Warehouse facility maintenance management system and maintenance management method

Abstract

To provide a maintenance management system and a maintenance management method for a warehouse facility, in which prevention of reduction in cargo handling efficiency in maintenance work in the warehouse facility and improvement of safety can both be achieved.SOLUTION: Provided is a maintenance management system 10 for a warehouse facility 1, having a storage area 4 that temporarily stores a large number of packages 3 and a plurality of cargo handling devices 5 that perform cargo handling of the packages 3 between the storage are 4 and a plurality of transport machines 2, including: a plurality of sensing devices 11 targeted for the plurality of cargo handling devices 5; and an arithmetic unit 12. The arithmetic unit 12 executes data processing of predicting a degree of influence on reduction in cargo handling efficiency in the warehouse facility 1 to be caused by execution of maintenance work based on detected data detected by the plurality of sensing devices 11 and scheduled in maintenance scheduled data 20 indicating a schedule of maintenance work of the plurality of cargo handling devices 5, and data processing of correcting the maintenance scheduled data 20 based on the predicted degree of influence.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to a maintenance management system and a maintenance management method for a warehouse equipment, and more specifically, to a maintenance management system and a maintenance management method for a warehouse facility that manages maintenance work in a warehouse facility having a storage area for temporarily storing a large number of items of luggage and a plurality of loading and unloading devices for loading and unloading the items of luggage between the storage area and a plurality of transport devices. [Background technology]

[0002] Currently, efforts are underway to use artificial intelligence (AI) to improve productivity at container terminals. Specifically, efforts are underway to use AI to analyze the tacit knowledge of skilled workers' crane operation and turn it into explicit knowledge, to remotely control or automate crane operation, and to use AI to analyze container product names, shipper names, and past loading and unloading dates to optimize container storage locations. These efforts will improve the efficiency of loading and unloading at container terminals. Thus, there is a demand for improved loading and unloading efficiency at warehouse facilities such as container terminals, where cargo is loaded and unloaded day and night.

[0003] On the other hand, in order to ensure the safety of loading and unloading operations at container terminals, it is also important to perform maintenance work (preventive maintenance work, predictive maintenance work) on a large number of various types of loading and unloading equipment, such as gantry cranes, transfer cranes (yard cranes), and on-site trailers. If the maintenance work on the loading and unloading equipment is insufficient and the loading and unloading equipment stops during loading and unloading operations, not only will loading and unloading operations stop, but a great deal of effort will be spent on its recovery, and loading and unloading efficiency will decrease significantly. Therefore, maintenance work on loading and unloading equipment is important in warehouse facilities to ensure loading and unloading efficiency. Regarding this maintenance work, various systems and methods have been proposed that use AI and the like to detect abnormalities in targets early and manage schedules related to maintenance work (see Patent Documents 1 and 2).

[0004] However, when maintenance work is performed, the remaining loading / unloading equipment excluding the loading / unloading equipment that is the target of the maintenance work is used for loading / unloading work, and the loading / unloading efficiency is reduced compared to loading / unloading work using all loading / unloading equipment. Thus, the implementation status of maintenance work for loading / unloading equipment has a high degree of impact on the reduction in loading / unloading efficiency. The inventions described in Patent Documents 1 and 2 do not take into account the impact of the implementation of maintenance work on the reduction in loading / unloading efficiency. Therefore, the loading / unloading efficiency is reduced due to predictive maintenance work and scheduled preventive maintenance work that are performed by early detection of abnormalities in the target equipment. Thus, there is room for improvement in order to simultaneously suppress the reduction in loading / unloading efficiency and improve safety in maintenance work at warehouse facilities. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Patent Publication No. 2021-182393 [Patent Document 2] JP 2022-010971 A Summary of the Invention [Problem to be solved by the invention]

[0006] An object of the present invention is to provide a maintenance management system and a maintenance management method for a warehouse facility that can simultaneously suppress a decrease in loading and unloading efficiency during maintenance work at the warehouse facility and improve safety. [Means for solving the problem]

[0007] The present invention provides a warehouse facility maintenance management system that achieves the above object, comprising: a storage area for temporarily storing a large number of items; and a plurality of loading and unloading devices for loading and unloading the items between the storage area and a plurality of transport devices. The system further includes a calculation device for correcting maintenance schedule data in which the date and time of maintenance work for the loading and unloading equipment in the warehouse is scheduled. In the warehouse facility maintenance management system, the arithmetic device the storage device further includes an auxiliary storage unit in which the maintenance schedule data, in which at least the date and time of predictive maintenance work based on a plurality of detection data detected by a plurality of sensing devices targeting the plurality of loading / unloading devices is scheduled as the date and time of the maintenance work on the loading / unloading devices, and loading / unloading schedule data indicating the number of loading / unloading operations at the warehouse facility for each date and time based on the scheduled departure and arrival dates and times of the plurality of transport devices, are stored, and a central processing unit is further configured to, when at least one of the maintenance schedule data and the loading / unloading schedule data stored in the auxiliary storage unit is updated, change the date and time of the maintenance work in the maintenance schedule data that is scheduled for a date and time when the loading / unloading efficiency of the warehouse facility is predicted to be lower than a target loading / unloading efficiency, assuming that the number of loading / unloading operations for a predetermined date and time scheduled in the loading / unloading schedule data will be performed by the remaining loading / unloading devices from among the plurality of loading / unloading devices excluding the loading / unloading devices that are the target of the maintenance work scheduled for the predetermined date and time in the maintenance schedule data, A data processing method for correcting the maintenance schedule data Reason It is characterized by carrying out

