KR20240013379A - A Simulation Method of Logistics Infrastructure Response through Demand Forecasting - Google Patents

Abstract

A user interface unit that receives data from the outside or provides information generated by the system, and a data statistical analysis unit that converts the information into a preset format to analyze data output from the postal logistics information system linked to the system. Based on the data output from the user interface or the data output from the data statistics analysis department, the simulation department performs simulations on operation and transportation, and the response plan is established and output according to the data output from the user interface department. It includes an information storage unit that stores standard information for the unit and simulation, simulation result information, and response plan information.
Through this simulation method for responding to logistics infrastructure through demand forecasting, data operating in the logistics infrastructure is refined and future volume is generated through demand forecasting. In addition, since the infrastructure load can be analyzed by future year volume, response plans can be selected through load analysis, and various mid- to long-term infrastructure construction plans can be established and changed in a short time, and the established response plans can be verified. You can also run

Description

A Simulation Method of Logistics Infrastructure Response through Demand Forecasting}

The present invention relates to a simulation method for responding to logistics infrastructure through demand forecasting.

In general, the direction of building logistics infrastructure is to collect operational performance data according to the purpose, define the analysis methodology and plan for infrastructure construction, and then establish alternatives using tools that can manually create the optimal plan for support. It is carried out in a way. In order to improve this performance method and short-term processes within the base, the verified method is applied after modeling and simulation.

In this case, a lot of time is spent on collecting conditions for new infrastructure, collecting data for each purpose such as adjusting jurisdictions to adjust the load between delivery stations and logistics centers, and collecting data for process improvement. In addition, since mid- to long-term infrastructure construction plans are established by establishing response plans with reference to various methodologies for each requirement, these methods must repeatedly perform the same work whenever necessary.

In addition, data must be generated and utilized to establish response plans using demand forecasting methods, and it is not easy to determine what problems exist within and between actual infrastructure bases, and it is impossible to verify short-term response measures in advance, making actual operation difficult. It is impossible to verify the validity of problems that may arise when applied to the environment.

Accordingly, a simulator that supports the establishment of a mid- to long-term logistics infrastructure construction plan and allows the use of an infrastructure load analysis simulation function that is depicted almost identically to the actual infrastructure environment is required. In addition, it must be possible to obtain and use simulation input data from the existing logistics operation system for detailed analysis of future volume generation, operation standard data generation, and infrastructure load analysis required for establishing mid- to long-term logistics infrastructure construction plans.

In addition, it is possible to simulate processes for each type of mail within a logistics base, transportation methods between logistics bases, and work processes at delivery stations, and can identify the work load, transport load, communication quality, operating costs, etc. within and between logistics bases. You need a mulator.

Therefore, the present invention provides a simulation system and method that can generate response plans necessary for establishing an infrastructure construction plan and verify the generated response plans in advance.

The logistics infrastructure response plan simulation system, which is a feature of the present invention to achieve the technical problem of the present invention, is:

A user interface unit that receives data from the outside or provides information generated by the system; a data statistical analysis unit that converts information into a preset format to analyze data output from a postal logistics information system linked to the system; a simulation unit that performs simulation of operation and transportation based on data output from the user interface or data output from the data statistical analysis unit; a response plan establishment unit that establishes and outputs a response plan according to the data output from the user interface unit; and an information storage unit that stores reference information for simulation, simulation result information, and response plan information.

A method of simulating a logistics infrastructure response plan, which is another feature of the present invention to achieve the technical problem of the present invention, is:

Upon receiving demand forecast data for either a base year or an arbitrary year, analyzing load by year based on the demand forecast data to generate load analysis information by year; Establishing a response plan for each year based on the generated load analysis information for each year; And it includes the step of establishing a response plan by analyzing the detailed load for each response plan based on the established response plan for each year.

According to an embodiment of the present invention, the data operated in the logistics infrastructure is refined and future volume is generated through demand forecasting, and the infrastructure load by future year volume can be analyzed, so a response plan through load analysis can be selected. .

In addition, it is possible to establish and change various mid- to long-term infrastructure construction plans in a short period of time, and verify the established response plans.

Below, with reference to the attached drawings, embodiments of the present invention will be described in detail so that those skilled in the art can easily implement the present invention. However, the present invention may be implemented in many different forms and is not limited to the embodiments described herein. In order to clearly explain the present invention in the drawings, parts that are not relevant to the description are omitted, and similar parts are given similar reference numerals throughout the specification.

Throughout the specification, when a part “includes” a certain element, this means that it may further include other elements rather than excluding other elements, unless specifically stated to the contrary.

Hereinafter, a logistics infrastructure response plan simulation system and method according to an embodiment of the present invention will be described with reference to the drawings.

1 is a system structure diagram according to an embodiment of the present invention.

As shown in Figure 1, the system according to an embodiment of the present invention includes a user interface unit 100, a data statistical analysis unit 200, a simulation unit 300, a response plan establishment unit 400, and an information storage unit ( 500).

First, the user interface unit 100 performs the function of receiving various data from the user or providing information generated in the system to the user, and includes a data input unit 110, a simulation performance and result inquiry unit 120, and a response plan establishment and It includes a result inquiry unit 130.

The data input unit 110 is connected to an information input means used by users such as logistics planning and operation personnel, and collects data input from users. In addition, the data input from the user is transmitted to the data statistical analysis unit 200. The simulation performance and result inquiry unit 120 transmits the data input through the data input unit 110 in conjunction with the simulation unit 300. It receives and transmits it to the simulation unit 300, or receives the results of a simulation using data from the simulation unit 300 and provides the results to the user.

The response plan establishment and result inquiry unit 130 is linked with the response plan establishment unit 400, and transmits the data collected by the data input unit 110 or the data performed and output from the simulation unit 300, based on the data. Establish a response plan. In addition, the results of the response plan related to the data generated by the response plan establishment unit 400 are received and provided to the user. Next, the data statistical analysis unit 200 performs conversion into a preset format to analyze information transmitted from the Postal Logistics Information System (PostNet). To this end, the data statistical analysis unit 200 includes a data migration unit 210, an input data conversion unit 220, and an output data conversion unit 230.

The data migration unit 210 converts data received from the postal logistics information system or the user interface unit 100 into a data format required for establishing a mid- to long-term response plan and performing an infrastructure load analysis simulation. Through this type of data conversion, the base year input data needed for simulation can be created in advance.

The input data conversion unit 220 refers to the preset demand forecast generation result required to start the simulation and generates the input quantity for the future year based on the base year quantity in which the demand forecast information is input by the user and performs the simulation. It is generated as simulation result data, which is input quantity information. For this purpose, the input data conversion unit 220 is largely divided into a recorded mail input data converter 221, a non-recorded mail input data converter 222, and an operation and transportation standard data converter.

It consists of (223) and a future mail volume data generator (224). The detailed structure of the input data conversion unit 220 will be described later.

The output data conversion unit 230 converts the simulation result data into output data such as load and communication quality in order to provide the simulation result generated from the input data conversion unit 220 to the user. In other words, for the logistics infrastructure, the volume of simulation input data is forecasted based on actual operation performance by year, by logistics base, by day, by time zone, by mail type, by work condition, by information from the receiving country to the delivery country, and by predicting the base year volume and demand. It is generated as a simulation input quantity for preliminary evaluation of future years by the method.

Next, the simulation unit 300 performs simulations of the user interface unit 100 and performs operations and operations according to the data based on data transmitted from the result inquiry unit 120 or data input from the data statistical analysis unit 200. Perform simulations on transportation. The simulation unit 300 includes an operation process simulation unit 310 and a transportation process simulation unit.

Includes (320).

When statistical simulation data output from the data statistical analysis unit 200 or data is input from the user interface unit 100, the operation process simulation unit 310 is operated at a base (e.g., a transportation exchange center, concentration station, logistics center, or delivery center). Bureau, etc.) performs simulation of the internal operating process logic. Through simulation, the operating process for each base currently in operation is modeled, and the base load is calculated for the input volume.

