the number of train sets to be ordered / removed, which are decision variables, are set.

[0109] Next, in step 1803, the transition of the total number of train sets shown in step 1701 is calculated according to the set timing of ordering and removal and the number of train sets to be ordered and removed. In the case of removal, the oldest train set is removed first.

[0110] After the transition of the total number of trains has been determined, in step 1804, the failure probability of each train is estimated and the number of operable trains 1702 is calculated.

[0111] After calculating the number of operable train sets 1702, an objective function is calculated in step 1805. The objective function is, for example, the following (Equation 3).

[0112] [Number 3] (Fixed asset tax + maintenance costs) × (total number of trains) + (operational disruption impact amount) × (probability of shortage of allocated trains) + (order price) × (number of ordered trains) × (order timing) + (retirement costs) × (number of retired trains) (Equation 3) The first term shows the cost of owning trains relative to the number of trains owned by the railway company. The second term shows the impact when service disruptions occur due to the number of operable trains falling below the allocated number of trains, i.e., the risk of service disruptions. The third term shows the cost of ordering railway vehicles. The fourth term shows the cost of work required to remove trains.

[0113] In particular, there is a trade-off between the cost of owning train sets and the risk of operational disruptions. For example, if the number of train sets is reduced in order to reduce the cost of owning train sets, the number of operational train sets will naturally decrease, increasing the risk of operational disruptions due to the inability to meet the required number of train sets.

[0114] On the other hand, increasing the number of trains in order to reduce the risk of operational disruptions increases the cost of owning trains. Therefore, by balancing the cost of owning trains with the risk of operational disruptions and minimizing the ordering and disposal costs, an optimal rolling stock ordering plan can be generated.

[0115] The objective function described here is not limited to this and may include other indicators. Furthermore, the decision variables are the timing of ordering and removal and the number of train sets ordered and removed, but other variables may be added or the number of decision variables may be reduced. For example, if the timing of ordering and removal is fixed at regular intervals, the objective function may be set to the number of train sets ordered and removed as the only decision variable.

[0116] After calculating the objective function, the convergence conditions are checked in process 1806. For example, when the difference in the decision variables between loops becomes sufficiently small, the repeated calculations are terminated. If the convergence conditions are not met, the timing of ordering and removal and the number of train sets to be ordered and removed are set to improve the objective function to a better value, and the process is repeated from process 1802. If the convergence conditions are met, the number of orders and the order timing found in process 1807 are output as an ordering plan and the process is terminated.

[0117] As described above, with this embodiment, even when targeting railway vehicles, it is possible to quantitatively evaluate the risk of operational disruptions, ensure appropriate reliability, reduce the number of surplus railway vehicle formations, and formulate an ordering plan.

[0118] It should be noted that the present invention is not limited to the above-described embodiment, and includes various modifications. For example, the above-described embodiment has been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to an embodiment having all of the described configurations. [Explanation of symbols]

[0119] 10:Railway maintenance support system 11: Failure probability estimation unit 12: Replacement part quantity calculation section 13:Failure impact amount calculation department 14: Order plan generation section 15: Order Planning and Adjustment Department 16: Input / output section 20: Traffic control system 21: Operation history 22: Operation plan 23: User history 24: Route information 30: Parts manufacturing information system 31: Delivery date forecast information 32: Price forecast information 40: Financial Management System 41: Budget Information 50: Inventory management system 51: Stock information 52: Ordering Department 101: Vehicle equipment information 102: Maintenance history 103: Conservation Planning 104: Conservation Effects 105: Exchange criteria information 1301: Parts usage calculation unit 1403: Calculation unit for the number of operable trains 1405: Number of allocated trains

Claims

1. A railway maintenance support system that manages the inventory and ordering timing of railway equipment, a failure probability estimation unit that estimates a failure probability based on the operation history of the railway vehicle; a railway equipment required quantity calculation unit that calculates the required quantity of railway equipment of the same type based on the failure probability of the railway equipment; a railway equipment ordering plan generation unit that receives probabilistic railway equipment requirements and generates an ordering plan based on the risk of service disruption due to inventory shortages, costs due to excess inventory, and delivery dates and prices of the railway equipment; A railway maintenance support system having an input / output unit that outputs the generated ordering plan.

2. 2. The railway maintenance support system according to claim 1, Railway equipment is a component of the railway maintenance support system.

3. 2. The railway maintenance support system according to claim 1, Railway equipment is a railway maintenance support system for trains.

4. 2. The railway maintenance support system according to claim 1, The railway maintenance support system, wherein the railway equipment required amount calculation unit changes the replacement period of the railway equipment and calculates the amount of usage of the railway equipment based on the replacement period of the railway equipment.

5. 2. The railway maintenance support system according to claim 1, The railway equipment required quantity calculation unit is a railway maintenance support system that estimates the failure probability for each piece of railway equipment based on operation history, future operation plans, maintenance history, and future maintenance plans, and calculates the required quantity of railway equipment by adding up the calculated failure probabilities.

6. 2. The railway maintenance support system according to claim 1, the input / output unit outputs a railway equipment required quantity and a railway equipment inventory quantity plan based on the ordering plan generated by the railway equipment ordering plan generation unit, and receives changes to the order quantity and order timing; The railway equipment order plan generation unit is a railway maintenance support system that recalculates the order plan based on the received order quantity and order timing.

7. 2. The railway maintenance support system according to claim 1, The railway maintenance support system calculates the operational disruption risk used by the railway equipment ordering plan generation unit to create ordering plans based on the impact amount calculated based on the number of affected users and the importance of the target line when railway operations cannot be carried out due to a shortage of railway equipment.

8. 2. The railway maintenance support system according to claim 1, A railway maintenance support system that includes a failure probability estimation unit that calculates the reduction in actual operating time of parts due to maintenance work, and a maintenance effect calculation unit that calculates the reduction in failure probability based on the calculated reduction effect.

9. A railway maintenance support method for managing inventory and ordering timing of railway equipment, comprising: The failure probability estimation unit estimates the failure probability based on the operation history of the railway vehicle, A railway equipment required quantity calculation unit calculates the required quantity of the same type of railway equipment based on the failure probability of the railway equipment, The railway equipment ordering plan generation unit receives the probabilistic railway equipment requirements and generates an ordering plan based on the risk of service disruption due to inventory shortages, the cost of excess inventory, and the delivery date and price of the railway equipment. A railway maintenance support method in which an input / output unit outputs a generated ordering plan.

10. 10. The railway maintenance support method according to claim 9, Railway maintenance support method in which railway equipment is a part.

11. 10. The railway maintenance support method according to claim 9, Railway equipment is a railway maintenance support method for trains.

Images