[0008] The present invention relates to a method for managing a warehouse facility, the method comprising: temporarily storing a large number of items in a storage area; and using a plurality of loading and unloading devices to load and unload the items between the storage area and a transport device. The maintenance schedule data, which indicates the date and time of the maintenance work for the loading and unloading equipment in the warehouse facility, is corrected by a computing device. In the method of maintaining and managing warehouse facilities, When at least one of the maintenance schedule data, which is stored in the auxiliary storage unit of the computing device and which includes at least a predictive maintenance work date and time based on a plurality of detection data detected by a plurality of sensing devices targeting the plurality of cargo handling equipment as a date and time of the maintenance work on the cargo handling equipment, and the cargo handling schedule data, which indicates the number of cargo handling operations at the warehouse facility for each date and time based on the scheduled departure and arrival dates and times of the plurality of transportation equipment, is updated, arithmetic device Central Processing Unit Due to Assuming that the number of loading and unloading operations at a predetermined date and time scheduled in the loading and unloading schedule data will be performed by the remaining loading and unloading equipment from among the plurality of loading and unloading equipment, excluding the loading and unloading equipment that is the target of the maintenance work scheduled at the predetermined date and time in the maintenance schedule data, changing the date and time of the maintenance work scheduled for a date and time when the loading and unloading efficiency of the warehouse facility is predicted to be lower than a target loading and unloading efficiency, The maintenance schedule data is modified. Effect of the Invention

[0009] According to the present invention, the revised maintenance schedule data takes into account the degree of impact of maintenance work on a decrease in loading and unloading efficiency compared to the original maintenance schedule data, thereby making it possible to suppress a decrease in loading and unloading efficiency of the warehouse facility when maintenance work is actually performed. In other words, if the maintenance work shown in the schedule is predicted to have a high impact on a decrease in loading and unloading efficiency, the schedule is changed, and if the maintenance work is predicted to have a low impact on a decrease in loading and unloading efficiency, the schedule is not changed. In this way, the present invention is configured to improve the safety of the warehouse facility by performing maintenance work on loading and unloading equipment, but by suppressing a decrease in loading and unloading efficiency due to the maintenance work, it is possible to achieve both suppression of a decrease in loading and unloading efficiency and improvement of safety. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 is an explanatory diagram illustrating a warehouse facility. [Diagram 2] FIG. 1 is an explanatory diagram illustrating an embodiment of a maintenance management system for a warehouse facility. [Diagram 3] FIG. 2 is an explanatory diagram illustrating an example of maintenance schedule data. [Figure 4] FIG. 2 is an explanatory diagram illustrating loading and unloading schedule data. [Diagram 5] FIG. 11 is a flow diagram illustrating a method for updating maintenance schedule data in a method for managing maintenance of a warehouse facility. [Figure 6] 11 is a flow diagram illustrating a method for updating loading and unloading schedule data in a warehouse facility maintenance management method. FIG. [Figure 7]FIG. 11 is a flow diagram illustrating a method for revising maintenance schedule data in the method for managing maintenance of a warehouse facility. [Figure 8] FIG. 13 is an explanatory diagram illustrating an example of corrected maintenance schedule data. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS A warehouse facility maintenance management system and a maintenance management method according to the present invention will be described below based on an embodiment shown in the drawings.

[0012] The warehouse facility 1 illustrated in FIG. 1 is a container terminal. The warehouse facility 1 may be any of various known warehouse facilities such as a container terminal or a logistics warehouse that is a logistics base. In the warehouse facility 1, luggage 3 transported by a plurality of transport devices 2 is received and temporarily stored, and the stored luggage 3 is transported to the outside by the plurality of transport devices 2. The transport devices 2 are various known vehicles that transport luggage 3, and in a container terminal, a ship or a vehicle that transports containers as luggage 3 is exemplified. The warehouse facility 1 has a storage area 4 and multiple loading and unloading devices 5. The storage area 4 is an area where a large number of luggage 3 are temporarily stored. The storage area 4 in the container terminal corresponds to a storage lane where a large number of containers are stored as luggage 3. The multiple loading and unloading devices 5 are various known loading and unloading devices that handle luggage 3 in the warehouse facility 1. The multiple loading and unloading devices 5 in the container terminal correspond to a gantry crane, a transfer crane (yard crane), and an in-house chassis that handle and transport containers as luggage 3.

[0013] 2 manages maintenance work for a plurality of pieces of cargo handling equipment 5 in a warehouse facility 1. The maintenance management system 10 includes a plurality of sensing devices 11 and a computing device 12.

[0014] The multiple sensing devices 11 sense the multiple loading equipment 5, and various known sensing devices can be used. Examples of the sensing devices include a wide variety of sensors that detect the position of the loading equipment 5, the temperature of a driving source such as a motor of the loading equipment 5, vibration of a moving part of the loading equipment 5, an image capturing an entire image or a partial image of the loading equipment 5, and a power supply status of devices mounted on the loading equipment 5. The multiple sensing devices 11 may be arranged in various places in the warehouse facility 1, or may be installed in each of the multiple loading equipment 5.

[0015] The arithmetic device 12 includes a central processing unit (CPU) 13, a main storage unit (memory) 14, an auxiliary storage unit (e.g., HDD) 15, and an input / output unit (e.g., a mouse, a keyboard, a network interface) 16. The auxiliary storage unit 15 stores maintenance schedule data 20 and loading / unloading schedule data 21. The arithmetic device 12 is connected to a plurality of sensing devices 11 so that a plurality of detection data acquired by the plurality of sensing devices 11 can be input. The arithmetic device 12 is also connected to communication devices and a management device mounted on the transportation equipment 2 (not shown) so that data transmitted from the communication devices and a management device that manages the operation of the transportation equipment 2 can be input. A network (including the Internet) may be used to connect the arithmetic device 12 to other devices.