The transportation process simulation unit 320 operates a transportation network (e.g., an exchange network between an exchange center and a central station, a central station between a central station and a logistics center) for transportation between logistics bases according to the results performed by the operation process simulation unit 310 within the logistics base. Simulation of the transportation process in the network (network, post office network between centralized station and centralized station, etc.) is performed. Then, the current transportation process is modeled for each transportation network to calculate the number of vehicles required and transportation costs.

Next, when the response plan establishment unit 400 receives data from the user interface unit 100, it establishes a response plan according to the received data and provides it to the user. The response plan establishment department 400 according to an embodiment of the present invention is the infrastructure new department.

(410), jurisdictional design department (420), network structure design department (430), and processing capacity coordination department (440).

The infrastructure new department 410 establishes new infrastructure according to simulation results for future volume. As new infrastructure is established, the processing capacity and jurisdiction of the new infrastructure can be set and the load for each infrastructure can be recalculated.

The jurisdiction design department 420 resets the commercial and parcel jurisdiction areas of the existing infrastructure and recalculates the load for each infrastructure. The network structure design unit 430 designs the network structure by establishing a new exchange center, setting exchange time and exchange area, and generates standard information for simulation.

The processing capacity adjustment unit 440 adjusts the processing capacity within the logistics base by redesigning the jurisdictional area by adjusting the functions of the existing infrastructure, such as specialization for commerce and parcels, and recalculating the load on the infrastructure by adjusting the processing capacity of the overloaded infrastructure. do. Next, the information storage unit 500 includes an operation/transportation standard data storage unit 510 that stores reference information for simulation, and a simulation result data storage unit 520 that receives and stores the simulation results from the simulation unit 300. ) and a response plan data storage unit 530 that stores the response plan established by the response plan establishment unit 400.

The components of the system discussed above will be described in more detail below with reference to the drawings. First, looking at the components of the data statistical analysis unit 200, the data statistical analysis unit 200 includes a data migration unit 210 that generates in advance the base year input data required for simulation and a data migration unit 210 that generates the base year input data required for simulation in advance. When the demand forecast information is input, the input quantity for future years is created based on the base year quantity. At this time, when performing an operation in the input data conversion unit 220 to generate the corresponding data as simulation input quantity data, the input quantity data is classified as shown in FIG. 2 and generated based on postal type 4.

Figure 2 is an exemplary diagram showing input data generation standards for each mail type according to an embodiment of the present invention.

As shown in FIG. 2, the types of mail according to the embodiment of the present invention are largely divided into four types, but are not necessarily limited to this.

The receipt register in the postal logistics information system is classified as recorded or unrecorded mail. Therefore, recorded mail parcels, special regular mail volume, and unrecorded mail volume are divided into small and large size to generate input data for each base for simulation. The standard for postal work conditions is to divide small mail into manual, machine, and line sorting, as in postal type 4, and the volume of line sorting mail is divided into line sorting mail 1 and line sorting mail 2 to match the shipping sorting and arrival sorting work conditions. Create it.

In the case of large-sized mail, line-sorted mail reflects the same conditions as the small mail volume calculation standards, and the remaining mail is calculated as general volume. In addition, volume data for demand forecasting is created based on postal type 2, and volume by location by year is generated and used when generating demand forecast values. And if there is a machine quantity, it can also be defined as small.

During this process, the present invention extracts the postal volume information marked as recorded mail in the reception ledger of the postal logistics information system and performs a quantity calculation process to provide it as input data for simulation. The recorded mail input data converter 221, which is a component of the input data converter 220 according to the embodiment, will be described with reference to FIG. 3.

Figure 3 is an exemplary diagram of the structure of a recorded mail input data converter of the input data converter according to an embodiment of the present invention. As shown in FIG. 3, the recorded mail input data converter 221 according to an embodiment of the present invention includes a first volume generation module 221-1, a second volume generation module 221-2, and ratio value generation. It includes a module 221-3, an input standard quantity data generation module 221-4, and an input quantity generation module 221-5.

The first volume creation module 221-1 generates volume data by reception base/type/day among the data received from the daily mail reception ledger of the postal logistics information system and converted by the data migration unit 210. Extract and save. In addition, in the recorded mail reception ledger of the postal logistics information system, the received volume is created and stored by general station/day using the national code code value for delivery station registration that matches the recipient postal code corresponding to the recipient address serial number. . In addition, daily volume values and volume meter values for each time zone are generated, and volume ratio values for each time zone are created and stored.

The second quantity creation module 221-2 generates and stores quantity values for the first general station (or dispatch general station) and the second general station (or arrival general station) based on the execution results of the first quantity creation module 221-1. .

When the input standard quantity data generation module 221-4 receives a message containing future year simulation period information, future year growth rate value information, and simulation performance request information from the user interface unit 100, it performs simulation by corresponding year, month, and day. Generate input standard quantity data for each base for the period for execution.

In the case of a base year simulation, the input quantity generation module 221-5 performs an operation to generate the base year input quantity as a simulation input quantity, and if a future year quantity growth rate value exists, the second quantity generation module 221-2 or After the input standard quantity data generation module 221-4 is completed, the simulation input quantity is generated.

Next, during the process mentioned above, the volume calculation process is performed to extract the postal volume information marked as unrecorded mail in the reception ledger of the postal logistics information system from the postal logistics information system and provide it as input data for simulation. To this end, the non-recorded postal mail input data converter 222, which is the second component of the input data converter 220 according to an embodiment of the present invention, will be described with reference to FIG. 4.

Figure 4 is an exemplary diagram of the structure of an unrecorded mail input data converter of the input data converter according to an embodiment of the present invention. As shown in FIG. 4, the unrecorded mail input data converter 222 according to the second embodiment includes a quantity data extraction module 222-1, an input standard quantity data generation module 222-2, and manually generated quantity data. It includes a generation module (222-3), a simulation input data generation module (222-4), and a delivery quantity ratio generation module (222-5).

The quantity data extraction module 222-1 extracts and stores quantity data by base, daily postal type, and work conditions. Here, volume data by postal base, daily postal type, and work conditions refers to data generated from the non-recorded mail reception register of the postal logistics information system on the volume of postal mail type by reception base by day, day of the week, special communication period, or work conditions. do. The input standard volume data generation module 222-2 generates and stores a ratio value based on the shipment/arrival/distribution volume values for each base from the operational volume data in the mail receipt ledger of the postal logistics information system. Then, quantity values for the first and second general stations are generated and stored. At this time, if future year quantity input data is not delivered from the user interface unit 100, the manually generated quantity generation data generation module 222-3 may be called.

When the dividend volume ratio generation module 222-5 obtains a request message including future year simulation period information, future year growth rate value information, and simulation performance request information from the user interface unit 100, it performs simulation on the corresponding day. To do this, input standard volume data for each base for the period is generated.

The manual labor volume data generation module (222-3) generates and stores a classification equipment operation performance data acquisition function among the data of the postal logistics information system. If there is a ratio value of the manual labor occurrence volume after machine work that is stored, this value includes machine work manual labor by base. Multiply the quantity to create additional manual quantity and save it.

For this purpose, it is determined whether there is a ratio value of the volume generated by manual labor after the classification machine work created from the operation data, and if the ratio value of the volume generated by manual labor does not exist, the volume generated by additional manual labor is requested to be calculated from the classification facility operation performance data. Afterwards, when a ratio value of the volume generated by manual work after machine work is generated from the classification facility operation performance data, this value is multiplied by the volume of machine work by base to generate the additional volume of manual work.

The simulation input data generation module 222-4 is notified of simulation start information from the input standard quantity data generation module 222-2, and if it is a base year simulation, the simulation is based on the uniform distribution value of the base year input quantity. Generated by input quantity. At this time, if the future year volume growth rate value exists, after the execution of the delivery volume rate generation module 222-5 is completed, the simulation input volume is generated based on the uniform distribution value.

Next, among the system structures mentioned in FIG. 1, the operation and transportation standard data converter ( 223) The structure will be described with reference to FIG. 5.

