Vessel allocation optimization device and method

Abstract

According to the present embodiments, the provided vessel allocation optimization device and method therefor comprise: storing first data required for a sea route plan among vessel allocation plans including the sea route plan, a shipment plan and an inventory plan, second data required for the shipment plan from among the vessel allocation plans, and third data required for the inventory plan from among the vessel allocation plans; applying all or some of the first data, the second data and the third data to an optimization model so as to establish vessel allocation plans, thereby minimizing deviation in the number of days of inventory; and executing the optimization model so as to output the established vessel allocation plans.

Description

Wiring optimization device and method

[0001] The present invention relates to a wiring optimization device and method for optimizing a wiring plan.

[0002] When transporting raw materials by ship, developing an appropriate transportation plan, including the vessel to be used, the goods to be loaded, and the route to be delivered, is crucial for ensuring a stable supply and reducing transportation costs. Furthermore, factories that manufacture products using transported raw materials must transport raw materials from their source to maintain inventory, thus ensuring a stable supply. However, as the types of raw materials handled and the number of factories serving as loading and unloading points increase, transportation planning becomes complex, making it difficult to devise ways to reduce transportation costs. To address these challenges, various planning methods utilizing optimization techniques such as mathematical programming and metaheuristics have been proposed.

[0003] However, existing optimization techniques use algorithms aimed solely at minimizing logistics procurement costs (freight, demurrage, and dispatch fees) and fail to account for efficient inventory management (yardage limits, inventory stability). In other words, establishing a cost-effective distribution plan requires ensuring a stable supply of raw materials while simultaneously minimizing inventory-day variance. However, existing optimization techniques fail to address this issue.

[0004] The present embodiments provide a wiring optimization device and method capable of increasing wiring efficiency by minimizing inventory days deviation.

[0005] The present embodiments provide a wiring optimization device and method for storing first data necessary for a routing plan among a routing plan including a routing plan, a shipping plan, and an inventory plan, second data necessary for a shipping plan among the routing plans, and third data necessary for an inventory plan among the routing plans, applying all or part of the first data, the second data, and the third data stored in a memory unit to an optimization model to establish a wiring plan to minimize a deviation in the number of days of inventory, and executing the optimization model to output the established wiring plan.

[0006] In one aspect, a wiring optimization device according to one embodiment includes a memory unit that stores first data necessary for the routing plan among a routing plan including a routing plan, a shipping plan, and an inventory plan, second data necessary for the shipping plan among the routing plans, and third data necessary for the inventory plan among the routing plans, a processing unit that applies all or part of the first data, the second data, and the third data stored in the memory unit to an optimization model to establish a wiring plan to minimize a deviation in the number of days of inventory, and an output unit that outputs a wiring plan established by executing the optimization model in the processing unit.

[0007] In another aspect, a wiring optimization method according to another embodiment includes a processing step of establishing a wiring plan to minimize a deviation in the number of days of inventory by applying all or part of first data necessary for the routing plan among the routing plans including the routing plan, the second data necessary for the routing plan among the routing plans, and the third data necessary for the inventory plan among the routing plans to an optimization model, and an output step of executing the optimization model in the processing step to output the established wiring plan.

[0008] The wiring optimization device and method according to the present embodiments enable an efficient wiring plan to be established by minimizing the deviation in the number of days of inventory.

[0009] Figure 1 is a block diagram of a wiring optimization device according to one embodiment.

[0010] Figure 2 is a block diagram of a wiring optimization device that receives first data through an input unit and second data through a communication unit.

[0011] Fig. 3 is a block diagram illustrating the processing unit of the wiring optimization device of Fig. 1.

[0012] Figure 4 is a graph showing the deviation in inventory days for the next 180 days by brand.

[0013] Figure 5 is a schematic diagram of the entire system in which wiring optimization is performed.

[0014] Figure 6 is a flowchart of a wiring optimization method according to another embodiment.

[0015] Figure 7 is a flowchart detailing the processing steps of the wiring optimization method of Figure 6.

[0016] Figure 8 is a configuration diagram of a computing system according to another embodiment.

[0017] Figure 9 is a configuration diagram of a client-server computer system according to another embodiment.

[0018] Hereinafter, some embodiments of the present disclosure will be described in detail with reference to exemplary drawings. When adding reference numerals to components in each drawing, identical components may have the same numerals as much as possible even if they are shown in different drawings. In addition, when describing the present embodiments, if it is determined that a detailed description of a related known configuration or function may obscure the gist of the technical idea of ​​the present invention, the detailed description may be omitted. When "includes," "has," "consists of," etc. are used in this specification, other parts may be added unless "only" is used. When a component is expressed in the singular, it may include a case in which the plural is included unless specifically stated otherwise.

[0019] Additionally, terms such as first, second, A, B, (a), (b), etc. may be used to describe components of the present disclosure. These terms are only intended to distinguish the components from other components, and the nature, order, sequence, or number of the components are not limited by the terms.

[0020] In a description of the positional relationship of components, when it is described that two or more components are "connected," "combined," or "connected," it should be understood that the two or more components may be directly "connected," "combined," or "connected," but that the two or more components may also be further "interposed" with another component to be "connected," "combined," or "connected." Here, the other component may be included in one or more of the two or more components that are "connected," "combined," or "connected" to each other.

[0021] In the description of the temporal flow relationship related to components, operation methods, or manufacturing methods, for example, when the temporal or flow relationship is described as “after”, “following”, “next to”, “before”, etc., it may also include cases where it is not continuous, unless “immediately” or “directly” is used.

[0022] Meanwhile, when numerical values ​​or corresponding information (e.g., levels, etc.) for components are mentioned, even without separate explicit description, the numerical values ​​or corresponding information may be interpreted as including an error range that may occur due to various factors (e.g., process factors, internal or external impact, noise, etc.).

[0023] The embodiments are described in detail with reference to the drawings below.

[0024] Figure 1 is a block diagram of a wiring optimization device according to one embodiment.

[0025] A wiring optimization device (100) includes a memory unit (110) that stores first data (10) necessary for a routing plan among a routing plan including a routing plan, a shipping plan, and an inventory plan, second data (20) necessary for a shipping plan among a routing plan, and third data (30) necessary for an inventory plan among a routing plan, a processing unit (120) that applies all or part of the first data (10), the second data (20), and the third data (30) stored in the memory unit (110) to an optimization model (121) to establish a wiring plan to minimize a deviation in the number of days of inventory, and an output unit (130) that outputs a wiring plan established by executing the optimization model (121) in the processing unit (120).