[0016] 3 shows an example of the maintenance schedule data 20. In the maintenance schedule data 20, maintenance work (PV, PD) is scheduled for each of the multiple pieces of cargo handling equipment 5 (a, b, . . .) for each date and time (D1, . . ., Dn) within a set period. The maintenance work for the multiple pieces of cargo handling equipment 5 is carried out at the date and time scheduled in the maintenance schedule data 20.

[0017] The maintenance work does not include post-event maintenance work, and refers to work such as inspection, maintenance, and part replacement that is performed before a malfunction or failure occurs in the multiple loading and unloading equipment 5. The maintenance work includes preventive maintenance work PV and predictive maintenance work (deterioration sign diagnosis, condition-based maintenance) PD. The preventive maintenance work PV is a regular inspection work performed at a predetermined period set in advance. The preventive maintenance work PV is scheduled for the date and time of the work based on a predetermined period. Examples of the predetermined period include a short-term period such as one month and a long-term period such as one year. It is desirable to perform both short-term and long-term regular inspection work for the preventive maintenance work PV. The predictive maintenance work PD refers to work such as inspection, maintenance, and part replacement that is performed by constantly monitoring the multiple loading and unloading equipment 5 and when a sign of malfunction or failure is predicted by the monitoring. Unlike the preventive maintenance work PV, the date and time of the work for the predictive maintenance work PD is predicted based on the monitoring of the loading and unloading equipment 5. That is, the preventive maintenance work PV can be scheduled for a date and time taking into consideration the scheduled loading and unloading work at the warehouse facility 1, but the date and time of the predictive maintenance work PD is predicted suddenly without considering the scheduled loading and unloading work. The unit time for the maintenance work is not limited to one day, but may be 12 hours, for example. The maintenance work may take one day or multiple days.

[0018] The set period of the maintenance schedule data 20 can be set arbitrarily. When a date and time of the preventive maintenance work PV is scheduled, the set period may be set to a period including the scheduled date and time. The set period may also be set to a short period such as one week, in which case, it is advisable to prepare another schedule data in which the date and time of the preventive maintenance work PV outside the set period is scheduled.

[0019] The maintenance schedule data 20 is first scheduled by filling in the table with the dates and times of preventive maintenance work PV based on a predetermined period. Then, the maintenance schedule data 20 is updated by filling in the table with the date and time of the predicted preventive maintenance work PD every time the date and time of the predictive maintenance work PD based on monitoring is predicted.

[0020] FIG. 4 shows an example of the loading / unloading schedule data 21. The loading / unloading schedule data 21 indicates the predicted number of loading / unloading operations in the warehouse facility 1 for each date and time (D1, . . . , Dn) within a set period. The loading / unloading schedule data 21 is not limited to a frequency distribution indicating the number of loading / unloading operations for each date and time, and may be data in which loading / unloading operations for multiple loading / unloading devices 5 are accumulated for each date and time, similar to the maintenance schedule data 20. In this embodiment, as described later, it is sufficient to predict the degree of influence of the maintenance work on the decrease in loading / unloading efficiency, and a detailed loading / unloading operation schedule for each loading / unloading device 5 is not particularly required, but in some cases a detailed loading / unloading operation schedule for each loading / unloading device 5 may be required.

[0021] The number of loading and unloading operations at the warehouse facility 1 per day is roughly proportional to the number of incoming and outgoing cargo, which is the sum of the number of cargoes 3 entering the warehouse facility 1 and the number of cargoes 3 leaving the warehouse facility 1. Therefore, the number of incoming and outgoing cargo at the warehouse facility 1 may be used instead of the number of loading and unloading operations. The change in the number of loading and unloading operations (number of incoming and outgoing cargo) at the warehouse facility 1 depends on the arrival and departure status of multiple pieces of transportation equipment 2. As the number of incoming and outgoing transportation equipment 2 increases, the number of loading and unloading operations increases, and as the number of incoming and outgoing transportation equipment 2 decreases, the number of loading and unloading operations decreases. The number of loading and unloading operations at a container terminal depends on the arrival and departure status of ships transporting containers as cargo 3, among other types of transportation equipment 2.

[0022] 5 to 7 show an example of a maintenance management method for the warehouse facility 1. First, a predetermined program stored in the auxiliary storage unit 15 of the computing device 12 is started and executed. The predetermined program causes the computing device 12 to execute each procedure. Specifically, the predetermined program causes the computing device 12 to execute each procedure (S110) to (S140) illustrated in FIG. 5 to update the maintenance schedule data 20. The predetermined program also causes the computing device 12 to execute each procedure (S210) to (S230) illustrated in FIG. 6 to update the loading and unloading schedule data 21. Furthermore, the predetermined program also causes the computing device 12 to execute each procedure (S310) to (S350) illustrated in FIG. 7 to correct the maintenance schedule data 20. The groups of steps illustrated in FIG. 5, FIG. 6, and FIG. 7 are performed independently of each other. The contents of each step are described below in detail.

[0023] By each step illustrated in FIG. 5, the date and time of the preventive maintenance work PV is predicted, and the predicted date and time of the preventive maintenance work PV is scheduled in the maintenance schedule data 20. Before each step is performed, the maintenance schedule data 20 is assumed to have the date and time of the preventive maintenance work PV scheduled based on a predetermined period. Each step is repeatedly performed at a predetermined cycle. The predetermined cycle can be set arbitrarily, but may also be set to a sampling cycle of multiple sensing devices 11, or may be set to a cycle in which the sampling cycle is repeated multiple times. Examples of a cycle in which the sampling cycle is repeated multiple times include one hour or one day.

[0024] In the step of acquiring the detection data (S110), the plurality of pieces of detection data acquired by the plurality of sensing devices 11 are input to the calculation device 12. The plurality of pieces of detection data acquired by the plurality of sensing devices are stored in the auxiliary memory unit 15 of the calculation device 12.