Figure 5 is an exemplary diagram of the operation of the input data conversion unit and the structure of the transportation standard data converter according to an embodiment of the present invention. As shown in FIG. 5, the operation and transportation standard data converter 223 includes a work standard information generation module 223-1 for acquiring operation data, a processing volume calculation module 233-2 for each time period, and a ratio value generation module. It includes a module 233-3, a manual generated quantity calculation module 233-4, a contract quantity data generation module 233-5 that acquires transport data, and a transport distance fee data generation module 233-6.

The work standard information generation module (233-1) is input from the operation management data of the postal logistics information system and converted by the data migration unit 210, and classifies the type and processing capacity of the facility by base and the work condition standards. After extracting the information, it is transferred to the operation/transportation standard data storage unit 510 and stored. As shown in Figure 5, the operation/transportation standard data storage unit 510 includes an operation standard data storage unit 510-1, an operation standard quantity data storage unit 510-2, and a transportation standard data storage unit 510-3. ), and the information extracted from the operation standard information generation module 233-1 is stored in the operation standard data storage unit 510-1.

The processing volume calculation module 233-2 by time slot is input from the operation management data of the postal logistics information system and converted by the data migration unit 210, when the user inputs the processing capacity of the facilities classified by base. Processing capacity data is obtained by time period for reference. Then, based on the acquired data, processing volume values for each time period of the classification equipment at each base are generated and delivered to the operation standard data storage unit 510-1 for storage.

The ratio value generation module 233-3 generates the shipment/arrival/distribution volume and ratio value for each base from the information input from the operation management data of the postal logistics information system and converted by the data migration unit 210. The generated shipment/arrival/distribution volume and ratio values for each base are transferred to the operation standard volume data storage unit (510-2) and stored.

The manually generated quantity calculation module (233-4) calculates the quantity supplied for machine processing through the classification equipment for each base from the information input from the operation management data of the postal logistics information system and converted by the data migration unit 210. Generates a ratio value of the volume generated by manual volume as machine processing is not possible. The ratio value of the generated quantity is transmitted to the operation standard quantity data storage unit 510-2 and stored. In addition, the volume generated manually after machine processing is calculated for each base, and the calculated information is also transferred to the operation standard volume data storage unit 510-2 and stored.

The contract quantity data generation module 233-5 is used to perform a simulation process of acquiring transport data to extract and provide transport standard data required to perform the simulation function from the postal logistics information system. From the information input from the operation management data and converted by the data migration unit 210, data on the contract quantity per parcel pallet is obtained. The acquired data on the quantity of contracts per parcel pallet is transferred to the transportation standard data storage unit 510-3 and stored.

The transportation distance fee data generation module 233-6 obtains transportation fee data from information input from the operation management data of the postal logistics information system and converted by the data migration unit 210. The acquired data is then transferred to the transportation standard data storage unit 510-3 and stored.

Next, among the system structures mentioned in FIG. 1, the structure of the demand forecast and future mail volume generator 224, which generates and provides demand forecast volumes to establish response plans through infrastructure load analysis on a future year basis, is shown. This will be explained with reference to 6.

Figure 6 is an example diagram of the demand prediction and future mail volume generation module structure of the input data conversion unit according to an embodiment of the present invention.

As shown in FIG. 6, the demand forecast and future mail volume generator 224 includes a volume data generation module 224-1, a reference data generation module 224-2, and a volume change generation module 224-3. do.

The quantity data generation module 224-1 calculates the daily or monthly quantity for each base based on the daily quantity data from the reception ledger of the postal logistics information system. The standard data generation module 224-2 uses the daily quantity calculation results for each base calculated by the quantity data generation module 224-1 to calculate the annual quantity for each base. Here, by base, it means a general office or post office, but it is not necessarily limited to this.

The volume change generation module 224-3 uses the past year's volume data and the newly created standard change volume value to predict demand and calculate future mail volume within the input data conversion unit 220, using the national or office-specific CAGR (CAGR) Create and store Compound Annual Growth Rate (Compound Annual Growth Rate). Additionally, when demand forecast information is input from the user interface unit 100, a quantity change rate is generated.

At this time, in order to generate the volume change rate, when the simulation unit 300 is running, volume increase/decrease data by postal type and logistics base stored in the information storage unit 500 is provided to the user, and then by postal type and nationwide demand. Once the forecast increase/decrease and volume increase/decrease rate values for each office are obtained, the volume change rate for future years for each base is generated. Afterwards, when the CAGR value for each office is obtained from the user interface unit 100, the predicted volume value for each post office based on the increase/decrease rate value for each post office is corrected to match the national total volume value to match the national demand forecast volume value. Then, the volume change rate for future years is generated for each base.

Once the volume change rate value for the future year is generated, the input volume generation modules 221-4 and 222-2 located in the recorded mail input data converter 221 and the non-recorded mail input data converter 222, respectively, Provides information on the rate of change in volumes generated for future years.

Next, among the system structures mentioned in FIG. 1, the results of the infrastructure load analysis simulation are used to convert detailed load analysis, vehicle requirements analysis, and communication quality analysis results into output data to provide to users, and perform the infrastructure load analysis simulation. When this is completed, refer to FIG. 7 for the structure of the output data conversion unit 230, which stores the load calculation results for each central station in the information storage unit 500 and then provides the results to the user through the user interface unit 100. 7 is a structural diagram of an output data conversion unit according to an embodiment of the present invention.

As shown in Figure 7, the output data conversion unit 230 according to an embodiment of the present invention includes a result confirmation module 231, a data search module 232, a detailed load analysis module 233, and vehicle requirements calculation. Module (234) and satisfaction rate calculation module for each mail type

Includes (235).

The result occurrence confirmation module 231 stores the simulation results and response plan results output from the simulation unit 300 and the response plan establishment unit 400, respectively, into the simulation result data storage unit 520 and the response plan result of the information storage unit 500. It is transmitted to the data storage unit 530 and stored.

The data search module 232 searches the data by operation standard/transportation standard/volume/region stored in the result data storage unit 520 or the response plan data storage unit 530, and displays the user interface unit 100. provided to users through

The detailed load analysis module 233 largely performs the function of calculating the overload ratio for logistics bases and the function of calculating the maximum daily load. That is, the detailed load analysis module 233 calculates the number of overload days and ratio values from the load values for each dispatch and arrival category for each central station among the simulation results. In addition, among the simulation results, the daily average load value and daily maximum load value are calculated from the load values for each shipment category and arrival category for each central station.

The vehicle requirement calculation module 234 calculates the degree of vehicle requirement for each transportation section from simulation performance data in relation to vehicle requirement analysis and transmits the result to the information storage unit 500. In addition, the results calculated through the user interface unit 100 are output and provided to the user screen.

In addition, the simulation results are used to calculate the daily average and daily maximum number of vehicles required for each size of vehicle between bases. Here, to calculate the daily average and daily maximum transportation costs, the daily average and daily maximum transportation costs incurred during the simulation period are also calculated.

The satisfaction rate calculation module 235 for each mail type calculates the satisfaction rate for delivery criteria for each mail type from simulation performance data in relation to communication quality analysis, and transmits the calculated result to the information storage unit 500. In addition, the results calculated through the user interface unit 100 are output and provided to the user screen.

Next, the components of the response plan establishment unit 400 of FIG. 1 will be described in detail with reference to FIGS. 8 to 10.

Figure 8 is a structural diagram of the infrastructure new section of the response plan establishment section according to an embodiment of the present invention. Figure 9 is a structural diagram of the jurisdiction design department of the response plan establishment unit according to an embodiment of the present invention, and Figure 10 is a structural diagram of the processing capacity adjustment function of the response plan establishment unit according to an embodiment of the present invention.

First, as shown in FIG. 8, the infrastructure new unit 410 according to an embodiment of the present invention receives communication time information and distance/time setting data from the user interface and stores them as reference data for simulating a response plan. In addition, processing capacity setting and jurisdiction setting data are input, and the load for each infrastructure is calculated in real time and provided to the user, thereby creating and providing an appropriate infrastructure new plan.

To this end, the new infrastructure unit 410 includes an information collection module 411, an infrastructure-specific volume calculation module 412, a new infrastructure processing capacity calculation module 413, and an infrastructure-specific load calculation module 414.