[0026] Routing planning for routing optimization includes a route plan that establishes a vessel's specific route, a loading plan that determines the quantity of goods to be loaded onto the vessel, and an inventory plan that minimizes variations in inventory days for each item at the port based on the route and loading plans. Developing a routing plan requires a variety of data, and the type of data used in each planning stage can be categorized based on the method used to develop the routing plan. For example, data may be used for route planning, loading planning, and inventory planning.

[0027] The memory section (110) stores various data for establishing a wiring plan.

[0028] Among the data stored, the first data (10) is data required for route planning in the distribution plan, and includes port entry information, loading and unloading information, nomi information, standard information that serves as the basis for cargo loading and operation, raw material purchase contract information, raw material transport contract information, etc.

[0029] Port entry information may include information such as the date a vessel entered a port. Loading and unloading information may include information on whether goods have been loaded or unloaded from a vessel. Nominal information may include information regarding a vessel's entry into a specific port. Standard information may include, for example, safety stock standards, available ports for vessel entry, available load quantities, the end date of the previous voyage, number of voyage days, and number of waiting days. Raw material purchase contract information may include quarterly purchase plans, and raw material transport contract information may include transportation contract information, vessel loading information, and contracted quantities of raw materials.

[0030] Among the data stored in the memory unit (110), the second data (20) is data required for the shipping plan during the wiring plan, and may be identical to all or part of the first data (10), or may be different. The second data (20) includes raw material information, which is information about cargo (e.g., steel raw materials). The raw material information may include information about the characteristics of the raw material (e.g., in the case of coal raw materials, the moisture content contained in the coal), the brand of the raw material, etc.

[0031] Among the data stored in the memory unit (110), the third data (30) is data required for inventory planning during the wiring plan, and includes usage information on the quantity of cargo required at each port, etc. The usage information may include the planned usage quantity at each port, basic inventory by product, etc.

[0032] The first data (10), the second data (20), and the third data (30) can be stored in the memory unit (110) in various ways. They can be stored in various ways, such as being input and stored through a separate input device, or being transmitted and stored from the outside, and are not necessarily limited to any one of them. However, the following description will be given as an example a case where the first data (10) is input through the input unit (140), and the second data (20) is received through the communication unit (150) and stored in the memory unit.

[0033] Figure 2 is a block diagram of a wiring optimization device that receives first data through an input unit and second data through a communication unit.

[0034] Referring to FIG. 2, the input unit (140) receives first data (10) required for route planning among the wiring plans and transmits it to the memory unit (110).

[0035] The communication unit (150) receives the second data (20) required for the shipping plan among the wiring plans and transmits it to the memory unit (130).

[0036] The memory unit (110) stores the first data (10) transmitted from the input unit (140) and the second data (20) transmitted from the communication unit (150). At this time, the third data (30) stored in the memory unit (110) may be transmitted through the input unit (140) or may be transmitted through the communication unit (150). Loaded on a ship

[0037] The processing unit (120) applies the first data (10), the second data (20), and the third data (30) to the optimization model (121) to establish a wiring plan to minimize the deviation in the number of days of inventory.

[0038] The output unit (130) outputs a wiring plan established by executing the optimization model (121) in the processing unit (120).

[0039] Establishing a distribution plan here means selecting an optimal distribution plan, such as the route layout for each vessel and the quantity to be loaded for each vessel, to minimize inventory days deviation.

[0040] The optimization model (121) of the processing unit (120) can select a route for each vessel for inventory management using all or part of the first data (10), the second data (20), and the third data (30), select a quantity to be shipped for each vessel using the route for each vessel and the aforementioned data, and establish an optimal routing plan to minimize the deviation in the number of days of inventory using the route for each vessel and the selected quantity for each vessel.

[0041] Fig. 3 is a block diagram illustrating the processing unit of the wiring optimization device of Fig. 1.

[0042] Referring to FIG. 3, the optimization model (121) of the processing unit (120) may include a route planning model (160), a shipment planning model (170), and an inventory planning model (180).

[0043] The route planning model (160) within the optimization model (121) selects a route plan for each vessel for inventory management using the first data from the input unit (140).

[0044] Specifically, routes are selected for each ship, including dedicated ships and spot ships. Here, dedicated ships refer to ships that only repeatedly operate on fixed routes, and spot ships refer to ships that can operate on a new route each time they finish operating without a fixed route.

[0045] Also, when selecting a route for each vessel, data on ports and vessel routes must be used, that is, information on which port cargo must be loaded, which route each vessel can use, and which port unloading must be performed. Therefore, among the first data (10), information such as ports available for vessel entry, the end date of the previous voyage, number of voyage days, and number of waiting days is required, and information such as port entry information, loading and unloading information, and nomi information is also required. Here, a voyage is a number that expresses the process of a vessel loading goods at a certain loading port and unloading them at a certain discharge port as a number of turns. For example, in the case of a vessel departing from Pohang to Australia, the process from loading goods in Pohang to unloading goods in Australia corresponds to one voyage, and when unloading is completed and the vessel departs from Australia, a new voyage begins.

[0046] When the first data (10) is input, the route planning model (160) calculates the route for each ship using a Monte Carlo simulation algorithm, and the loading port assignment is probabilistically sampled according to the distribution of the purchase plan quantity for each loading port.

[0047] Monte Carlo simulation is an algorithm that mathematically approximates the value of a specific function using repeated random sampling. In the route planning model (160), vessels are assigned to each route to meet the quarterly purchase plan required for each loading port. Since the quarterly purchase plan for each loading port must be known, raw material purchase contract information and raw material transportation contract information from the first data (10) are used. At this time, the quantity of each item is ignored and only the total amount of cargo to be loaded is considered. It is assumed that all vessels load their maximum possible load. Each vessel loads its maximum possible load, thereby reducing the quarterly purchase plan for each loading port. In other words, the efficiency of achieving the quarterly purchase plan for each loading port is determined by which vessels are assigned to which routes.

[0048] To this end, the route planning model (150) runs a Monte Carlo simulation to calculate which ships are deployed on which routes to effectively achieve the planned purchase quantity.

[0049] Specifically, input data for the simulation can include purchase plan and contract information, information on ports where dedicated ships can enter, tables of dedicated ship operation times (for empty and full ships, respectively), and tables of waiting times at loading and unloading ports, etc., which can be entered through code.