[0025] In the step (S120) of predicting the date and time of the predictive maintenance work PD, the calculation device 12 executes data processing to predict the date and time when a malfunction or failure will occur in the loading and unloading equipment 5 based on multiple detection data, and predict a date and time before the predicted date and time as the date and time of the predictive maintenance work PD. Various known prediction methods can be used to predict the date and time when a malfunction or failure will occur. An example of the prediction method is a method of comparing the detection data with a preset threshold value to identify a symptom of a malfunction or failure and predict the date and time when the malfunction or failure will occur. The threshold value and the predicted date and time used in this method can be set in advance based on an empirical rule obtained from the history of past malfunctions and failures. The predicted date and time is specified as the date and time when a period based on an empirical rule has elapsed since the symptom of a malfunction or failure was identified. The history of past malfunctions and failures can be the operation history of the warehouse facility 1, as well as the accumulation of many experiments and tests and the accumulation of computer simulation results. In addition, an example of the prediction method is a method of predicting the date and time using multiple detection data and a prediction model created by supervised machine learning.

[0026] It is desirable to use an index indicating safety as the criteria for setting the threshold value of the prediction method and the objective function in machine learning. As an index indicating safety, the specifications of various parts constituting the cargo handling equipment 5 and regular inspection items can be used. For example, when the cargo handling equipment 5 is a crane, examples of an index indicating the safety of the wire rope include the degree of wire rope breakage (the number of broken wires and their distribution), the diameter of the wire rope, the degree of corrosion of the wire rope, and other prescribed replacement standards.

[0027] The date and time of the predicted predictive maintenance work PD may be any date and time before the date and time when the predicted malfunction or failure occurs, but it is desirable to set the date and time earlier so that the maintenance work can be performed with ample time in terms of the schedule. For example, the date and time of the predicted predictive maintenance work PD is calculated by adding a fixed value (e.g., one week) to the date and time when the predicted malfunction or failure occurs. In addition, the date and time of the predicted predictive maintenance work PD is calculated by multiplying the length of the period until the date and time when the predicted malfunction or failure occurs by a positive value smaller than "1" (e.g., "0.7"). In this way, by setting the date and time of the predicted predictive maintenance work PD to a date and time before the date and time when the predicted malfunction or failure occurs, it is possible to perform the predictive maintenance work PD before the malfunction or failure actually occurs, even if the date and time of the predictive maintenance work PD is earlier or later.

[0028] In the step (S130) of updating the maintenance schedule data 20, the computing device 12 executes data processing for scheduling the date and time of the predicted predictive maintenance work PD in the maintenance schedule data 20 and updating the maintenance schedule data 20. With this step, the maintenance schedule data 20 is scheduled with the date and time of the preventive maintenance work PV based on a predetermined period and the date and time of the predictive maintenance work PD based on the detection data (monitoring) of the multiple sensing devices 11.

[0029] 6, the number of cargo handling operations for each date and time is predicted, and the cargo handling schedule data 21 is updated to the predicted number of cargo handling operations. Each step may be repeated at a predetermined interval, similar to each step in FIG. 5, or may be performed every time the scheduled departure and arrival dates and times of multiple transportation devices 2 are input to the calculation device 12.

[0030] In the step (S210) of acquiring the scheduled arrival and departure dates and times of the transportation equipment 2, the scheduled arrival and departure dates and times of the transportation equipment 2 transmitted from the transportation equipment 2 or the manager of the transportation equipment 2 are input to the calculation device 12. The scheduled arrival and departure dates and times of the transportation equipment 2 are stored in the auxiliary memory unit 15 of the calculation device 12. The scheduled arrival and departure dates and times of the transportation equipment 2 may be predicted using a prediction model created by supervised machine learning using the name of the baggage 3, the name of the shipper, and past dates of entry and exit from the warehouse as teaching data.

[0031] In the step of predicting the number of cargo handling operations (S220), the computing device 12 executes data processing to predict the number of cargo handling operations for each date and time of the set period based on the scheduled arrival and departure dates and times of the transportation equipment 2. Various known prediction methods can be used to predict the number of cargo handling operations. Examples of prediction methods include a method of calculating the number of cargo handling operations entering and leaving the warehouse facility 1 from the number of arrival and departure vehicles of the transportation equipment 2 for each date and time and their types (e.g., external chassis and ships) and calculating the number of cargo handling operations based on the calculated number of cargo handling operations, and a method of using the scheduled arrival and departure dates and times of the transportation equipment 2 and a prediction model created by supervised machine learning.

[0032] It is desirable that the predicted number of loading and unloading operations includes cargo handling work in the storage area 4, in addition to cargo handling work between the storage area 4 and multiple transport devices 2. Cargo handling work is the work of organizing the luggage 3 stored in the storage area 4, and is necessary for smooth loading and unloading work. Therefore, in this step, in addition to the scheduled departure and arrival dates and times of the transport devices 2, the storage positions of the luggage 3 in the storage area 4 should be used to predict the number of loading and unloading operations. By including cargo handling work in the predicted number of loading and unloading operations, the number of loading and unloading operations can be predicted with higher accuracy.

[0033] In the step (S230) of updating the loading and unloading schedule data 21, the calculation device 12 executes data processing to update the number of loading and unloading operations for each date and time in the loading and unloading schedule data 21. In the loading and unloading schedule data 21, the number of loading and unloading operations for each date and time may vary significantly depending on the scheduled departure and arrival dates and times of the transportation equipment 2. Therefore, it is desirable to update the loading and unloading schedule data 21 every time the scheduled departure and arrival dates and times of the transportation equipment 2 are obtained. In particular, when the warehouse facility 1 is configured as a container terminal, it is desirable to update the loading and unloading schedule data 21 every time the scheduled departure and arrival dates and times of a ship, which has a significantly variable number of loading and unloading operations, are obtained.