The information collection module 411 receives communication time and distance/time setting data from the user interface unit 100 and transfers them to the response plan data storage unit 530 of the information storage unit 500 as a standard for simulating a response plan. Be sure to save it as data. The information collected by the information collection module 411 is transmitted to the classification volume calculation module 412 for each infrastructure and the new infrastructure processing capacity calculation module 413 to calculate the classification volume for each infrastructure and the processing capacity of the new infrastructure.

The infrastructure-specific load calculation module 414 receives the information calculated by the infrastructure-specific volume calculation module 412 and the new infrastructure processing capacity calculation module 413, calculates the load for each infrastructure in real time, and sends it to the user interface unit 100. It is delivered and provided to the user.

As shown in FIG. 9, the jurisdiction design department 420 is used to analyze the effect of adjusting the dispatch and arrival jurisdiction general bureaus for commerce and parcels in the concentration station and logistics center, and the setting module 421 and infrastructure Quantity calculation module by category

(422) and load calculation module for each infrastructure (423).

The setting module 421 receives normal shipping jurisdiction, normal arrival jurisdiction, parcel sending jurisdiction, and parcel arrival jurisdiction setting information through the user interface unit 100. Then, based on the standard quantity information stored in the operation standard quantity data storage unit 510-2 and the input information, the classification quantity calculation module 422 for each infrastructure and the load calculation module 423 for each infrastructure are operated according to the changed jurisdiction data. Calculate the load for each infrastructure in real time. And the newly calculated load-related information for each infrastructure is provided to the user through the user interface unit 100.

In other words, the jurisdiction of the conventional concentration station mostly uses both commerce and parcels except for some general bureaus, but in the embodiment of the present invention, for flexibility in establishing response measures, the concentration station's commerce dispatch jurisdiction, commerce arrival jurisdiction, and parcel dispatch jurisdiction are used together. , so that it can be set separately by parcel arrival jurisdiction.

As shown in FIG. 10, the processing capacity adjustment function unit 440 analyzes the effect of changing the functions of the existing centralization station and logistics center, adjusting the classification equipment and manual processing capacity, and resetting the jurisdiction area accordingly, and establishing a new exchange center. It includes a module 441, an exchange time setting module 442, a processing capacity setting module 443, and an exchange area setting module 444.

Each module 441 to 444 of the processing capacity adjustment function unit 440 determines the network structure for establishing a new exchange center, exchange time setting information, the processing capacity of the newly established exchange center, and the exchange area to be included in the newly established exchange center. When set, the set information is transferred to the response plan data storage unit 530 and stored. In other words, by receiving infrastructure function adjustment data, classification equipment processing capacity data, manual processing capacity data, and jurisdictional area setting data from the user interface unit 100, the load change amount for each logistics base is generated in real time and provided to the user, thereby providing appropriate The results of establishing a response plan to adjust infrastructure processing capacity are provided through the user screen.

Next, the operation process simulation unit 310 and the transportation process simulation unit 320 of FIG. 1 will be described with reference to FIG. 11.

Figure 11 is a simulation example of an operation process simulation unit and a transportation process simulation unit according to an embodiment of the present invention.

As shown in FIG. 11, the operation process simulation unit 310 and the transportation process simulation unit 320 are used to prepare the logistics classification work process within the logistics handling base and the arrival and shipment/distribution work between logistics bases. Simulate the process.

When volume generation starts from the data statistical analysis function, the operation process simulation unit 310 performs simulation by processes for each postal type and work condition that occur within the logistics bases that exist from reception to delivery process.

In addition, regular mail is used to generate the volume for each logistics base to be sent based on the shipment and distribution volume values for the destination logistics center and delivery country. To this end, the process of generating the volume for each logistics base based on the simulation result volume must be included.

The transportation process simulation unit 320 starts the simulation based on the results of the operating process and simulates a method of selecting and sending a transportation vehicle for each destination based on the mail container conversion ratio value for each mail type and the quantity of containers that can be loaded on the transportation vehicle. For this purpose, a simulation of the transportation process in the transportation network is performed. The network formed between the exchange center and the central office shown in Figure 11 is called the exchange network, the network formed between the central office and the logistics center is called the centralized national network, and the network formed between the centralized office and the general office is called the post office network.

Once the simulation of the transportation process is completed, the number of vehicles and transportation costs required for the transportation process for each transportation network are calculated, and then re-simulated according to the response plan establishment procedure to generate simulation results that can compare response plans.

Next, a method of simulating infrastructure load analysis through a system having the structure described above will be described with reference to FIG. 12.

Figure 12 is a flowchart of a method for simulating infrastructure load analysis according to an embodiment of the present invention.

As shown in Figure 12, according to an embodiment of the present invention, the operation of the infrastructure load analysis simulator for establishing and verifying a mid- to long-term logistics infrastructure construction plan based on the base year and future volume is largely divided into the steps of receiving information and the daily schedule. It consists of a step of calculating the amount supplied and a step of performing a load analysis by year.

To explain in detail, first, the data input unit 110 of the user interface unit 100 receives demand forecast data for the base year or future year input by the user and starts performing the simulation (S100). At this time, the input demand forecast data for the base year or future year is the result generated by converting the input data for each base, postal type, and standard volume generated through the input data conversion unit 220 of the data statistical analysis unit 200. This is data generated based on volume demand forecast information.

After converting the input data to generate input data, the daily standard quantity to be stored in the response plan data storage unit 530 is calculated (S110). Step S110 is explained as an example of being performed once at the beginning of the system, but it is not necessarily limited to this. Once the daily standard volume is calculated, the response plan establishment and result inquiry unit 130 performs an annual load analysis based on the data (S120). At this time, annual load analysis is performed for each agency or centralized station, and preparation work for simulation is completed through step S120.

Here, when receiving demand forecast data in step S100, the procedure for converting the input data by the input data conversion unit 220 will be described with reference to FIG. 13.

Figure 13 is a flowchart showing the input data conversion procedure of the data converter according to an embodiment of the present invention.

As shown in Figure 13, the input data conversion procedure includes a quantity data generation step (S200), an operation/transportation standard data generation step (S210), a demand forecast standard volume generation step (S220), and a future volume generation step (S230). It is done through.

Looking at each procedure in detail, first, the quantity data generation step (S200) is broadly divided into a recorded postal quantity data generation process, a non-recorded postal quantity data generation process, and sending country-receiving country quantity information generation, as shown in FIGS. 14a to 14c. It can be divided into processes. FIG. 14A is a flowchart of a process for generating recorded postal volume data according to an embodiment of the present invention, and FIG. 14B is a flowchart showing a method of generating sending station-receiving station volume information according to an embodiment of the present invention. And Figure 14c is a flowchart of the process of generating unrecorded postal volume data according to an embodiment of the present invention.

In general, recorded mail generates simulation input data based on the received volume values for each time period. In cases where volume information by reception time cannot be calculated, such as for unrecorded mail, it is generated in an even distribution.

In other words, as shown in FIG. 14a, the process of generating recorded mail volume data is based on the volume information by reception base/type/day generated in the reception register of the postal logistics information system, by the recorded mail input data converter 221. 1 The quantity creation module (221-1) generates quantity information by logistics base/type/day/time zone (S201). At this time, the volume ratio value for each time period is also generated.

When the first quantity creation module (221-1) generates quantity information by logistics base/type/day/time zone, the ratio value generation module (221-3) calculates the quantity by destination country by recipient postal code, and this Based on the calculation results, the input standard quantity data generation module (221-4) generates and stores quantity information (quantity, ratio value) from (quantity received at the reception general station) - to (quantity expected to arrive at the delivery general station).

Here, the detailed method by which the input standard quantity data generation module 221-4 generates the sending station-receiving station quantity information is as shown in FIG. 14b. Figure 14b is a flowchart showing a method of generating sending country-receiving country volume information for recorded mail volume according to an embodiment of the present invention.

