WO2010041432A1 - Ship allocation plan creation device, method, and program - Google Patents

Abstract

A ship allocation plan creation device creates a ship allocation plan for transporting raw materials of a plurality of brands from a plurality of loading ports to a plurality of discharging ports.  The ship allocation plan creation device comprises: a data acquisition means; a ship resource creation means; a mathematical model setting means for setting a mathematical model for expressing at least the operation restriction of a ship included in a ship resource and the supply and demand balance restrictions of the raw materials at the discharging ports; an optimization calculation means for, using the set mathematical model, performing an optimization calculation according to an objective function constructed for at least transport cost; a simulator operating according to the optimization calculation result and including a stock transition simulator for simulating the transition of a stock state and a ship operation state transition simulator for simulating the transition of a ship operation state; and an output means.

Description

Ship allocation plan creation device, method and program

The present invention relates to a ship assignment plan creation apparatus, method, and program suitable for creating a ship assignment plan for transporting raw materials of a plurality of brands from a plurality of loading sites to a plurality of landing sites. This application claims priority based on Japanese Patent Application No. 2008-260170 filed in Japan on October 6, 2008, the contents of which are incorporated herein by reference.

In many industries, including steel, it is required to make an optimal transportation plan for raw materials. In the case of steel, multiple brands of raw materials (ores, coals, etc.) are transported by ship (ship funding source) from mountain bases scattered around the world to multiple steelworks. In this case, it is necessary to create a ship allocation plan so as to keep the contracted target quantity of collection and to prevent out of stock when each transported raw material is used every day at the steelworks. When preparing a ship allocation plan, cost (cost) is judged as an important indicator, and transportation costs (freight, which is the cost of hiring a ship, and stagnation that is paid if the ship is anchored at the port for longer than the contract period) ) And purchase costs must be minimized (minimized).

As this type of technology, Patent Document 1 distributes by repeatedly assigning brands that are likely to run out of stock preferentially after reading raw material usage plans and annual operation plans of raw material carriers as known data. An inference apparatus for material transportation allocation plan for creating a ship plan is disclosed.

Further, in Patent Document 2, the operation of each means of transportation is simulated based on the constraint conditions in the simulation unit, and the stock amount change for each material brand is calculated based on the simulation result in the raw material stock amount change calculation unit. There is disclosed a physical distribution plan creation device that calculates and evaluates the result by an evaluation value calculation unit.

JP-A-8-272402 Japanese Patent Laid-Open No. 11-310313

In Patent Document 1, in a ship allocation planning device, a brand to be processed is selected, a ship suitable for transporting the selected brand is selected, and a transportation schedule is determined. For example, when selecting a brand to be processed, a standard is disclosed such that an essential brand that requires a certain amount of stock is selected first. However, a specific method for each selection is not disclosed. There are a wide variety of combinations of brands, ships, and transportation schedules to be processed, and it is necessary to perform calculations while changing each of these factors until a satisfactory result is obtained. It will take.

In addition, because ships are selected with essential brands first, there is a problem that the number of out-of-stocks or the quantity of out-of-stocks cannot be minimized when all brands are viewed within a given period. there were. In other words, there is a problem that the balance between long-term supply and demand is not taken into consideration because priority is given to the prevention of short-term stocks out of stock. For this reason, the inventory is maintained when viewed in the short term, but there is a problem that it is not possible to avoid the situation where there is no ship to be transported when the inventory is exhausted.

In Patent Document 2, the scale of the simulation unit and the raw material inventory transition calculation unit tends to be large. In addition, as the number of constraints increases, recalculation must be repeated in order to obtain an appropriate evaluation, and a longer time is required until a satisfactory result is obtained.

In actual transportation, ships are operated based on different types of contracts, and ships are called, for example, continuous voyages, irregular ships, and spot ships according to these contract types. However, Patent Documents 1 and 2 do not consider the minimization of transportation costs including the types of ships.