[0034] 7, the degree of impact of the performance of the maintenance work scheduled in the maintenance schedule data 20 on the decrease in the loading and unloading efficiency in the warehouse facility 1 is predicted, and the maintenance schedule data 20 is corrected based on the predicted degree of impact. Each step may be performed when at least one of the maintenance schedule data 20 and the loading and unloading schedule data 21 is updated in step (S310), and may be repeatedly performed at predetermined intervals similar to the steps in FIGS. 5 and 6.

[0035] In the step (S310) of determining whether the data has been updated, the calculation device 12 executes data processing to monitor updates to the maintenance schedule data 20 and the loading and unloading schedule data 21 and determine whether at least one of the data has been updated. When each step illustrated in Fig. 7 is performed at a predetermined cycle, this step can be omitted.

[0036] In the data reading step (S320), the arithmetic device 12 executes data processing to read both the maintenance schedule data 20 and the loading and unloading schedule data 21. The data reading is to read the maintenance schedule data 20 and the loading and unloading schedule data 21 stored in the auxiliary storage unit 15 into the main storage unit 14.

[0037] In the step (S330) of predicting the degree of influence on the decline in the loading / unloading efficiency, the calculation device 12 executes data processing to predict the degree of influence on the decline in the loading / unloading efficiency of the warehouse facility 1 due to the implementation of the maintenance work scheduled in the maintenance schedule data 20 for each date and time of the set period based on the maintenance schedule data 20 and the loading / unloading schedule data 21. Various methods can be used to predict the degree of influence. For example, a method may be used in which the ratio between the number of loading / unloading operations scheduled at a specific date and time in the loading / unloading schedule data 21 and the maximum number of loading / unloading operations in the remaining loading / unloading equipment 5 excluding the loading / unloading equipment 5 that is the target of the maintenance work scheduled at the specific date and time in the maintenance schedule data 20 is compared with a preset threshold value. Also, a method may be used in which the number of loading / unloading operations per unit of the remaining multiple loading / unloading equipment 5 at a specific date and time is calculated, and the calculated number of loading / unloading operations per unit is compared with a preset threshold value. Also, a method of predicting the number of cargo handling operations scheduled at a specific date and time in the cargo handling schedule data 21 may be used by computer simulation assuming that the number of cargo handling operations scheduled at a specific date and time in the maintenance schedule data 20 is to be performed by the remaining cargo handling equipment 5, excluding the cargo handling equipment 5 that is the target of maintenance work scheduled for that specific date and time among the multiple cargo handling equipment 5. The computer simulation may use a model that models the cargo handling operations in the warehouse facility 1, or may use a prediction model created by supervised machine learning.

[0038] It is desirable to use an index indicating cargo handling efficiency as the criteria for setting the threshold value of the method for predicting the degree of impact and the objective function in the computer simulation. As an index indicating cargo handling efficiency, the number of cargo handling operations per unit of multiple cargo handling equipment 5, the residence time of multiple transport equipment 2 at the warehouse facility 1, the number of cargoes 3 entering and leaving the warehouse per hour, the cargo handling speed of the cargoes 3, etc. can be used. For example, when the warehouse facility 1 is a container terminal, the Container Port Productivity Index (CPPI) can be used.

[0039] The degree of impact indicates the degree of impact on the decline in the loading / unloading efficiency of the warehouse facility 1 when maintenance work scheduled in the maintenance schedule data 20 is carried out. The degree of impact is positively correlated with the ratio between the number of loading / unloading operations at a given date and time and the number of pieces of loading / unloading equipment 5 available for loading / unloading operations at that given date and time. The degree of impact increases as the number of loading / unloading operations at a given date and time increases, and decreases as the number of loading / unloading operations at a given date and time decreases. In addition, the degree of impact increases as the number of pieces of loading / unloading equipment 5 available for loading / unloading operations at a given date and time decreases, and decreases as the number of pieces of loading / unloading equipment 5 available for loading / unloading operations at a given date and time increases.

[0040] In the step (S340) of determining whether the degree of impact is high, the calculation device 12 executes data processing to determine whether the degree of impact for each date and time in the set period is higher than a preset standard. In this step, if it is determined that the degree of impact for any one of the dates and times in the set period is higher than the standard, the process proceeds to step (S350), and if it is determined that the degree of impact for all of the dates and times in the set period is equal to or lower than the standard, the process returns to the start. Specifically, when the degree of impact is higher than the standard, the calculation device 12 determines that the cargo handling efficiency of the warehouse facility 1 will fall below the target cargo handling efficiency if the scheduled maintenance work is performed, and when the degree of impact is equal to or lower than the standard, the calculation device 12 determines that the cargo handling efficiency of the warehouse facility 1 will be maintained at or above the target cargo handling efficiency even if the scheduled maintenance work is performed.

[0041] If the loading / unloading efficiency of the warehouse facility 1 falls below the target loading / unloading efficiency of the warehouse facility 1, the time that the multiple transport machines 2 stay at the warehouse facility 1 will be longer, or the number of incoming and outgoing cargo 3 per hour will decrease. In other words, it is desirable to maintain the loading / unloading efficiency of the warehouse facility 1 without falling below the target loading / unloading efficiency. If the impact of the scheduled maintenance work is low even if the loading / unloading efficiency falls, the amount of the fall is small, and the warehouse facility 1 is unlikely to fall below the target loading / unloading efficiency. On the other hand, if the impact is high, the amount of the fall in the loading / unloading efficiency is large, and the warehouse facility 1 is likely to fall significantly below the target loading / unloading efficiency. In this way, the impact of the scheduled maintenance work being performed is an index for determining whether the target loading / unloading efficiency of the warehouse facility 1 can be maintained. Therefore, by predicting the impact, it is possible to take measures to prevent the loading / unloading efficiency of the warehouse facility 1 from falling below the target loading / unloading efficiency.