[0050] The following conditions can be entered into the simulation through the program code: a condition for assigning a vessel when the planned purchase balance is at the loading port; a condition for continuously operating chartered vessels; a condition for arriving at the loading port on any date for spot vessels; and a condition for randomly sampling and assigning vessels from the loading port balance distribution.

[0051] Among these, the condition of random sampling and assignment is to prevent cases where ships are not assigned to loading ports with small purchase plans when ships are assigned completely randomly. By probabilistically sampling and assigning ships by considering the distribution of purchase plans by loading port, the difficulties of applying Monte Carlo simulation can be overcome.

[0052] After the route is assigned to each ship, that is, after the loading port is assigned to each ship, a simulation is run to update the purchase plan of the loading port by subtracting the ship's loading volume from the purchase plan of the loading port. If the updated purchase plan is greater than 0, the purchase plan has not yet been fully achieved, so the updated purchase plan is input into the simulation and the simulation is run again to deploy the ships. After the loading port is assigned to each ship, the purchase plan of the loading port is updated by subtracting the ship's loading volume from the purchase plan of the loading port, and the simulation is repeated until the updated purchase plan of the loading port is less than 0.

[0053] The above-mentioned update process can be calculated using continuous operation conditions for dedicated lines, and can be calculated using the remaining purchase plan quantity for spot lines.

[0054] The simulation is repeated and if the purchase plan quantity of the updated loading port is less than 0, the simulation is terminated. As a result of the simulation, the route layout for each vessel is output, that is, the loading port and discharge port for each vessel, the arrival date and discharge date at the loading port, the arrival date and discharge date at the discharge port, and the total number of voyages.

[0055] The loading planning model (170) selects the loading quantity for each vessel to minimize the binomial unloading using the integer programming method.

[0056] The loading planning model (170) retrieves the results of the vessel-specific route arrangement obtained from the route planning model (160), i.e., the loading port and discharge port for each vessel, the arrival date and discharge date at the loading port, the arrival date and discharge date at the discharge port, and the total number of voyages, and then selects the loading quantity for each vessel using integer programming. In other words, the quantity of goods to be loaded onto the vessel is selected when the specific route plan for each vessel is determined. In this process, since the brand and quantity of each product to be loaded onto the vessel must be distinguished, the loading planning model (170) can utilize the raw material information, which is the second data (20), in addition to the vessel-specific route arrangement obtained from the route planning model (160). The integer programming is used in the process of determining the loading quantity for each vessel using the vessel-specific route plan and the second data (20), and in this process, conditions are set to minimize the unloading of two ports.

[0057] Integer programming is a mathematical model that optimizes an objective function while satisfying integer conditions. According to this example, when distributing the quantity of goods to each ship according to integer programming, the quantity of goods loaded on each ship can be obtained as an integer value, and at the same time, a case can be found where goods are loaded on each ship in an optimal quantity.

[0058] Specifically, the execution of integer programming involves setting decision variables, setting constraints, and then finding decision variable values ​​that optimize the objective function.

[0059] More specifically, the decision variables are defined as binary variables x and y (i.e., x and y take on the values ​​0 or 1). The binary variable x can be defined as x[voyage, hold, qty_level, brand, factory], and another binary variable y can be defined as y[voyage, factory], where y can take on the value 0 or 1 to indicate whether a particular voyage is assigned to a particular factory.

[0060] Here, voyage refers to the voyage number, hold refers to the ship's hold number, i.e., the number of the loading area where the goods are loaded on the ship, loading quantity (qty_level) refers to the quantity that can be loaded in each hold (loading area), brand refers to a value arbitrarily assigned to the brand of the goods, and factory refers to the number of the port of discharge.

[0061] For example, if the number of the voyage that first departs from Pohang, loads in Australia, and unloads in Pohang is 1, and the third hold of this ship's holds (loading areas) is loaded with 280,000 tons, the loaded goods are brand 1 of coal (brands are numbered according to arbitrary particle size and ballistic), and the discharge port of Pohang is 2, then the binary variables x and y can be expressed as x[1, 3, 28, 1, 2] and y[1, 2], respectively. After setting the decision variables, set the constraints. The constraints enforced on the model at this time are as follows.

[0062] First, a vessel and a hold can only have one loading quantity (qty_level), brand, and factory. Simply put, a vessel's hold can only carry a fixed quantity and brand, and the vessel can only discharge at a single discharge port. These constraints are in place because each hold on a vessel has a maximum capacity, and each hold must be loaded with goods up to its maximum capacity to ensure efficient loading. Furthermore, efficient discharge is achieved when the same brand is loaded on each hold. Furthermore, the goal is to prevent double-loading, i.e., discharging goods across two discharge ports instead of just one, which would increase transportation costs.

[0063] If we express the condition that one of the first conditions above must contain only one brand in a hold as a formula, it can be expressed as follows (here, the voyage number is 1, the number of holds is 3, the loadable quantity is fixed at 280,000 tons, the discharge port number is fixed at 2, and it is assumed that there are 10 brands (brand 1 to brand 10)).

[0064] x[1, 3, 28, brand1, 2] + x[1, 3, 28, brand2, 2] + x[1, 3, 28, brand3, 2] + … + x[1, 3, 28, brand10, 2] = 1

[0065] That is, for a ship operating on the first voyage, only one brand from brand 1 to brand 10 can be loaded on the ship's hold 3. For example, if brand 1 is loaded on the hold 3 of the above ship, x[1, 3, 28, brand 1, 2] = 1, and the remaining brands, such as x[1, 3, 28, brand 2, 2] and x[1, 3, 28, brand 3, 2], cannot be loaded on hold 3, so they have a value of 0.

[0066] Second, the total loading quantity of a vessel ranges from the minimum loading quantity (qty_level) to the maximum loading quantity (qty_level).

[0067] The reason why the minimum loading quantity (qty_level) is set is to ensure that each hold of the ship is not empty as it is assumed that all ships are loaded to their maximum capacity, and the reason why the maximum loading quantity (qty_level) is set is to ensure that the loadable quantity set for each hold is not exceeded.

[0068] Thirdly, there is a set upper limit on the number of brands that can be shipped per vessel.

[0069] Since the number of holds per ship is fixed and each hold can only load one brand, the number of brands that can be loaded on a ship has a certain upper limit.