In the contract of a ship, it is necessary to dispatch a continuous voyage ship and an irregular ship. On the other hand, for spot ships, it is necessary to determine the size of the ship to be hired and the number of ships appropriately according to the take-up target amount and inventory status. However, Patent Documents 1 and 2 do not consider the minimization of transportation costs including the employment form and fleet configuration of such ships. In other words, for example, it is better to ship with one continuous voyage ship and two spot ships with a maximum load capacity of 75000t, or with one continuous voyage ship and three spot ships with a maximum load capacity of 50000t. It is necessary in the actual work to allocate ships considering this. In this way, the shipping cost of a ship varies greatly depending on the maximum load capacity and the route of the ship to be hired. In Patent Documents 1 and 2, there is no ship allocation taking these into consideration.
In addition, in actual operation, if the stock status of the stock to be used is severe, a stock with similar properties (a stock with a certain chemical property in common: a stock that can be used by replacing each other) is used as an alternative. As a result, out of stock is being suppressed. In addition, by actively using this alternative, it will be changed to a brand that can only be transported by a ship with a high freight rate. Is going. However, in Patent Documents 1 and 2, no ship assignment is performed in consideration of brands having similar properties.

The present invention has been made in view of the situation as described above, and enables a ship allocation plan for transporting a plurality of brands of raw materials from a plurality of loading sites to a plurality of landing sites quickly and with high accuracy. The purpose is to enable the minimization of transportation costs, including the decision to hire or not to hire a ship that determines the type and composition of the fleet.
Furthermore, by considering brands with similar properties, the objective is to enable further suppression of out-of-stocks and minimization of transportation costs compared to individual brands.