[0042] The standard of the high or low impact degree may be set arbitrarily, but it is preferable to use the loading / unloading efficiency targeted by the warehouse facility 1 as the standard in order to avoid the loading / unloading efficiency falling below the target. The loading / unloading efficiency targeted by the warehouse facility 1 may be expressed as a target value of a preset indicator of loading / unloading efficiency (the number of loading / unloading operations per unit of the multiple loading / unloading devices 5, the stay time of the multiple transport devices 2 at the warehouse facility 1, the number of incoming and outgoing cargoes 3 per hour, and the loading / unloading speed of the cargoes 3), or may be expressed as an average value, a median value, or the like of an indicator of the loading / unloading efficiency of the warehouse facility 1. In addition, the loading / unloading efficiency targeted by the warehouse facility 1 may be expressed as the number of loading / unloading devices 5 capable of loading / unloading operations according to the number of scheduled loading / unloading operations. In this way, by determining the high or low impact degree and setting a condition that prevents the loading / unloading efficiency caused by a high impact degree from falling below the standard, it is possible to more reliably maintain the loading / unloading efficiency of the warehouse facility 1 so as not to fall below the target loading / unloading efficiency.

[0043] In the step (S350) of correcting the maintenance schedule data 20, the calculation device 12 executes data processing to correct the maintenance schedule data 20 based on the predicted impact. Specifically, the calculation device 12 extracts a date and time with a high impact from the dates and times of the set period, and assumes that the cargo handling efficiency of the warehouse facility 1 will fall below the target cargo handling efficiency if the maintenance work scheduled for the extracted date and time is performed, and executes data processing to correct the maintenance schedule data 20 by changing the extracted date and time. The finally corrected maintenance schedule data 20 has an impact degree lower than the standard for all dates and times of the set period even if the scheduled maintenance work is performed, and the cargo handling efficiency of the warehouse facility 1 can maintain the target cargo handling efficiency. Note that the finally corrected maintenance schedule data 20 does not need to have an impact degree lower than the standard for all dates and times of the set period, and it is sufficient that the impact degree of the dates and times between the first date and time of the maintenance schedule data 20 and a predetermined period (e.g., one week) is lower than the standard. The period during which the degree of influence in the finally corrected maintenance schedule data 20 is lower than the standard can be set arbitrarily, but is preferably three days to one week or more.

[0044] As a method for correcting the maintenance schedule data 20, various known schedule adjustment methods can be used as long as the influence degree for a predetermined period (including all dates and times in the set period) is lower than the standard. As an example of the schedule adjustment method, a method is exemplified in which the maintenance work date and time scheduled for the date and time with the highest influence degree among the dates and times with influence degrees higher than the standard is changed to correct the maintenance schedule data 20, and the influence degree of the corrected maintenance schedule data 20 is predicted again and repeated. In this repeating method, it is preferable that the unit of the date and time change is a short period, which may be the work time of the maintenance work, or may be a period of one to three days. In addition, in this repeating method, it is possible to arbitrarily set whether the changed date and time is advanced or delayed from the original date and time, and the date and time may be advanced or delayed to a date and time with a lower influence degree than the influence degree of the original date and time. As an example of the schedule adjustment method for correcting the maintenance schedule data 20, a method is exemplified in which a schedule adjustment model created by supervised machine learning with an objective function as the influence degree is used. In addition, when a schedule adjustment model created by supervised machine learning is used as a method for correcting the maintenance schedule data 20, it is not necessary to repeatedly predict the impact level or correct the maintenance schedule data 20, so the process may be configured to return to the start when step (S350) is completed.

[0045] In this step, it is desirable to set the priority of the date and time of the preventive maintenance work PV lower than the priority of the date and time of the predictive maintenance work PD. With this setting, when the date and time are changed, the date and time of the preventive maintenance work PV, which has a lower priority, is corrected first. The preventive maintenance work PV only needs to be performed at a predetermined interval set in advance, and there is no problem even if the date and time are changed from the scheduled date and time. On the other hand, the predictive maintenance work PD needs to be performed before the date and time at which the predicted malfunction or failure occurs. Therefore, by setting the priority of the date and time of the preventive maintenance work PV lower than the priority of the date and time of the predictive maintenance work PD, it is possible to increase the frequency at which the predictive maintenance work PD is performed as scheduled. As a result, the predictive maintenance work PD is performed before the date and time at which the predicted malfunction or failure occurs with a margin, which is advantageous in avoiding the risk of malfunction or failure occurring in the loading equipment 5. Note that the date and time of the predictive maintenance work PV may be changed, but the scheduled date and time of the predictive maintenance work PV is set with a sufficient margin for the date and time at which the predicted malfunction or failure occurs. Therefore, even if the scheduled date and time varies slightly, it is possible to carry out the predictive maintenance work PV before the predicted malfunction or failure occurs.

[0046] Fig. 8 shows an example of maintenance schedule data 20 corrected based on the original maintenance schedule data 20 illustrated in Fig. 3 and the loading and unloading schedule data 21 illustrated in Fig. 4. As illustrated in the original maintenance schedule data, preventive maintenance work PV based on a short-term period is scheduled for dates and times D3, D7, and Dn in the set period, preventive maintenance work PV based on a long-term period is scheduled for dates and times D4 to D6, and predictive maintenance work PD is scheduled for dates and times D6 to D8. As illustrated in the loading and unloading schedule data 21, the number of scheduled loading and unloading works is large for dates and times D4 to D7 in the set period. In the original maintenance schedule data 20, the influence of dates and times D5 to D7 is high.