[0070] Finally, the goods a vessel carries have a set lower limit per item.

[0071] All ships are assumed to be loaded with as much goods as possible, and since each specific brand of goods can only be loaded in one hold, a minimum quantity of goods loaded is set to ensure that as much as possible is loaded in one hold.

[0072] After setting the decision variables and constraints as described above, the decision variable values ​​that optimize the objective function are found. Here, the objective function can refer to determining the number of ships loaded to minimize the number of binomial unloadings (y). The process of finding the decision variable values ​​that optimize the objective function can be performed using the open-source GLPK (GNU Linear Programming Kit) as a solution program (solver).

[0073] The shipping planning model (170) determines the optimal loading quantity for each vessel by finding the values ​​of decision variables through the above process. That is, it uses information such as the vessel's route layout determined by the route planning model (160), such as the vessel's loading port, discharge port, and number of voyages, as well as third-party data (30), to determine information on the brands to be loaded for each vessel and the quantity for each brand.

[0074] The inventory planning model (180) adjusts voyages using a genetic algorithm based on information on vessel-specific route arrangements and vessel-specific loading quantities determined in the route planning model (160) and shipment planning model (170), and applies a swapping simulation module to each adjusted voyage to minimize inventory days deviation. At this time, basic inventory information corresponding to usage information, the third data (30), is used to determine the inventory days for each item.

[0075] Specifically, the inventory planning model (180) uses a genetic algorithm to change the ship-by-ship dispatch schedule (arrival date and departure date at the loading port for each ship, arrival date and departure date at the unloading port for each ship) resulting from the route and shipment planning department.

[0076] The GA package in the R language can be used as a genetic algorithm, specifically adjusting the voyage through the processes of selection, crossover, and mutation. Selection refers to selecting some or all of the voyages assigned to all ships. Crossover refers to the process of randomly swapping voyages. Mutation refers to the process of changing each voyage order to a random value.

[0077] For example, consider a route layout where a single (dedicated) vessel departs Pohang, loads in Australia, and unloads in Pohang. The first voyage is then followed by a second voyage, this same vessel departs Pohang, loads in Canada, and unloads in Gwangyang. In this case, the voyage order can be varied. That is, the first and second voyages can be swapped, a process known as a crossover. As a result of the crossover, the vessel would depart Pohang, load in Canada, and unload in Gwangyang as the first voyage, while the second voyage would depart Pohang, load in Australia, and unload in Pohang. If this crossover reduces inventory variance, it approaches the optimal voyage.

[0078] The mutation process involves changing the number of voyages of the first voyage from 1 to an arbitrary value, for example, 3, even though it is known that changing the voyages 1 and 2 above will reduce the number of days in inventory. In this case, the route that was previously the first voyage to Pohang, Australia, and Pohang will be changed to the third voyage of the vessel, not the first voyage. This allows for exploration of various scenarios. The selection process described above can be applied to all or some of the voyages, and the crossover and mutation processes can be applied simultaneously, only one of them can be applied, or neither can be applied.

[0079] The route layout applied through the process of selection, crossover, and mutation according to the genetic algorithm has a route layout with a different arrangement of voyages from the vessel-by-vessel voyages obtained from the existing route and shipping planning model, and numerous route layouts (first route layout, second route layout, etc.) are obtained depending on the number of crossovers and mutations that occur.

[0080] The genetic algorithm is specifically implemented as a swapping simulation module described below.

[0081] The swapping simulation module changes the ship's routing schedule based on a random variable (a real number ranging from 1 to 90) generated for each voyage.

[0082] To be more specific, you can set a random variable called x and reflect in the simulation code the content of adjusting the navigation schedule according to the value of x.

[0083] At this time, the x value determines the order of the routes between the same dedicated lines in the order of the size of the x value.

[0084] For example, in the case of the route layout of a dedicated ship, the first voyage (Pohang -> Australia -> Pohang) is assigned a random variable value of 10, and the second voyage (Pohang -> Canada -> Gwangyang) is assigned a random variable value of 2. The swapping simulation module adjusts the voyage order from the second voyage with a large random variable value to the first voyage with a small random variable value. In other words, the voyage from Pohang -> Canada -> Gwangyang becomes the first voyage, and the voyage from Pohang -> Australia -> Pohang becomes the second voyage. In the case of a spot ship that operates once, since there is only one route, the voyage order is adjusted using the date of arrival at the loading port as the random variable value.

[0085] This method is applied to all route layouts obtained through the genetic algorithm to adjust the voyage order. When the voyage order is adjusted, the route layout for each vessel changes, and since the quantity loaded at each loading port changes due to the changed route layout, the number of days of inventory for each item at each port changes. In other words, routing plans with different route layouts and loading quantities for each vessel determined in the route planning model (150) and loading planning model (160) are generated, and these routing plans have different number of days of inventory for each item.

[0086] When the inventory days of the goods are obtained according to each distribution plan, the inventory planning model (180) finds a distribution plan that minimizes the deviation of the inventory days. In other words, the optimal solution x that optimizes the inventory days deviation for various distribution plans for the random variable x input in the simulation can be derived. The deviation of the inventory days is compared based on 180 days and is expressed in the following mathematical expression 1.

[0087] [Mathematical Formula 1]

[0088] X d = d-day inventory quantity (ton) / d-day daily usage (ton / day)

[0089] In mathematical expression 1, d is a natural number from 1 to 180, and X d is the inventory days deviation for the next 180 days.

[0090] According to mathematical expression 1, the optimal routing plan is determined to be the one that minimizes the deviation in the number of inventory days over the next 180 days. As described above, the routing plan at this time may be a plan in which the vessel-specific routing arrangement and vessel-specific loading quantity obtained from the route planning model (160) and the loading planning model (170) are partially modified. In other words, the inventory planning model (180) updates all or part of the routing plans obtained from the route planning model (160) and the loading planning model (170).

[0091] The output unit (130) executes the optimization model in the processing unit (120) and outputs a routing plan (route arrangement by ship, quantity loaded by ship) that minimizes the deviation in the number of days of inventory according to the optimal solution x.

[0092] Figure 4 is a graph showing the deviation in inventory days for the next 180 days by brand.