(1) A ship assignment plan creation device according to a first aspect of the present invention is a ship assignment plan creation device for creating a ship assignment plan for transporting a plurality of brands of raw materials from a plurality of loading sites to a plurality of landing sites. Ships on which a plurality of vessels to be operated based on a plurality of types of chartering contracts are listed, the scheduled usage amount of the raw materials, the target amount of the raw materials, the stock status of the raw materials, the purchase cost of the raw materials Data fetching means for fetching data including a list, a ship operation status of each ship, and a transportation cost when using each ship; the ship required from the ship list based on the ship operation situation; And a formula model representing at least a ship funding creation means for creating a ship fund; and a ship operation constraint included in the ship fund and a demand-supply balance constraint of the raw material at the landing site A formula model setting means for setting a model; an optimization calculation means for performing an optimization calculation based on at least an objective function constructed with respect to the transportation cost by using the set formula model; and a result of the optimization calculation A simulator including an inventory transition simulator for simulating the transition of the inventory status and a ship navigation status transition simulator for simulating the transition of the vessel operation status; Output means for outputting a plan.
(2) In the ship allocation plan creation device of (1) above, when the raw materials are handled, a plurality of brands of the raw materials included in a range defined by chemical properties may be grouped.
(3) In the ship allocation plan creation apparatus according to (1), the mathematical model setting unit may further set a mathematical model that represents a target amount restriction of the raw material.
(4) In the ship allocation plan creation device of (1), the chartering contract type of the ship included in the ship list may include a continuous voyage ship, an irregular ship, and a spot ship.
(5) In the ship allocation plan creation device according to (4) above, the ship finance generation means extracts the continuous navigation ship having an undecided operation portion in the plan creation period based on the ship list and the ship operation status. For each of the extracted continuous voyage vessels, all of the combination patterns of the loading place and the landing place in the plan creation period may be created.
(6) In the ship allocation plan creation device according to (5), the ship fund generation means can be used in the plan creation period based on the ship list and the ship operation status, and there is an operation undetermined part. The irregular ship may be extracted, and for the extracted irregular ship, all the combinations of the loading place and the landing place in the plan creation period that meet a predetermined condition may be created. .
(7) In the ship allocation plan creation device according to (6), the ship funding creation means includes a total sum of the take-up target amounts in the plan creation period, and a maximum of the extracted continuous sailing ship and the irregular ship. The amount of the raw material to be transported by the spot ship may be calculated based on the total load capacity, and the spot ship candidates may be extracted based on the ship list.
(8) The ship allocation plan creation device according to the above (1) to (7) sets the mathematical model with a predetermined macro time accuracy within a preset optimization period, performs the optimization calculation, and performs the optimization calculation. A macro optimization unit comprising the formula model setting means and the optimization calculation means for outputting a calculation result in a part of the plan determination period of the optimization period; and the plan determination obtained by the macro optimization unit Using the calculation result in a period, the mathematical model is set with micro time accuracy finer than the macro time accuracy in the plan finalization period, the optimization calculation is performed, and the result of the optimization calculation is calculated with the simulator And a micro-optimization unit including a mathematical expression model setting unit and an optimization calculation unit.
(9) In the ship planning plan creation device according to (1) to (7), the simulator reflects the change spilloverly when a change occurs in the operation time of one voyage of the continuous voyage ship. The operation time of the subsequent voyage of the continuous voyage ship may be corrected, and the mathematical model setting means and the optimization calculation means may perform processing based on the correction.
(10) In the ship allocation plan creation device according to the above (1) to (7), the optimization calculation means: for each loading place, collects and collects the pick-up amount of all the brands in a seasonal unit or a monthly unit. The amount of pick-up is calculated by summing up the amount of the pick-up target for all the brands in a seasonal unit or a month for each loading point; The optimization calculation may be performed based on an objective function that further aims to minimize the difference between the accumulation and the accumulation of the collection target amount.
(11) In the ship planning plan creation device according to the above (1) to (7), the optimization calculation means: sums up and accumulates the unloading amount of all the brands in a seasonal unit or a monthly unit for each landing site. The unloading accumulation accumulation is calculated by calculating the standard unloading capacity accumulation for each landing site by summing up and accumulating the standard unloading capacity amount in a seasonal unit or a monthly unit; The optimization calculation may be performed based on an objective function for the purpose of minimizing the difference from the standard landing capacity of the landing site.
(12) The ship allocation plan creation device described in (1) to (7) described above is for ship type, number of ships, loading place, landing place, loading brand, lifting brand, loading, and lifting quantity individually according to the user's intention. You may further have an input part which enables fixation.
(13) The ship allocation plan creation device according to (1) to (7) may further include an input unit that enables the loading place, loading brand, and loading amount to be fixed collectively according to the user's intention. Good.
(14) In the ship allocation plan creation device described in (1) to (7) above, the transportation cost may include a freight rate and a berthing fee.
(15) The ship allocation plan creation method according to the second aspect of the present invention is a ship allocation plan creation method for creating a ship allocation plan for transporting a plurality of brands of raw materials from a plurality of loading sites to a plurality of landing sites. There: a plurality of ships operated based on the planned use amount of the raw material, the target amount of the raw material, the stock status of the raw material, the purchase cost of the raw material, and a plurality of types of dredger contracts Fetching data including a listed ship list, a ship operation status of each ship, and a transportation cost when using each ship; based on the ship operation status by a ship fund generation means; Selecting the ship from the ship list and creating a ship fund; and, by mathematical model setting means, operation restrictions of the ship included in the ship fund and the raw material at the landing A step of setting a mathematical model that represents at least a supply-demand balance constraint of a fee; and an optimization calculation means that uses the set mathematical model to perform an optimization calculation based on at least an objective function constructed with respect to the transportation cost A step of simulating the inventory status and the vessel operation status based on a result of the optimization calculation by a simulator; and a step of outputting a ship allocation plan as a simulation result by the simulator by an output means; Having
(16) According to the third aspect of the present invention, a program for causing a computer to execute a process for creating a ship assignment plan causes the computer to function as the ship assignment plan creation device described in (1) above. It is a program for.

According to the present invention, it is possible to create a ship allocation plan for transporting a plurality of brands of raw materials from a plurality of loading sites to a plurality of landing sites quickly and with high accuracy. In addition, the types of ship contracts (continuous voyage ships, irregular ships, spot ships), and the fleet composition (ship funding) with the decision to hire / not hire each ship are optimized for minimizing transportation costs. Is possible.
Furthermore, by considering brands with similar properties, it is possible to further optimize out-of-stock control and minimizing transportation costs compared to considering individual brands individually.