As shown in FIG. 14b, the input standard quantity data generation module 221-4 generates reception quantity information for each general station/concentration center based on the reception country symbol (S203-1). The reception quantity information generated at this time by general station/concentration center is the same as the quantity information generated by reception base/type/day in the reception ledger of the postal logistics information system. Then, as a result of searching the recipient's zip code from the recipient's address serial number in the receipt ledger, delivery quantity information based on the standard delivery country is generated (S203-2).

Next, in the quantity data generation step (S200), apart from the recorded postal quantity data generation process mentioned in FIG. 14a, another procedure, the non-recorded postal quantity data generation process, is as shown in FIG. 14c. Figure 14c is a flowchart showing a procedure for generating unrecorded postal volume data according to an embodiment of the present invention.

As shown in FIG. 14C, the quantity data extraction module 222-1 refers to common data (i.e., jurisdictional area information) in which the relationship between logistics bases is defined, and determines whether the collected postal type code is the affiliated general station or affiliated central station. Identify whether it is recognized (S300). At this time, the postal type code is a code based on the receiving country identifier (for example, national flag for registration, etc.) generated in the non-recorded mail reception register of the postal logistics information system for identification.

The volume data extraction module 222-1 determines whether the collected mail type code is stored in the unrecorded mail reception register of the postal logistics information system (S301), and determines whether the non-recorded mail is mail received from the affiliated general office. Or, you can find out whether the mail was received at the affiliated central office. Then, if it is determined that the mail does not belong to either the affiliated general office or the affiliated central office, that is, if the mail type code is not stored in the unrecorded mail reception register of the postal logistics information system, then the volume data by base for the non-recorded mail Create and store (S302).

And when the volume data for each base is completed from the input standard volume data generation module (222-2), it is based on the postal type code and the reduction and additional reduction standard code values that are preset as the operation standard data for simulation. As a result, the dividend volume ratio generation module 222-5 generates and stores volume information for each postal type and work conditions and volume ratio values for each work condition (S303).

From the delivery volume ratio generation module 222-5, it is determined whether the volume information for each base, daily mail type, work condition, and volume ratio value data for each work condition have been generated (S304). If completed, the simulation input data generation module (222-4) generates the shipment and arrival volume for each country (S305) and then creates and stores the delivery volume for each general station (S306).

However, if it is determined that the creation has not been completed in step S304, the ratio value and the concentration station arrival and concentration station reception volume meter values by postal type are generated based on the information on the shipment/arrival/distribution volume by base and dispatch/arrival/distribution volume data in the operation standard volume data. Save (S307).

And the manual work quantity data generation module 222-3 refers to the operation management data (S308), generates and stores the shipment quantity and ratio values for each work condition for each base (S309). Then, the difference value of the quantity sent at the same time is generated and generated as the domestic shipment arrival classification quantity data (S310).

After determining whether the creation of delivery quantity data for each general station has been completed (S311), if it is complete, add the quantity value for arrival in other countries to the generated shipment arrival classification quantity data for the home country, create delivery quantity data for each general station (or delivery country), and store it in the information storage unit. Save (S312). However, if it is not completed as a result of determination in step S311, steps S309 and S310 are repeated until completion.

Next, the step (S210) of generating the operation/transportation standard data of FIG. 13 will be described with reference to FIGS. 15A to 15D.

Figures 15A to 15D are flowcharts showing an operation/transportation standard data generation procedure according to an embodiment of the present invention. In the process of generating operation and transportation standard data, the operation and transportation standard data converter 123 converts transportation plan information, performance data, classification facility standard information, operation management information, and machine data among the data of the postal logistics information system. By referring to data related to manual work volume compared to processed volume, standard data is extracted so that it can be used as simulation operation and transportation standard data (S211-1). The data of the postal logistics information system used for data extraction in the embodiment of the present invention is as mentioned above, but is not necessarily limited thereto.

First, as shown in FIG. 15A, the work standard information generation module 233-1 determines whether standard data has been extracted from transportation plan data and performance data among the data received from the postal logistics information system (S211-2 ). If the standard data has been extracted, the contract quantity data generation module 233-5 acquires transportation data from the standard data. To this end, the total number of pallets and the total number of parcels by shipping concentration country are calculated using the standard data obtained from the contract ledger data. Calculate (S211-5).

Then, the contract quantity data generation module 233-5 calculates a parcel conversion standard per pallet using the calculated total number of pallets and the total number of parcels, and obtains transport fee data from pre-stored distance fee table data. Afterwards, the transportation distance fee data generation module 233-6 acquires the distance fee table data registered in the information storage unit 500 and stores the obtained distance fee table data in the information storage unit 500 (S211-6) ).

On the other hand, if it is determined that the standard data has not been extracted in step S211-2, the manual generation volume calculation module (233-4) extracts the transportation distance and fee data between bases and then registers them (S211-3). After determining whether the total number of pallets and total number of parcels by shipping concentration country have been calculated (S211-4), if it is determined that the total number of pallets and total number of parcels by shipping concentration country have not yet been calculated, perform again from step S211-1. Repeat until the total number of pallets and total number of parcels by shipping concentration country are extracted.

As shown in FIG. 15b, the classification facility standard information among the data received from the postal logistics information system is used (S212-1). The work standard information generation module (233-1) and the time slot calculation module (233-2) extract the type of classification equipment, processing capacity, and classification work condition standard information for each base and register them in the information storage unit (500). (S212-2).

Then, in order to be able to refer to the information about the processing capacity of the classification facility when it is entered by the user in the operation standard data, the processing volume calculation module 233-2 for each time zone that acquires the processing capacity data for each time zone is The name, number, and processing capacity standard data for each base are extracted from the standard data for each base classification facility and stored in the information storage unit 500 (S212-3). Whether registration has been completed in the operation standard data information storage unit 510

After making the judgment (S212-4), the volume information for each base, facility, and time period is extracted and the steps after S212-1 are repeated until storage in the information storage unit 500 is completed.

Next, as shown in FIG. 15c, use operational management data among the data received from the postal logistics information system (S213-1) to generate standard information used to generate sending country-receiving country volume information of unrecorded mail. To this end, the ratio value generation module (233-3) creates a ratio table using the daily volume values for the shipment, arrival, and distribution volume by work condition for each base in the operation management table of the postal logistics information system (S213-2 ).

Then, it is created as a file for each base or stored in the information storage (S213-3). Check whether this procedure has been completed (S213-4) and repeat the procedure after step S213-1 until completed.

As shown in FIG. 15D, in the manual labor volume calculation module 233-4, operation performance data for each base of classification facilities extracted from the operation management data of the postal logistics information system is used to generate small manual labor volume data.

Use (S214-1). After generating small manual quantity data (S214-2), from the operation performance data for each base of the classification facilities, the value of the supply quantity-classification processing completed quantity is generated as the manual labor occurrence rate value of the base, and stored in the information storage (500) ) (S214-3). Then, check whether the creation of the manual work occurrence rate value among the machine processing supply volume for each base has been completed (S214-4), and repeat the procedure after step S214-1 until completed.

Next, the demand prediction standard quantity generation step (S220) and the future quantity generation step (S230) of FIG. 13 will be described with reference to FIG. 16. The demand forecast standard quantity creation step (S220) and the future quantity creation step (S23 0) shown in FIG. 16 represent steps for calculating nationwide quantity.

Figure 16 is a flowchart showing the generation of demand forecast standard quantity data and future quantity data according to an embodiment of the present invention.

As shown in FIG. 16, the volume data generation module 224-1 and the standard data generation module 224-2 use general-recorded (parcel)/non-recorded and special mail monthly data to calculate daily volume for each base. Generate quantity data (S400). Then, monthly volume data for each general office, monthly volume data for each agency, and yearly volume data for each agency are generated (S410). In the volume change generation module 224-3, the parcel generates yearly increase/decrease rate values based on regression analysis results, and uses the generated volume and increase/decrease rate values as a reference to calculate the increase/decrease rate by type and CAGR value by agency input from the user. Use it to generate future quantity values (S420). The future volume values generated at this time are generated in the form of daily volume values by type and general station. Then, the occupancy ratio value for each office is generated based on the nationwide volume and provided to the user (S430).