[0093] Referring to Fig. 4, (a), (b), (c), and (d) of Fig. 4 show the deviation of inventory days for the next 180 days by brand of goods. The horizontal axis represents the date from the 1st to the 180th day, and the vertical axis represents the inventory days for each date. The dotted line is a graph showing the inventory quantity for the existing goods for each brand for 180 days, and the solid line is the result of minimizing the deviation of inventory days for 180 days through the route planning model (160), the shipment planning model (170), and the inventory planning model (180). The dotted line on the graph is a graph showing the deviation of inventory days when the optimization model (121) is not executed, and the solid line is a graph showing the deviation of inventory days when the optimization model (121) is applied. If we look at the vertical axis data within the rectangular area in each of the graphs (a), (b), (c), and (d), i.e. the number of days in stock by date, we can see that the number of days in stock along the solid line does not differ significantly from the number of days in stock along the dotted line when compared to the area outside the rectangular area. In other words, as a result of applying the optimization model (121), we can see that the number of days in stock on each date does not fluctuate significantly over a period of 180 days and is evenly distributed.

[0094] Figure 5 is a schematic diagram of the entire system in which wiring optimization is performed.

[0095] Referring to FIG. 5, the interior of the wiring optimization system (200) is composed of a standard management (201) window including information on business standard management and optimization standard management, a ship wiring (202) window including information on wiring optimization and wiring plan establishment, a supply and demand plan (203) window including information on raw material use plan and inventory management, and an operation management (204) window including information on ship operation / movement management, loading / unloading performance management, and cargo fee management.

[0096] Specifically, some of the standard information among the first data (10) may be entered in the work standard management section of the standard management (201) window. For example, information such as safety stock standards may be entered, and information such as ports where ships can enter, quantity available for loading, end date of previous voyage, number of voyage days, and number of waiting days may be entered in the optimization standard management section.

[0097] In the wiring optimization section of the ship wiring (202) window, a simulation model used in the route planning model (150), the shipping planning model (160), and the inventory planning model (170) can be input, and in the wiring plan establishment section, a wiring plan that minimizes the deviation in the number of inventory days can be input.

[0098] In the raw material usage plan section of the supply plan (203) window, the planned usage amount at each port corresponding to the usage information can be entered, and the inventory management section can include the basic inventory for each item corresponding to the usage information. In the ship operation / movement management section of the operation management (204) window, information on the Automatic Identification System (AIS) and the location of the ship on the map that is entered from outside the system can be entered, and in the loading / unloading performance management section, the loading and unloading performance of the port according to the loading information and unloading information that are entered from outside the system can be entered. In addition, the unloading fee management section can input the unloading fee information included in the raw material transportation contract information.

[0099] In addition to data directly input within the wiring optimization system (200), there is data transmitted to the system from outside the wiring optimization system.

[0100] First, among the data transmitted from within the supply chain management (SCM) to the distribution optimization system (200), the raw material specification information may include raw material information, for example, information such as the moisture content of coal in the case of coal as a raw material. The raw material usage plan / shipping information may include usage information such as the planned usage amount by port. The logistics standard information may include information such as the safety stock standard among the standard information, the distribution status information may include information on the routes of dedicated ships and spot ships, and the transportation contract information and demurrage information may include information such as the contracted quantity of raw materials and the contracted demurrage. In addition, the raw material purchase contract information may include the planned quarterly purchase amount, etc. At this time, the raw material specification information and the raw material usage plan / shipping information are transmitted to the system and input into the optimization model, and the logistics standard information, distribution status information, transportation contract information, and demurrage information are input into the optimization model in the system and are also newly recorded data according to the results obtained by executing the optimization model in the system.

[0101] Among the data (220) corresponding to resource management (ERP), raw material usage records may include information on planned usage amounts in usage information, receipts and disbursements information may include raw material purchase and transportation contract information, and basic inventory may also include information on basic inventory for each item in usage information. This information is transmitted to the system and input into the optimization model.

[0102] In addition, among the data (230) transmitted (API call) from outside the supply chain (SCM) to the system, AIS / Weather's vessel information and map may include information on the vessel's location, and weather information may include weather data. Among the shipping company / agent data, shipment information may include information on the quantity of cargo loaded at each port, port entry information may include information on the date and time the vessel entered each port, and nomi information may include information on the country to which the vessel is designated. Unloading information, which is unloading history data, may include information on the quantity of cargo unloaded at each port. At this time, the data included in the shipment information, port entry information, nomi information, and unloading information may be input into the optimization model. The wiring optimization system (200) receives all or part of data input within the system and data (210) input from outside the system but within the supply chain management (SCM) or data input from outside the supply chain management (SCM), executes an optimization model within the system, and establishes an optimal wiring plan based on the data.

[0103] The wiring optimization system (200) described above is an example of a part of the wiring optimization device (100) described in FIG. 1 that can be implemented in software.

[0104] All or part of the first data (10) input into the input unit (140) of the wiring optimization device, the second data (20) transmitted from the communication unit (150) to the processing unit (120), and the third data (30) stored in the memory unit (110) and transmitted to the processing unit (120) are input into the wiring optimization system (200) from inside or outside the supply chain management (SCM), and the input wiring optimization system (200) executes the optimization model (121) to establish an optimal wiring plan that minimizes the deviation in the number of days of inventory, and stores the plan in the wiring plan establishment section of the ship wiring (202) window. 121

[0105] That is, although the overall system configuration diagram of FIG. 5 omits the configuration of the input unit (140), communication unit (150), and memory unit (110) of the wiring optimization device (100) of FIG. 1, it can be viewed as software that implements the roles of the input unit (140), communication unit (150), and memory unit (110) of the wiring optimization device (100) because it receives raw material purchase contract information, raw material transport contract information, reference information, port entry information, loading and unloading information, and nomi information as first data (10), receives raw material information as second data (20), and receives usage information as third data (30).

[0106] In addition, the wiring optimization system (200) executes an optimization model (121) using the first data (10), the second data (20), and the third data (30) to establish an optimal wiring plan and then stores it, so it can be viewed as software that implements the configuration of the processing unit (120) of the wiring optimization device (100), the optimization model (121) included in the processing unit (120), and the output unit (130).

[0107] Figure 6 is a flowchart of a wiring optimization method according to another embodiment.

[0108] Referring to FIG. 6, a wiring optimization method (300) according to another embodiment includes a processing step (S310) of establishing a wiring plan to minimize a deviation in the number of days of inventory by applying first data (10) required for the routing plan, second data (20) required for the routing plan, and third data (30) required for the routing plan among a routing plan including a routing plan, a shipping plan, and an inventory plan to an optimization model (121), and an output step (S320) of outputting the wiring plan established by executing the optimization model (121) in the processing step (S310).