It is a figure which shows schematic structure of the whole system containing the ship allocation plan preparation apparatus which concerns on this embodiment. It is a flowchart for demonstrating the ship assignment plan preparation process by the ship assignment plan preparation apparatus which concerns on this embodiment. It is a figure for demonstrating the ship list | wrist among capture data. It is a figure for demonstrating the ship operation condition of acquisition data. It is a figure for demonstrating the freight list of capture data. It is a figure for demonstrating the list of the berthing charges among capture data. It is a flowchart for demonstrating the selection process of a ship. It is a figure which shows a mode that the pattern of the combination of a loading place and a landing place in the plan preparation period is created about the extracted continuous voyage ship. It is a figure for demonstrating the conveyance amount which should be supplemented with a spot ship. It is a figure for demonstrating the route list of a spot ship. It is a figure which shows the relationship between time and stock quantity. It is a figure for demonstrating the restriction | limiting that a taking over amount and a taking over target amount do not leave | separate greatly. It is a figure for demonstrating the restriction | limiting that a taking over amount and a taking over target amount do not leave | separate greatly. It is the figure which showed typically the relationship between macro optimization and micro optimization. It is a figure for demonstrating the objective function aiming at the leveling of the load in a loading area. It is a figure for demonstrating the objective function aiming at the leveling of the load in a landing. It is the result of the ship allocation plan planned by a skilled person skilled in the art by a conventional method. It is the ship allocation plan result planned using the ship allocation plan preparation apparatus and method which concern on this embodiment.

Hereinafter, preferred embodiments of the present invention will be described with reference to the accompanying drawings. In the present embodiment, an example will be described in which raw materials such as ore and coal are transported to a plurality of steelworks from mountains scattered all over the world.

(First embodiment)
FIG. 1 is a diagram illustrating a schematic configuration of an entire system including a ship allocation plan creation device according to the present embodiment. In FIG. 1, 100 is a ship allocation plan creation device, which transports multiple brands of raw materials (ore, coal, etc.) from a plurality of loading sites (mountains scattered around the world) to a plurality of landing sites (steelworks). Create a ship assignment plan. The purpose of this embodiment is to create a ship allocation plan that minimizes the transportation cost of all steelworks, not the leveling of transportation costs for each steelworks. Furthermore, it aims to create a ship allocation plan that minimizes costs including purchase costs in addition to transportation costs.

200 is a blending plan creation device that creates a blending plan for blending raw materials for each steel mill. As a result, the planned amount of use of the raw material transported to each steelworks is planned every day. As a blending plan creation technique in the blending plan creation apparatus 200, any technique may be used.

300 is a database that stores data used by the devices 100 and 200 and data calculated by the devices 100 and 200.

Reference numeral 400 denotes a host computer called a process computer or the like, which refers to data stored in the database 300 or stores and updates data in the database 300.

The ship allocation plan creation device 100 uses the planned amount of raw materials used, the target amount to be taken, the ship list in which ships with different contract types are listed from the database 300, the operational status of the ships listed in the ship list, Data such as inventory status, raw material purchase costs, and transportation costs when using a ship listed in the ship list are taken in, and for example, a ship allocation plan for three months (September) is created. Here, January is a period obtained by dividing January into three. Specifically, the ship allocation plan includes continuous navigation ships, non-regular ships, landing sites for spot ships (shipping ports), loading brands, loadings, port order, berth berth, entry / exit timing, and hire Determine the number and type of spot ships (the size of the ship defined based on the maximum capacity of the ship), etc.

In the ship allocation plan creation apparatus 100, 101 is a simulator that simulates ship operation, loading and unloading facilities, yards, and the like. The simulator 101 is a loading port (unloading port), a loading brand, a loading amount, a calling port for a continuous voyage ship, an irregular ship, and a spot ship determined by a macro optimization unit 102 and a micro optimization unit 103 described later. Receives information on the order, berth berth, entry / exit timing, number of spot ships to be hired and ship type (the size of the ship defined based on the maximum load capacity of the ship), and performs detailed simulation based on this information Execute. This simulator is composed of an inventory transition simulator and a ship operation status transition simulator.

The inventory transition simulator calculates the inventory transition of raw materials at each steelworks. This inventory transition simulator calculates the inventory transition for each brand of raw material in detail, taking into account the planned use amount of the raw material for each steelworks and the unloading time for each brand of the ship's raw material. For example, if multiple brands are loaded on a ship and one brand is unloaded and then the second brand is unloaded, if the yard capacity is overflowing, the stock amount of raw materials on the yard will be reduced, and the yard capacity can be afforded. Taking into account whether it is necessary to unload after a certain amount of time, the transition of the inventory corresponding to the unloading time is accurately simulated.