[0047] By executing each step of the above (S310) to (S350), in the finally corrected maintenance schedule data 20, the date and time of the preventive maintenance work PV scheduled for the dates and times D5 to D7 is changed based on the priority, as exemplified in (1) in the figure. As a result, the influence of the dates and times D5 and D7 is reduced. Also, as exemplified in (2) in the figure, even if the date and time of the preventive maintenance work PV scheduled for the dates and times D5 to D7 is changed, the influence of the date and time D6 remains high, so the date and time of the predictive maintenance work PD scheduled for the dates and times D6 to D8 is changed to the dates and times D7 to D9. As a result, the number of loading / unloading devices 5 that can perform loading / unloading work at the date and time D6 is secured, so the influence of the date and time D6 is reduced. Furthermore, as exemplified in (3) in the figure, the influence of the date and time D3 has increased due to the change in the date and time of the preventive maintenance work PV in (1), so the date and time of the preventive maintenance work PV scheduled for the date and time D3 is changed. As a result of the above date and time changes, the influence of all the dates and times in the set period is reduced in the finally corrected maintenance schedule data 20. In this way, the influence degree is reduced at all dates and times during the set period of the maintenance schedule data 20, so that a significant drop in the cargo handling efficiency of the warehouse facility 1 is avoided even if all maintenance work scheduled in the maintenance schedule data 20 is performed. As a result, the cargo handling efficiency of the warehouse facility 1 is maintained at or above the target cargo handling efficiency.

[0048] As described above, according to this embodiment, the revised maintenance schedule data 20 (Fig. 8) takes into consideration the degree of impact of maintenance work on a decrease in loading and unloading efficiency compared to the original maintenance schedule data 20 (Fig. 3), and thus it is possible to suppress a decrease in loading and unloading efficiency of the warehouse facility 1 when maintenance work is actually performed. In this way, this embodiment is configured to improve the safety of the warehouse facility 1 by maintenance work on the loading and unloading equipment 5, while suppressing a decrease in loading and unloading efficiency due to the maintenance work, thereby achieving both improved loading and unloading efficiency and improved safety.

[0049] Furthermore, according to this embodiment, by carrying out predictive maintenance work PD in addition to preventive maintenance work PV, replacement and repair work is carried out immediately before a malfunction or breakdown occurs in the loading and unloading equipment 5, reducing the frequency of unnecessary replacement and repair, and making it possible to keep maintenance costs low. In addition, the frequency of situations in which loading and unloading work is interrupted due to malfunction or breakdown of the loading and unloading equipment 5 during loading and unloading work is also significantly reduced. This eliminates the need for a great deal of effort to restore stopped loading and unloading equipment 5, making it possible to avoid a significant drop in loading and unloading efficiency. As a result, it becomes possible to ensure the target loading and unloading efficiency.

[0050] Although an embodiment of the present invention has been described above, the warehouse facility maintenance management system 10 and maintenance management method of the present invention are not limited to a specific embodiment, and various modifications and variations are possible within the scope of the gist of the present invention.

[0051] The maintenance management system 10 is not limited to only targeting the multiple loading and unloading equipment 5 for maintenance work. The maintenance management system 10 may also manage maintenance work for the storage area 4 by having the multiple sensing devices 11 target the storage area 4 as well. Examples of maintenance work for the storage area 4 include inspection and maintenance of the road surface on which the loading and unloading equipment 5 travel. Maintenance work for the storage area 4 may have a greater impact on reducing the loading and unloading efficiency of the warehouse facility 1 than maintenance work for the loading and unloading equipment 5. For this reason, it is desirable for the maintenance management system 10 to also manage maintenance work for the storage area 4, and by managing the maintenance work for the storage area 4, it becomes possible to carry out maintenance work that takes into account the loading and unloading efficiency of the warehouse facility 1.

[0052] It is desirable for maintenance work to include both preventive maintenance work PV and predictive maintenance work PD, but if it is possible to ensure safety with predictive maintenance work PD alone, then only predictive maintenance work PD may be used. The accuracy of predicting the date and time of predictive maintenance work PD may increase as the number of predictions increases. By limiting maintenance work to predictive maintenance work PD, in addition to reducing unnecessary part replacement costs, it is also possible to reduce the effort and costs required for regular preventive maintenance work PV.

[0053] The maintenance management system 10 constantly monitors a plurality of cargo handling equipment 5 using a plurality of sensing devices 11. Therefore, even if an unforeseen malfunction or failure occurs in the cargo handling equipment 5 during cargo handling operations and causes the cargo handling equipment 5 to stop, the system can quickly detect the stoppage of the cargo handling equipment 5. This makes it possible to quickly respond by carrying out recovery work, which contributes to minimizing downtime by allowing early recovery.

[0054] In the maintenance management system 10, the computing device 12 may be configured with at least three computing devices. Specifically, the three computing devices may be a maintenance computing device that executes data processing to create and update the maintenance schedule data 20, a loading and unloading computing device that executes data processing to create and update the loading and unloading schedule data 21, and a computing device that corrects the maintenance schedule data 20 based on the maintenance schedule data 20 and the loading and unloading schedule data 21. In this way, the computing device 12 is configured with a plurality of computing devices, which is advantageous for distributing the computation load. Note that the maintenance management system 10 may execute only data processing to correct the maintenance schedule data 20 using the maintenance schedule data 20 and the loading and unloading schedule data 21 created and updated by another device, without executing data processing to create and update the maintenance schedule data 20 and the loading and unloading schedule data 21.