[0109] In the processing step (S310), the first data (10), the second data (20), and the third data (30) are applied to the optimization model (121) to establish a wiring plan to minimize the deviation in the number of days of inventory.

[0110] The first data (10) is data required for route planning in the distribution plan, and includes port entry information, loading and unloading information, nomi information, standard information that serves as the basis for cargo loading and operation, raw material purchase contract information, raw material transport contract information, etc.

[0111] Port entry information may include information such as the date a vessel entered a port. Loading and unloading information may include information on whether goods have been loaded or unloaded from a vessel. Nominal information may include information regarding a vessel's entry into a specific port. Standard information may include, for example, safety stock standards, available ports for vessel entry, available load quantities, the end date of the previous voyage, number of voyage days, and number of waiting days. Raw material purchase contract information may include quarterly purchase plans, and raw material transport contract information may include transportation contract information, vessel loading information, and contracted quantities of raw materials.

[0112] The second data (20) is data required for the shipping plan during the distribution plan, and may be identical to or different from all or part of the first data (10). The second data (20) includes raw material information, such as information regarding cargo (e.g., steel raw materials). The raw material information may include information regarding the characteristics of the raw material (e.g., in the case of coal raw materials, the moisture content of the coal), the brand of the raw material, etc.

[0113] The third data (30) is data required for inventory planning during the distribution plan, and includes usage information such as the quantity of cargo required at each port. The usage information may include the planned usage quantity at each port, basic inventory by product, etc.

[0114] The second data (20) may be identical to all or part of the first data (10), or may be different.

[0115] Fig. 7 is a flowchart detailing the processing step (S310) of the wiring optimization method of Fig. 6.

[0116] Referring to FIG. 7, the processing step (S310) may include a route planning step (S311), a shipment planning step (S312), and an inventory planning step (S313).

[0117] As mentioned above, in the route planning step (S311), the first data (baseline information, raw material purchase contract information, raw material transport contract information, port entry information, loading information, unloading information, and nominal information) are input from the input step (S300), and then the route for each vessel is calculated through Monte Carlo simulation. Monte Carlo simulation is an algorithm that mathematically approximates the value of a specific function using repeated random sampling. In the route planning step (S311), vessels are assigned to each route to meet the quarterly purchase plan required for each loading port.

[0118] In the loading planning stage (S312), the ship-specific route layout obtained in the route planning stage (S311), i.e., the loading port and discharge port for each ship, the arrival date and discharge date at the loading port, the arrival date and discharge date at the discharge port, and the total number of voyages are input to select the loading quantity for each ship. In other words, the quantity of goods to be loaded onto the ship is selected when the specific route plan for each ship is determined. Since the brand and quantity of each goods to be loaded onto the ship must be distinguished in this process, in addition to the ship-specific route layout obtained in the route planning stage (S311), the raw material information, which is the second data (20), can be used in the loading planning stage (S312). The selection process is performed using integer programming. Integer programming refers to a mathematical model that optimizes an objective function while satisfying integer conditions. According to this example, when the quantity of goods is allocated to each ship according to integer programming, the quantity of goods to be loaded onto each ship can be obtained as an integer value, and at the same time, a case where the goods are loaded onto each ship in an optimal quantity can be found.

[0119] In the inventory planning stage (S313), based on the vessel-specific route layout and vessel-specific loading quantity determined in the route planning stage (S311) and loading planning stage (S312), a genetic algorithm is used to adjust voyages. A swapping simulation module is then applied to each adjusted voyage to minimize inventory days deviation. At this time, basic inventory information, corresponding to usage information (the third data (30), is used to determine the inventory days for each item.

[0120] In the output stage (S320), the result obtained by executing the optimization model (121) in the processing stage (S310), i.e., the routing plan (route arrangement by ship, loading quantity by ship) that minimizes the deviation in inventory days is output.

[0121] The wiring optimization method according to another embodiment described with reference to FIGS. 6 and 7 can be applied in the same or similar manner to the contents described in the wiring optimization device (100) according to one embodiment described with reference to FIGS. 1 to 3.

[0122] The wiring optimization device and method according to the present embodiments can minimize future inventory days deviation by applying a simulation model that takes inventory days into account.

[0123] Figure 8 is a configuration diagram of a computing device (400) according to embodiments of the present invention.

[0124] Referring to FIG. 8, a computing device (400) may include a memory (410) and a processor (420).

[0125] The memory (410) can store the first data (10), the second data (20), and the third data (30), but can also be stored separately in a separate large-capacity storage server, etc. The memory (410) can be a volatile memory (e.g., SRAM, DRAM) or a non-volatile memory (e.g., NAND Flash).

[0126] When the processor (420) receives the first data (10) and the second data (20) and the third data (30), it executes the optimization model (121) to minimize the inventory days deviation and outputs the result.

[0127] The memory (410) stores an optimization model (121). When a wiring optimization task is requested, the optimization model (121) stored in the memory is executed to output a wiring plan that minimizes the deviation in the number of days of inventory according to the first data (10), the second data (20), and the third data (30).

[0128] A computing system according to embodiments of the present invention may include a computer device (400) including a memory (410) and a processor (420) and a server (500) including a memory (410) and a processor (420). The computer device (400) and the server (500) may be connected wired or wirelessly via a network (600).

[0129] The memory (510) of the server (500) can store the aforementioned optimization model (121).

[0130] When a wiring optimization task is requested (queried), the processor (420) of the computer device (400) retrieves the first data (10), the second data (20), and the third data (30) stored in the memory (410). The memory (410) of the computer device (400) can store the first data (10), the second data (20), and the third data (30) described above.

[0131] The memory (410) of the computer device (400) can transmit the first data (10), the second data (20), the third data (30) stored in the memory (410) and this request (query) to the server (500).

[0132] The processor (520) of the server (500) can execute an optimization model (121) on the first data (10), second data (20), and third data (30) received to output a wiring plan that minimizes the deviation in the number of days of inventory, and transmit the result to a computer device (400).

[0133] Various possible examples are described below for the computer system described with reference to FIGS. 8 and 9.

[0134] The wiring optimization device (100) may be configured as a computing system (400) as shown in FIG. 9, or may be configured as a GPU server equipped with a GPU processor and general memory, but the present invention is not limited thereto.