In this way, through the demand forecast standard quantity creation step (S220) and the future quantity creation step (S230), the process of transmitting the volume increase/decrease rate value for the future year to the input data conversion step is performed. In the input data conversion stage, the daily volume value for each general office is multiplied by the national volume increase/decrease input value to generate the simulated future daily volume. In addition, when the CAGP value for each agency is input, it is corrected to be equal to the sum of the volume by the national growth rate value, and the increase/decrease rate value for each future year is generated based on the input simulation period value and the corresponding actual future year's increase/decrease rate value. Correct the input data quantity standard information (S440).

At this time, the quantity standard information corrected in step S440 is a correction for the quantity at the national level. In other words, when the CAGR value for each agency is input, the value for each agency must be corrected in the same way as the sum of the volume by the national growth rate value, and the increase/decrease rate value for each future year must be generated to calculate the input simulation period value and the corresponding actual future year value. The input data volume standard information must be corrected based on the increase/decrease rate value. For this purpose, the volume standard information for each agency must be corrected together.

As a method of correcting the volume standard information by agency, correction according to the market share by agency or correction according to the increase/decrease rate by agency can be performed. In the embodiment of the present invention, when the increase/decrease rate by agency is selected, the method of correcting the volume standard information is explained as an example. I decided to do it. An embodiment of correcting the input data quantity standard information for each office will be described with reference to FIG. 17.

Figure 17 is an exemplary diagram of input data volume standard information correction according to an embodiment of the present invention.

As shown in FIG. 17, from the information stored in the information storage unit 500, the input data conversion unit 220 converts the detailed daily reception volume (S441) by postal type by general office in the base year into annual receipts by postal type by office in the base year. Count the quantity (S442). The input data conversion unit 220 multiplies the volume calculated in step S442 by a preset equation to correct the annual reception volume for each office and postal type (S443). At this time, the correction is repeated as many times as the target year. In addition, in the practice of the present invention, the preset equation is "1 + competent authority CAGR × correction value (t)" as an example, but is not necessarily limited to this.

At this time, the annual receipt volume by office and mail type in the base year may require correction of the detailed daily receipt volume by mail type by general office in the target year in more detail. Therefore, the preset formula used to aggregate the annual receipt volume by office and mail type in the base year can also be used to aggregate the detailed daily receipt volume by mail type by general office in the target year.

When data is generated as described in FIGS. 13 to 17 above, a procedure is performed to verify whether the reference data generated in step S210 has been properly generated. This will be explained with reference to FIG. 18.

Figure 18 is a flowchart showing a reference data verification procedure according to an embodiment of the present invention.

As shown in FIG. 18, it is determined whether any changes have occurred to the operating standard data for simulation (S500). If the operating standard data is changed, the corresponding standard data is searched and provided through the user interface unit 100 (S510). Then, after checking the changed standard data from the user interface unit 100 (S520), it is updated through the user interface and registered in the operation/transportation standard data 510 (S530). This process is repeated until registration of the changed data is completed.

On the other hand, if it is determined in step S500 that the reference data for simulation has not changed, a decision is made as to whether to perform the simulation according to the base year (S540). If it is determined that simulation is being performed, simulation is started (S580). On the other hand, when it is determined that the device is waiting to perform simulation, that is, when a simulation start message is generated from the user interface unit, demand forecast standard data is displayed on the user interface unit 100 (S550).

Then, after searching for the demand forecast value, the result is transmitted to the user interface unit 100 (S560), and when the demand forecast value is updated by the user, the changed information is stored (S570). In addition, for the demand forecast results and the simulation period (e.g., year, month), the volume ratio value to be communicated to the delivery bureau by time zone for the received quantity by general station, by logistics center work conditions, is generated as the simulation input quantity. Simulation is performed through the process (S580).

Next, continuing the description of FIG. 12, when the simulation unit 300 is driven in step S130 and the simulation performance is completed, the output data conversion unit 230 receives a message from the simulation engine that the simulation is complete and converts the simulation result data. to be displayed on the user interface screen. When a response plan establishment start message is received from the next user interface screen, it is transmitted to the response plan establishment unit 400 and instructs it to establish a response plan (S140). When the user interface unit 100 receives a message from the response plan establishment engine that a response plan has been established, it displays the corresponding response plan establishment result data on the user interface screen. Then, the simulation unit 300 performs a detailed load analysis simulation for each response plan to find out how much the load is when using the established response plans (S150).

The simulator result data storage module 520 of the information storage unit 500 stores comparative evaluation results for response methods, and the user interface unit 100 outputs the evaluation results to the user screen in order to provide the corresponding information to the user. (S160). When information indicating whether or not to select a response plan is input from the user, the user interface unit 100 determines whether to complete the establishment of a response plan based on the information (S170).

And among the simulation results, the user interface unit 100 provides comparison information for response plans established by year targeting overloaded bases so that the user can compare before and after the response plans established by year (S180). Afterwards, it is determined whether approval/application information notifying the user to plan with the information has been received (S190), and if it is determined that the plan has been approved with the corresponding response plan, the infrastructure is changed so that it can be reflected in the corresponding details. (S195). However, if it is determined not to approve the plan as a response plan, the procedures after step S120 are performed.

Next, the operation flows of the operation process simulation unit 310 and the transportation process simulation unit 320 of the simulation unit 300 according to an embodiment of the present invention will be described with reference to the drawings. This is to determine the processing capacity currently processed in the central office in order to predict future volume, and will be explained with reference to FIG. 19.

Figure 19 is an exemplary diagram showing a queue configuration according to mail classification according to an embodiment of the present invention.

As shown in Figure 19, when the mail volume arrives at the central office and is unloaded (S600), a shipment classification procedure is performed by affiliation (S610). Quantities that have been sorted are placed in a queue for each destination country and transported. At this time, regular/special/parcels are separated to form a pallet (S620).

At this time, the task priority according to the shipment classification procedure performed in step S610 of FIG. 19 is as shown in FIG. 20 below.

Figure 20 is an exemplary diagram of task priorities in a simulation process according to an embodiment of the present invention.

As shown in Figure 20, the shipment classification according to the embodiment of the present invention is largely divided into small type, large type, special type, and parcel type, but is not necessarily limited to this. Small systems are divided into machine processing, manual processing, and line separation, and large systems are divided into general processing and line separation. The machine processing capacity uses the efficiency of the machine classification, and the processing capacity of each unit is added up to obtain the machine processing capacity (pass/hour) without any division by number of the number of equipment.

In the case of small machines, OVIS processing capabilities are used when sorting shipments, and LSM processing capabilities are used when sorting arrivals. Additionally, in the case of small-sized machines, the sorting efficiency is entered by reflecting the re-input ratio when sorting shipments. And when classifying arrivals, the re-injection rate is not taken into consideration. The type of machine processing volume is reflected by each concentration station, and can also be reflected when a packet separator is used to sort parcels.

The small-scale manual work uses time processing capability (pass/time) input from the user interface unit 100. On the other hand, because small-scale line separation does not affect the system load, a certain amount of time (e.g., “minutes/pallet”) is given to the line separation task. At this time, all central stations apply the same time, and the same time is applied to small and large lines.

In the case of job priority when classifying shipments, when classifying machines, processing is done in the order of input without priority (FIFO: First In First Out). When manually sorting, the domestic volume is sorted in bulk during the shipment sorting process, and then detailed sorting is performed, so priority is given to the volume in other countries. However, priority of tasks is not considered when classifying arrivals.

In the case of volumes subject to processing, shipment classification applies to all volumes received/collected, and arrival category volumes included in the shipment category volume are also processed within the shipment classification time zone. For small-sized manual work, all manual work is processed and then the manual work generated after machine work is also processed.

Next, when it is confirmed that the mail volume shown in FIG. 19 has arrived at the logistics base, the procedures until the sorted volume is generated at the start stage, configured in the queue, and sent will be explained with reference to FIGS. 21 and 22. do. At this time, the volume generated at the general office is considered to have occurred at the general office by combining the post office receipt volume in addition to the volume at the general office. When calculating quantity, the region of the base year is used, and when performing simulation, the region of the corresponding year is used.