[0055] In the maintenance management system 10, when the sensing device 11, the communication device of the transport device 2, and the management device are connected to the calculation device 12 via a network, various known servers such as a physical server or a virtual server (including a cloud server) can be used as the calculation device 12. The calculation device 12 and other devices may be connected to each other via the same network (LAN), may be connected to each other via a wide area network (WAN) in which multiple networks are connected to each other via an Internet router, or may be connected to each other via the Internet. The maintenance management system 10 may be a client-server system or a Web system. In the present disclosure, a client-server system refers to a system that uses a dedicated application instead of a Web browser, and a Web system refers to a system that uses a Web browser.

[0056] In the above-described embodiment, the maintenance schedule data 20 and the loading and unloading schedule data 21 are each shown by day, but each schedule may be shown by hour. By showing each schedule of the maintenance schedule data 20 and the loading and unloading schedule data 21 by hour, it becomes possible to manage the schedule of the warehouse facility 1 in more detail. [Explanation of symbols]

[0057] 1. Warehouse facilities 2 Transport equipment 3. Luggage 4. Storage Area 5. Cargo handling equipment 10 Maintenance Management System 11 Sensing equipment 12 Arithmetic unit 20 Data to be preserved 21 Loading and unloading schedule data PV preventive maintenance work PD Predictive maintenance work

Claims

1. A warehouse facility maintenance management system includes a storage area for temporarily storing a large number of items, and a plurality of items of loading and unloading equipment for loading and unloading the items between the storage area and a plurality of transport equipment, the system including a calculation device for correcting maintenance schedule data that schedules dates and times of maintenance work for the items of loading and unloading equipment, The computing device has an auxiliary storage unit and a central computing unit in which the maintenance schedule data, which at least schedules the dates and times of predictive maintenance work based on a plurality of detection data detected by a plurality of sensing devices targeting the plurality of cargo-handling equipment as the dates and times of maintenance work on the cargo-handling equipment, and cargo-handling schedule data, which indicates the number of cargo-handling operations at the warehouse facility for each date and time based on the scheduled dates and times of departure and arrival of the plurality of transport equipment, are stored, This warehouse facility maintenance management system is characterized in that, when at least one of the maintenance schedule data and the loading and unloading schedule data stored in the auxiliary memory unit is updated, the central processing unit performs data processing to modify the maintenance schedule data by changing the date and time of the maintenance work in the maintenance schedule data that is scheduled for a date and time when the loading and unloading efficiency of the warehouse facility is predicted to fall below a target loading and unloading efficiency, assuming that the number of loading and unloading operations for a specified date and time scheduled in the loading and unloading schedule data will be performed using the remaining loading and unloading equipment from among the multiple loading and unloading equipment, excluding the loading and unloading equipment that is the target of the maintenance work scheduled for the specified date and time in the maintenance schedule data.

2. A maintenance management system for a warehouse facility as described in Claim 1, wherein the central processing unit executes the corrective data processing so that the loading / unloading efficiency of the warehouse facility at all dates and times during a predetermined set period is equal to or greater than a target loading / unloading efficiency.

3. The maintenance schedule data includes a date and time of preventive maintenance work based on a preset period and a date and time of the predictive maintenance work, A maintenance management system for a warehouse facility as described in claim 1, wherein in the data correction process, the priority of the date and time of the preventive maintenance work is lower than the priority of the date and time of the predictive maintenance work, and the date and time of the preventive maintenance work is changed first.

4. The maintenance management system for a warehouse facility as described in claim 1, wherein the central processing unit performs data processing to predict, for each date and time, the impact of performing the maintenance work scheduled in the maintenance schedule data on a decrease in loading / unloading efficiency at the warehouse facility, as an indicator for extracting dates and times when the loading / unloading efficiency of the warehouse facility is predicted to fall below a target loading / unloading efficiency, based on the maintenance schedule data and the loading / unloading schedule data, regards a date and time when the impact is higher than a predetermined standard as a date and time when the loading / unloading efficiency of the warehouse facility is predicted to fall below the target loading / unloading efficiency, and regards a date and time when the impact is below the standard as a date and time when the loading / unloading efficiency of the warehouse facility is predicted to be maintained at or above the target loading / unloading efficiency, extracts a date and time when the impact is higher than the standard from dates and times in a predetermined setting period, changes the date and time of the maintenance work scheduled for the extracted date and time, and performs data processing to correct the maintenance schedule data.

5. A method for managing maintenance of a warehouse facility, in which a large number of items are temporarily stored in a storage area and the items are loaded and unloaded between the storage area and a transport device by a plurality of loading and unloading devices, comprising the steps of: modifying, by a computing device, maintenance schedule data that schedules dates and times of maintenance work on the loading and unloading devices; When at least one of the maintenance schedule data, which is stored in the auxiliary memory unit of the computing device and which at least schedules the dates and times of predictive maintenance work based on a plurality of detection data detected by a plurality of sensing devices targeting the plurality of loading and unloading equipment as the dates and times of maintenance work on the loading and unloading equipment, and the loading and unloading schedule data, which indicates the number of loading and unloading operations at the warehouse facility for each date and time based on the scheduled dates and times of departure and arrival of the plurality of transportation equipment, is updated, The central processing unit of the computing device A method for managing maintenance of a warehouse facility, characterized by modifying the maintenance schedule data by changing the date and time of the maintenance work scheduled for a date and time when the loading efficiency of the warehouse facility is predicted to fall below a target loading efficiency, assuming that the number of loading and unloading operations for a specified date and time scheduled in the loading and unloading schedule data will be performed using the remaining loading and unloading equipment from among the plurality of loading and unloading equipment, excluding the loading and unloading equipment that is the target of the maintenance work scheduled for the specified date and time in the maintenance schedule data.

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