[0135] The above-described wiring optimization device (100) may be implemented by a computing device including at least some of a processor, a memory, a user input device, and a presentation device. The memory is a medium that stores computer-readable software, applications, program modules, routines, instructions, and / or data, etc., which are coded to perform a specific task when executed by the processor. The processor can read and execute the computer-readable software, applications, program modules, routines, instructions, and / or data stored in the memory. The user input device may be a means for allowing a user to input a command to cause the processor to perform a specific task or to input data necessary for the execution of a specific task. The user input device may include a physical or virtual keyboard or keypad, key buttons, a mouse, a joystick, a trackball, a touch-sensitive input means, a microphone, etc. The presentation device may include a display, a printer, a speaker, or a vibration device, etc.

[0136] Computing devices can include a variety of devices, including smartphones, tablets, laptops, desktops, servers, and clients. A computing device may be a single, standalone device, or it may include multiple computing devices operating in a distributed environment, each of which collaborates with another through a communications network.

[0137] In addition, the above-described wiring optimization device (100) can be executed by a computing device having a processor and a memory storing computer-readable software, applications, program modules, routines, instructions, and / or data structures coded to perform a wiring optimization method (300) utilizing an optimization model when executed by the processor.

[0138] The embodiments described above may be implemented through various means. For example, the embodiments may be implemented through hardware, firmware, software, or a combination thereof.

[0139] In the case of hardware implementation, the wiring optimization method (300) utilizing the optimization model according to the present embodiments may be implemented by one or more ASICs (Application Specific Integrated Circuits), DSPs (Digital Signal Processors), DSPDs (Digital Signal Processing Devices), PLDs (Programmable Logic Devices), FPGAs (Field Programmable Gate Arrays), processors, controllers, microcontrollers, or microprocessors.

[0140] For example, the wiring optimization method (300) according to the embodiments may be implemented using a semiconductor device implemented with semiconductor elements. In this case, the semiconductor elements may be currently used semiconductor elements, such as SRAM, DRAM, NAND, etc., or may be next-generation semiconductor elements, such as RRAM, STT MRAM, PRAM, etc., or may be a combination thereof.

[0141] When implemented using firmware or software, the wiring optimization method (300) according to the present embodiments may be implemented in the form of a device, procedure, or function that performs the functions or operations described above. The software code may be stored in a memory unit and executed by a processor. The memory unit may be located within or outside the processor and may exchange data with the processor using various known means.

[0142] Additionally, terms such as "system," "processor," "controller," "component," "module," "interface," "model," or "unit" as described above may generally refer to a computer-related entity, such as hardware, a combination of hardware and software, software, or software in execution. For example, the aforementioned components may be, but are not limited to, a process driven by a processor, a processor, a controller, a control processor, an object, a thread of execution, a program, and / or a computer. For example, both an application running on a controller or a processor and the controller or the processor may be components. One or more components may be within a process and / or thread of execution, and the components may be located on a single device (e.g., a system, a computing device, etc.) or distributed across two or more devices.

[0143] Meanwhile, another embodiment provides a computer program stored on a computer storage medium that performs the aforementioned wiring optimization method (300). Furthermore, another embodiment provides a computer-readable storage medium storing a program for implementing the aforementioned wiring optimization method.

[0144] The program recorded on the recording medium can be read, installed and executed by a computer, thereby executing the steps described above.

[0145] In this way, in order for a computer to read a program recorded on a recording medium and execute functions implemented as a program, the above-mentioned program may include code coded in a computer language such as C, C++, JAVA, or machine language that can be read by the computer's processor (CPU) through the computer's device interface.

[0146] Such code may include functional code related to functions defining the aforementioned functions, and may also include control code related to execution procedures required for the computer's processor to execute the aforementioned functions according to a predetermined procedure.

[0147] Additionally, such code may further include memory reference related code regarding where in the internal or external memory of the computer the additional information or media required for the computer's processor to execute the aforementioned functions should be referenced.

[0148] Additionally, if the computer's processor needs to communicate with another computer or server located remotely in order to execute the functions described above, the code may further include communication-related code regarding how the computer's processor should communicate with another computer or server located remotely using the computer's communication module, and what information or media should be sent and received during the communication.

[0149] The computer-readable recording medium that records the program as described above includes, for example, ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical media storage device, etc., and may also include one implemented in the form of a carrier wave (e.g., transmission via the Internet).

[0150] Additionally, computer-readable recording media can be distributed across network-connected computer systems, allowing computer-readable code to be stored and executed in a distributed manner.

[0151] In addition, the functional program for implementing the present invention and the code and code segments related thereto may be easily inferred or changed by programmers in the technical field to which the present invention belongs, taking into consideration the system environment of the computer that reads the recording medium and executes the program.

[0152] The wiring optimization method (300) described through FIGS. 6 and 7 may also be implemented in the form of a recording medium containing computer-executable instructions, such as an application or program module executed by a computer. Computer-readable media may be any available media that can be accessed by a computer, and include both volatile and nonvolatile media, removable and non-removable media. Furthermore, computer-readable media may include all computer storage media. Computer storage media include both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable instructions, data structures, program modules, or other data.

[0153] The above-described wiring optimization method (300) can be executed by an application that is installed by default on the terminal (which may include a program included in a platform or operating system installed by default on the terminal), or by an application (i.e., a program) that the user directly installs on the master terminal through an application providing server such as an application store server, an application, or a web server related to the service. In this sense, the above-described wiring optimization method can be implemented as an application (i.e., a program) that is installed by default on the terminal or directly installed by the user, and recorded on a computer-readable recording medium such as on the terminal.

[0154] The foregoing description of the present invention is for illustrative purposes only, and those skilled in the art will readily appreciate that the present invention can be readily modified into other specific forms without altering the technical spirit or essential characteristics of the present invention. Therefore, the embodiments described above should be understood as illustrative in all respects and not restrictive. For example, each component described as a single entity may be implemented in a distributed manner, and similarly, components described as distributed may be implemented in a combined manner.

[0155] The scope of the present invention is indicated by the claims described below rather than the detailed description above, and all changes or modifications derived from the meaning and scope of the claims and their equivalent concepts should be interpreted as being included in the scope of the present invention.