Figure 21 is an exemplary diagram showing a queue classification procedure for transport from a central station to a switching center according to an embodiment of the present invention, and Figure 22 is a queue sorting procedure for transport from a switching center to a centralized station according to an embodiment of the present invention. This is an example diagram showing .

First, as shown in Figure 21, when the goods that have arrived at the logistics base are unloaded and the sorted goods are generated (S700), a queue is formed for each destination country (S710). Afterwards, the status indicating delivery is determined according to the mail type and arrival time (S720). In normal cases, quantities sent before the second cut-off time for distribution from the arrival concentration station to the general station are considered to be delivered the next day, and quantities arriving later are considered to be delivered two days later. Therefore, quantities arriving before the first distribution cut-off time are calculated to be delivered on the same day, and those arriving after are calculated to be delivered the next day. Next-day express delivery is made on Saturday or Sunday.

Exchange network transportation refers to transportation between a concentration country (or logistics center) and an exchange center. Transportation consists of the following stages: vehicle generation - quantity loading - vehicle departure. Quantities that need to be redistributed to the designated concentration country are prevented from being transported to other concentration countries or exchange centers. Also, when loading a quantity, loading priority is given so that parcels/special parcels are processed first.

Depending on the volume level, the decision whether to go to a redemption center or a destination country is based on vehicle size and maximum number of vehicles. In the embodiment of the present invention, the size of the vehicle is described by taking 11 tons, 8 tons, and 5 tons as examples, but it is not necessarily limited to this.

In the concentration country → exchange center / concentration country transportation logic, when the volume going to one concentration country reaches one car (11 tons), it is set to be transported directly to the destination concentration country. And at the end of the first exchange shipment, if the volume going to one concentration country is more than 10 pallets, it will be put on standby, and if the volume going to one concentration country is less than 10 pallets, it will be transported to the exchange center (larger vehicles first, 11 tons → 8 tons → 5 tons). .In the case of the final vehicle bound for the first exchange center, if there is a parcel/special volume, it is sent unconditionally. If there is only regular volume, it is temporarily stored in the queue. At the end of shipment to other concentration countries, it is decided that if the volume to one concentration country reaches one car (5 tons, 8 tons), it will be transported directly to the arrival concentration station, and if the volume to one concentration country is less than 10 pallets, it is temporarily stored in the queue.

As for the results generated within the second exchange shipment deadline, if the volume going to one concentration country reaches one car (5 tons, 8 tons), it will be transported directly to the destination concentration country, and if the volume going to one concentration country is less than 10 pallets, it will be sent to the exchange center. Transport is carried out by (large vehicles first, 11 tons → 8 tons → 5 tons). In the case of the final vehicle destined for the secondary exchange center, it is treated as transported unconditionally regardless of load factor.

On the other hand, as shown in Figure 22, in the case of exchange center → concentrated transportation, when the exchanged volume is generated (S800), the volume is waiting for each arrival concentration country (S810), and then the volume is one car (e.g. , based on 11 tons) (S820). If it reaches the volume of one car, it is treated as transported (S830). At this time, the pallet composition is divided into regular/special/parcel. For the final vehicle, the tonnage level (5 tons, 8 tons) is determined relative to the volume (S840), and the final vehicle is sent unconditionally regardless of the load factor.

Next, the procedure after establishing a response plan in step S140 of FIG. 12 will be described with reference to FIG. 23.

Figure 23 is a flowchart of the procedure after establishing a response plan according to an embodiment of the present invention.

As shown in FIG. 23, when the response plan established by the response plan establishment unit 140 is selected by the user (S900), some or all of the functions for detailed response plans that need to be established among several response plans are established. I do it. The function establishment process is largely carried out in the infrastructure new phase (S910), jurisdiction design phase (S920), network structure design phase (S930), and processing capacity adjustment phase (S940).

First, in the infrastructure new stage (S910), the sorting processing capacity is calculated by inputting the hourly processing capacity of the new infrastructure and manual work for sorting shipments and arrivals by mail type (e.g., small, large, parcel, etc.). Do (S911). To calculate the processing capacity of the new infrastructure, the processing capacity of the new infrastructure is calculated by multiplying the processing capacity per hour entered from the user interface and the available time for each mail type and arrival category of the new infrastructure (S912).

In addition, the new infrastructure's normal and parcel shipping and arrival jurisdictions are input and the shipping and arrival volumes are calculated by mail type (small, large, and parcel) for each infrastructure. This is to calculate the separate volume values for each infrastructure by performing a process of re-aggregating and calculating the volume of the jurisdictional general bureau by mail type by infrastructure and mail type as a general station is added or removed from the new infrastructure.

Once the new infrastructure processing capacity calculation (S911) and the classification volume calculation for each infrastructure (S912) are completed, the load for each new infrastructure is recalculated (913S). To this end, the load for each new infrastructure is calculated by dividing the aggregated volume by mail type (small, large, parcel) and arrival by infrastructure by each processing capacity.

Next, in the jurisdictional area design stage (S920), changes to the jurisdictional infrastructure are input for each general office, and the shipment and arrival volume by postal type (small, large, parcel) by infrastructure is recalculated (S921). To this end, as the current jurisdictional infrastructure for commerce and parcel shipment and arrival is changed by each bureau, the volume of each jurisdiction by mail type and destination is recalculated and calculated. Then, the load for each infrastructure is recalculated (S922).

The next network structure design step (S930) is performed, in which data on new exchange center establishment, exchange time, processing capacity, and exchange area settings are input and simply stored in the simulation DB to be used as reference data for simulation of response measures.

Lastly, in the processing capacity adjustment step (S940), the classification processing capacity is recalculated by receiving corrections for the hourly processing capacity of the infrastructure and manual work for classification of mail types (small, large, parcel). do. It is calculated for each infrastructure (base) by multiplying the hourly processing capacity for each mail type for each mail type and the available time for each modified infrastructure (S941).

Then, when a function is removed due to the commerce and parcel specialization of the infrastructure, or when the function is adjusted again and a new one is established, a change plan for the general office in charge of the infrastructure is input and the sending and arrival volume by mail type (small, large, parcel) by infrastructure is reconsidered. As a calculation function, when a function is added, the volume of the jurisdictional general office by mail type by infrastructure and postal type is re-counted and calculated as a general station is added or removed from the infrastructure (S942).

If the function is removed, as the current jurisdictional infrastructure for commerce and parcel shipment and arrival is changed by each general office, the volume of the jurisdictional general office by mail type by infrastructure and mail type is recalculated to calculate the divided volume by infrastructure. C (S943). In addition, it is performed at the same time as the calculation of processing capacity by infrastructure and the calculation of classified volume by infrastructure is completed, and is a function to recalculate the load by infrastructure, calculating the categorized volume aggregated by mail type (small, large, parcel) by infrastructure and by arrival. Infrastructure processing capacity is adjusted through a process of calculating by dividing by the processing capacity of .

Once response measures are established according to the above procedures, it is possible to perform a detailed load analysis function by performing operation and transportation simulations using the standard information on the results generated during the establishment process.

The embodiments of the present invention described above are not only implemented through devices and methods, but may also be implemented through a program that realizes the function corresponding to the configuration of the embodiment of the present invention or a recording medium on which the program is recorded. The implementation can be easily implemented by an expert in the technical field to which the present invention belongs based on the description of the embodiments described above.

Although the embodiments of the present invention have been described in detail above, the scope of the present invention is not limited thereto, and various modifications and improvements made by those skilled in the art using the basic concept of the present invention defined in the following claims are also possible. It falls within the scope of rights.