The ship operation status transition simulator includes the arrival date and time (ETA: Estimated Time Of Arrival) of the loading port, the date and time of arrival at the loading port (ETB: Estimated Time Of Berthing), and the date and time of departure from the loading port (ETD: Estimated Time Of) Calculate the transition of ship operation status including Departure). In addition to loading capacity and unloading capacity, this ship operation status transition simulator takes into account interference with other ships, such as whether or not there is a ship on the other quay (if it exists) Simulate ship operation. For example, regarding the number of unloaders used to unload vessels, in addition to the position of the hatch where the brand to be unloaded is loaded, whether or not there is a vessel handling at another berth of the same unloading port is considered. . The loading capacity is affected by the number of unloaders used for unloading. As an example, when unloading with one unloader, unloading can be performed with a capacity of 100% at 1500 t / h. In the case of two unloaders, unloading can be performed with a capacity of 70% at 1500 t / h × 2. The above-mentioned ship operation status transition simulator incorporates changes in unloading capacity due to conditions such as the unloader radix into the simulation and accurately simulates it. This makes it possible to create a specific production / logistics plan that takes into account the fine constraints required for actual operations.

Reference numeral 102 is a macro optimization section, which assumes that the total amount of freight out of transportation costs will be based on the assumption that there will be no hindrance to the compounding plan (scheduled amount of raw materials used) at the steelworks and that the amount that can be shipped will be protected. For the purpose of making it the cheapest, for the purpose of making it the cheapest, there are landing sites (unloading ports) for continuous voyage vessels, irregular ships, spot vessels, loading brands, loading amount, order of port calls, and the number of spot vessels to be hired. And optimization to determine the ship type (the size of the ship defined based on the maximum load capacity of the ship). The macro optimization unit 102 includes a ship funding source creation unit 102a that functions as a ship funding source creation unit according to the present invention, a formula model setting unit 102b that functions as a formula model setting unit according to the present invention, and an optimization calculation unit according to the present invention. A functioning optimization calculation unit 102c is provided, and for example, 9 seasons are calculated with seasonal accuracy.

Reference numeral 103 denotes a micro-optimization unit which performs optimization so as to determine a docking berth and an entry / exit timing at which the total amount of the berthing fee is the lowest in the plan optimized by the macro-optimization unit 102. An instruction for 101 is calculated. The micro optimization unit 103 includes a formula model setting unit 103a that functions as a formula model setting unit according to the present invention, and an optimization calculation unit 103b that functions as an optimization calculation unit according to the present invention. Calculate to

Reference numeral 104 denotes a data fetching unit that functions as a data fetching unit in the present invention, which is listed in the ship list, a list of ships whose raw materials are scheduled to be used, a target quantity to be taken, and different types of contracts. Ship operation status, raw material inventory status, raw material purchase cost, transportation cost when using a ship listed in the ship list, ship berth status at loading area, loading capacity status, facility repair -Capture data such as planned outage, ship berthing status at landing site, unloading capacity status, facility repair / outage schedule.

Reference numeral 105 denotes an output unit functioning as an output means in the present invention, which is a ship allocation plan created as a simulation result by the simulator 101, specifically, a landing site for a continuous voyage ship, an irregular ship, and a spot ship (loading). Unloading port), unloading brand, unloading order, arrival order, berthing berth, arrival / departure timing, number of spot ships to be hired and ship type (the size of the ship defined based on the maximum loading capacity of the ship) Display on screen or send data to external device.

Hereinafter, the details of the ship assignment plan creation processing by the ship assignment plan creation apparatus 100 according to the present embodiment will be described. FIG. 2 is a flowchart for explaining the steps of each process in the ship assignment plan creation method using the ship assignment plan creation apparatus 100. In the present embodiment, a ship allocation plan is created with a plan creation period of 3 months (9 seasons) from the planning start date set by the user.

(1) Data acquisition (step S101)
The data acquisition unit 104 of the ship allocation plan creation device 100 includes a ship list in which ships with different raw material usage schedules, take-up target quantities, and contract types are listed from the database 300, and ships that are listed in the ship list. Operational status, raw material inventory status, raw material purchase cost, transportation cost when using a ship listed in the ship list, ship berthing status at loading site, loading capacity status, facility repair / suspension schedule, lifting Capture data such as ship berthing status, unloading capacity status, facility repair / outage schedule, etc.