[0156] The above description is merely an illustrative example of the technical idea of ​​the present disclosure, and those skilled in the art to which the present disclosure pertains will appreciate that various modifications and variations can be made without departing from the essential characteristics of the technical idea of ​​the present disclosure. In addition, the present embodiments are not intended to limit the technical idea of ​​the present disclosure but rather to explain it, and therefore the scope of the technical idea of ​​the present disclosure is not limited by these embodiments. The scope of protection of the present disclosure should be interpreted by the claims below, and all technical ideas within a scope equivalent thereto should be interpreted as being included within the scope of the rights of the present disclosure.

[0157]

[0158] CROSS-REFERENCE TO RELATED APPLICATION

[0159] This patent application claims priority under 35 USC § 119(a) to Korean Patent Application No. 10-2023-0117385, filed September 5, 2023, the entire contents of which are incorporated herein by reference. Furthermore, this patent application claims priority in countries other than the United States for the same reasons, the entire contents of which are incorporated herein by reference.

Claims

1. A memory unit storing first data required for the route plan among the routing plan including the route plan, the shipping plan, and the inventory plan, second data required for the shipping plan among the routing plan, and third data required for the inventory plan among the routing plan; A processing unit that establishes a wiring plan to minimize the inventory days deviation by applying all or part of the first data, the second data, and the third data stored in the memory unit to an optimization model; and A wiring optimization device including an output unit that outputs the wiring plan established by executing the optimization model in the processing unit.

2. In paragraph 1, An input unit for receiving the first data required for the route plan among the routing plan including the route plan, the shipping plan, and the inventory plan; and In addition, the wiring plan further includes a communication unit for receiving the second data required for the shipping plan; A wiring optimization device in which the memory section stores the first data input to the input section and the second data received to the communication section.

3. In paragraph 1, The above first data includes reference information, raw material purchase contract information, and raw material transport contract information required for route planning among the above wiring plans, and a wiring optimization device including operation information, loading and unloading information, and nomi information.

4. In paragraph 1, The second data is a wiring optimization device including raw material information required for the shipping plan among the wiring plans.

5. In paragraph 1, The third data is a wiring optimization device that includes usage information required for the inventory plan among the wiring plans.

6. In paragraph 1, The above optimization model establishes the routing plan including the route plan, the shipping plan, and the inventory plan to minimize the inventory days deviation. A route planning model that selects a route plan for each vessel using all or part of the first data, which are standard information, raw material purchase contract information, raw material transport contract information, port entry information, loading information, unloading information, and cargo information; A shipping planning model that selects the quantity to be shipped per ship using the ship-specific route plan output from the above route planning model and the raw material information, which is the second data; and A routing optimization device including an inventory planning model that modifies the vessel-specific route plan and the vessel-specific shipment quantity to minimize the inventory days deviation by using the vessel-specific route plan, the vessel-specific shipment quantity, and the third data, usage information, output from the vessel-specific route planning model and the vessel-specific shipment planning model.

7. In paragraph 6, The above route planning model is a wiring optimization device that calculates the route plan for each ship using a Monte Carlo simulation algorithm.

8. In paragraph 7, The above Monte Carlo simulation algorithm is a routing optimization device that randomly samples and assigns shipping ports according to the distribution of purchase plans for each shipping port.

9. In paragraph 6, The above-mentioned loading planning model is a wiring optimization device that selects the loading quantity for each vessel to minimize binomial unloading using an integer programming method.

10. In paragraph 6, The above inventory planning model is a wiring optimization device that adjusts the number of days of inventory using a genetic algorithm and applies a swapping simulation module to each adjusted number of days to minimize the deviation.

11. A processing step of establishing a routing plan to minimize the deviation in inventory days by applying all or part of the first data required for the routing plan, the second data required for the shipping plan, and the third data required for the inventory plan among the routing plans, to an optimization model among routing plans including a routing plan, a shipping plan, and an inventory plan; and A wiring optimization method comprising an output step for outputting the wiring plan established by executing the optimization model in the processing step.

12. In paragraph 11, An input step for receiving the first data required for the route plan among the routing plan including the route plan, the shipping plan, and the inventory plan; and A wiring optimization method further comprising a communication step of receiving the second data required for the shipping plan among the above wiring plans.

13. In paragraph 11, A method for optimizing a wiring plan, wherein the first data includes reference information, raw material purchase contract information, and raw material transport contract information required for a route plan among the wiring plans, and includes operation information, loading and unloading information, and nomi information.

14. In paragraph 11, A wiring optimization method wherein the second data includes raw material information required for the shipping plan among the wiring plans.

15. In paragraph 11, A wiring optimization method wherein the third data includes usage information required for the inventory plan among the wiring plans.

16. In paragraph 11, The above optimization model establishes the routing plan including the route plan, the shipping plan, and the inventory plan to minimize the inventory days deviation. A route planning model that selects a route plan for each vessel using all or part of the first data, which are standard information, raw material purchase contract information, raw material transport contract information, port entry information, loading information, unloading information, and cargo information; A shipping planning model that selects the quantity to be shipped per ship using the ship-specific route plan output from the above route planning model and the raw material information, which is the second data; and A routing optimization method including an inventory planning model that modifies the vessel-specific route plan and the vessel-specific loading quantity to minimize the inventory days deviation by using the vessel-specific route plan, vessel-specific loading quantity, and the third data, usage information, output from the vessel-specific route planning model and the vessel-specific loading planning model.

17. In paragraph 16, The above route planning model is a routing optimization method that calculates the route plan for each ship using a Monte Carlo simulation algorithm.

18. In paragraph 17, The above Monte Carlo simulation algorithm is a routing optimization method that assigns shipping ports by probabilistically sampling according to the distribution of purchase plans for each shipping port.

19. In paragraph 16, The above-mentioned loading planning model is a routing optimization method that selects the loading quantity for each vessel to minimize binomial unloading using integer programming.

20. In paragraph 16, The above inventory planning model is a wiring optimization method that adjusts the number of days of inventory using a genetic algorithm and applies a swapping simulation module to each adjusted number of days to minimize the deviation.

21. A routing plan including a route plan, an inventory plan, and a shipping plan, first data required for the route plan, second data required for the shipping plan, and third data required for the inventory plan, and a memory storing an optimization model, wherein the optimization model is an optimization model that minimizes the inventory days deviation from the first data, the second data, and the third data; and A computer device including a processor that, when a task of minimizing the deviation of the number of days of inventory for any first data, second data, or third data is requested, executes an optimization model for minimizing the deviation of the number of days of inventory stored in the memory to minimize the deviation of the number of days of inventory from the first data, second data, or third data.

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