Claims (15)

In the logistics infrastructure response simulation method through demand forecasting,
a user interface unit that receives data input from the user or provides information generated by the system; In order to analyze the data collected by the user interface unit, data statistical analysis is converted into a preset format.
wealth;
a simulation unit that performs simulation of operation and transportation based on data output from the user interface or converted data output from the data statistical analysis unit;
a response plan establishment unit that establishes and outputs a response plan according to the data output from the user interface unit; and an information storage unit that stores reference information for simulation, simulation result information, and response plan information.
Logistics infrastructure response simulation method through demand forecasting including. According to paragraph 1,
The user interface unit,
a data input unit that collects data input from the user and transmits the collected data to the data statistical analysis unit;
a simulation performance and result inquiry unit that, in conjunction with the simulation unit, provides data collected by the data input unit or simulation results output from the simulation unit to the user; and
In conjunction with the response plan establishment unit, a response plan is established based on the data collected by the data input unit or data output from the simulation unit, and the response plan result information generated by the response plan establishment unit is provided to the user. Establish response plan and result inquiry department
Logistics infrastructure response simulation method through demand forecasting including. According to paragraph 1,
The data statistical analysis unit,
a data migration unit that converts data output from a postal logistics information system linked to the simulation system or data output from the user interface unit into a preset data format;
An input data conversion unit that generates input quantities for future years based on the preset demand forecast generation results required to start the simulation and outputs them as simulation performance result data; and
An output data conversion unit that converts the simulation performance result data generated from the input data conversion unit into output data to provide it to the user.
Logistics infrastructure response simulation method through demand forecasting including. According to paragraph 3,
The input data conversion unit,
A recorded mail input data converter, an unrecorded mail input data converter, an operational and transit criteria data converter, and a future mail volume data converter, the recorded mail input data converter comprising:
a first volume creation module that extracts and stores volume data information by reception base, type, and day from the data converted by the data migration unit, and generates and stores reception volume information by a plurality of general bureaus;
a second quantity creation module that generates and stores quantity data for the sending and arriving stations based on the reception quantity information for each station generated in the first quantity creation module;
Ratio value generation module;
When receiving a message including simulation period information for a random year, growth rate value information for a random year, and simulation performance request information from the user interface unit, input standard quantity data for each base for simulation for the random year is received. an input-based quantity data generation module; and
When performing a base year simulation, an input quantity generation module that generates the input quantity data of the base year as the simulation input quantity.
Logistics infrastructure response simulation method through demand forecasting including. According to paragraph 4,
The unrecorded mail input data converter,
a volume data extraction module that extracts and stores volume data by base, daily, postal type, and work conditions from data output from the postal logistics information system;
a module that generates a ratio value based on the shipment/arrival/distribution volume values for each base from data output from the postal logistics information system and generates volume data based on input from the first and second management bureaus;
A manual labor volume data generation module that generates additional manual labor volume data when a manual labor volume ratio value occurs after machine work;
a simulation input data generation module that generates simulation input quantity data based on the uniform distribution value of the input quantity in the base year when receiving simulation start information from the input standard quantity data generation module; and
Upon receiving a request message from the user interface unit, a dividend volume ratio generation module that generates input standard volume data for each base to perform simulation for the future year.
Logistics infrastructure response simulation method through demand forecasting including. According to paragraph 4,
Operational and transportation reference data converters include:
A work standard information generation module that extracts the type and processing capacity of classification equipment and classification work condition standard information for each base from the data output from the data migration unit;
a processing volume calculation module for each time slot that obtains processing capacity data for each time slot from the data output from the data migration unit and generates processing volume values for each time slot for each base;
a ratio value generation module that generates shipment/arrival/distribution volume and ratio values for each base from the data output from the data migration unit;
a manual labor quantity calculation module that generates a ratio value of the manual labor volume to machine processing generated at each base from the data output from the data migration unit;
a contract quantity data generation module that acquires contract quantity data per parcel pallet from the data output from the data migration unit; and
A transportation distance rate data generation module that obtains transportation rate data from the data output from the data migration unit.
Logistics infrastructure response simulation method through demand forecasting including. According to paragraph 4,
The future postal volume data converter,
a volume data generation module that calculates daily or monthly volume for each base from the data output from the data migration unit;
a standard data generation module that calculates annual volume data for each base using the monthly volume calculated in the volume data generation module; and
A volume change generation module that calculates the national average annual growth rate using volume data from earlier than the current point and the newly created standard change volume value.
Logistics infrastructure response simulation method through demand forecasting including. According to paragraph 3,
The output data conversion unit,
a result generation confirmation module that transmits the simulation results and response plan results output from the simulation unit and the response plan establishment unit to the information storage unit;
a data search module that searches data by operating standards/transportation standards/volume/region stored in the information storage unit and provides the data to the user through the user interface unit;
A detailed load analysis module that calculates the overload ratio and maximum daily load for logistics hubs;
a vehicle consumption calculation module that calculates the vehicle consumption for each transportation section and transmits it to the information storage unit; A simulation method for responding to logistics infrastructure through demand forecasting, including a satisfaction rate calculation module for each type of mail that calculates the satisfaction rate of delivery standards for each type of mail and transmits it to the information storage unit. The method of claim 1, wherein the simulation unit,
an operation process simulation unit that performs a simulation of the operation process logic of a logistics base based on data output from the data statistical analysis unit or data output from the user interface unit; and
A transportation process simulation department that performs simulation of the transportation process for transportation between logistics bases, based on the results of the simulation performed in the operation simulation department.
Logistics infrastructure response simulation method through demand forecasting including. According to paragraph 1,
The response plan establishment department said,
An infrastructure new department that establishes new infrastructure according to the simulation results of the quantity information inputted from the outside, which is predicted to occur after the current point in time;
A jurisdictional design department that recalculates the load for each infrastructure by resetting the commercial and parcel jurisdiction areas of the already established infrastructure; a network structure design department that designs the network structure of the simulation system and generates reference information for the simulation; and a processing capacity adjustment unit that recalculates the load on the infrastructure by adjusting the processing capacity of overloaded infrastructure.
Logistics infrastructure response simulation method through demand forecasting including. The method of claim 10, wherein the infrastructure new department,
An information collection module that receives communication time and distance/time setting data from the user interface unit, transfers it to the information storage unit, and stores it as reference data for simulating a response plan;
A load calculation module for each infrastructure that calculates the level of volume for each infrastructure from the information collected by the information collection module; Calculation of new infrastructure processing capacity by calculating new infrastructure processing capacity from information collected by the above information collection module
module; and
An infrastructure-specific volume calculation module that receives the information calculated by the infrastructure-specific volume calculation module and the new infrastructure processing capacity calculation module, calculates the load by infrastructure, and transmits it to the user interface unit.
Logistics infrastructure response simulation method through demand forecasting including. The method of claim 10, wherein the jurisdictional design department,
A setting module that receives normal shipping jurisdiction, normal arrival jurisdiction, parcel shipping jurisdiction, and parcel arrival jurisdiction setting information from the user interface unit;
an infrastructure-specific load calculation module that calculates the infrastructure load by changing jurisdiction data based on the standard quantity information stored in the information storage unit and the information received by the setting module; and
An infrastructure-specific quantity calculation module that calculates the quantity that can be classified by infrastructure by changing jurisdictional data based on the standard quantity information stored in the information storage unit and the information received by the setting module.
Logistics infrastructure response simulation method through demand forecasting including. In the method of simulating logistics infrastructure response measures,
Upon receiving demand forecast data for either a base year or an arbitrary year, analyzing load by year based on the demand forecast data to generate load analysis information by year;
Establishing a response plan for each year based on the generated load analysis information for each year; and
A logistics infrastructure response simulation method through demand forecasting, which includes the step of establishing a response plan by analyzing the detailed load for each response plan based on the annual response plan established above. According to clause 13,
The step of generating the load analysis information by year is,
Converting the received demand forecast data into a preset format;
calculating a daily standard quantity based on the demand forecast data whose format has been converted; And a logistics infrastructure response simulation method through demand forecasting, including the step of analyzing the annual load based on the calculated daily standard volume. The method of claim 14, wherein the converting step includes:
Generating postal volume data for recorded or unrecorded mail based on the demand forecast data, generating delivery volume information based on a standard delivery country, and extracting volume data;
Extracting simulation operation and transportation standard data from the demand forecast data and generating operation/transportation standard data;
Generating daily quantity data for each base and monthly quantity data for each office to generate a demand forecast standard quantity; and
A logistics infrastructure response simulation method through demand forecasting, including the step of generating future volume for the arbitrary year based on the demand forecast standard volume.