Here, the planned usage amount of raw materials is information representing the planned usage amount for each steelworks (landing site) and for each brand of raw material in the plan creation period, calculated from the blending plan created by the blending plan creation device 200. is there. The raw materials differ in quality, properties, etc. for each brand, so the amount to be used for each brand is determined and blended.

The collection target amount is information representing the collection target amount (planned collection amount) for each Yamamoto (loading place) and each brand. For example, each Yamamoto contracts with each brand to decide how much to take for each year, for example. Dividing it by the number of seasons gives the target amount for each season. It is required to allocate ships so as to approach this take-up target amount. However, in relation to the ship allocation plan, up and down fluctuations of about tens of thousands of tons from the take-off target amount are within the allowable range through negotiations with Yamamoto. In addition, depending on the contract, there may be a contract in which a predetermined brand is not picked up for a predetermined period. Specific information regarding such a contract may be included in the fetched data.

As shown in FIG. 3, the ship list is information that lists ships with different types of contracts, specifically, continuous navigation ships, irregular ships, and spot ships here. A continuous voyage vessel is a vessel that has a contract to continue voyage during the contract period. For this reason, it is required to allocate ships with the highest priority. Irregular ships are ships that have a contract that sails only for the number of contracts in the contract period or that only sails for the contracted period. For this reason, it is required to ship as much as possible within the contracted voyage number or contracted voyage period. Spot ships are usually unsigned at the simulation stage. Even if a continuous voyage ship and a non-regular ship are assigned, if the necessary take-up amount cannot be satisfied or the inventory of the landing site cannot be satisfied, the ship can be requested to sail in a spotted contract form. For continuous voyages, charter code (code that identifies each ship), contract classification, contract period (start date and end date), maximum load capacity, and ship name are described. For irregular ships, charter code, contract category, contract period (start date and end date), contract details (contracted voyage or contracted voyage period), maximum loading capacity, and ship name are described. These continuous cruise ships and non-regular ships are listed individually, but for spot ships, the name of the region where the ship can navigate and the type of ship (the size of the ship defined based on the maximum load capacity of the ship). ), And chartering code (area name and size is described), contract classification, and maximum load capacity are described.

Pmax representing the ship type of the spot ship is a ship that can pass through the Panama Canal (generally this ship type is called Panamax), Cape is a ship that can pass Cape Cape (generally this ship type is called Cape size), VL (Very Large ) Means a larger ship than these. Here, the normal Panamax is a ship that is 900 feet long and 106 feet wide and has a maximum load capacity of 60,000 to 80,000 tons. The normal cape size refers to ships with a maximum capacity or capacity of 150,000 to 170,000 tons. For spot ships, at the stage of making a ship assignment plan, based on this navigable area and the size of the ship, the required number of ships and ship type (the ship size defined based on the maximum load capacity of the ship) decide. At a stage where the ship allocation plan is finalized to some extent, a procedure for negotiating with the actual shipping company and contracting a ship that matches the above-mentioned ship type is taken. For this reason, at the stage of making a ship allocation plan, the number of ships and the type of ship (the size of the ship defined based on the maximum load capacity) are first determined in an uncontracted state (before negotiation with the shipping company). It is required to decide.

As shown in FIG. 4, the vessel operation status is information representing the actual operation status and the confirmed schedule of each vessel listed in the vessel list. As with loading-lifting, loading-loading-lifting, loading-lifting-lifting, loading ports and lifting ports may be one port or multiple ports. Such a series of ship operations is handled as one voyage, and a voyage number is given. For each ship listed in the ship list, the voyage No. Unloading dock classification, unloading serial number, unloading port code, berth code, unloading port arrival date (ETA), unloading port arrival date (ETB), unloading port departure date (ETD), voyage Time is listed.

For example, voyage No. of continuous cruise ship A 3 arrived off the port of loading (X1 port) at 20 o'clock on March 7, 2008, and arrived at the berth represented by code “1” of loading port (port X1) at 20 o'clock on March 12, 2008. After leaving the port (X1 port) at 20 o'clock on March 14, 2008, sailed for 46920 minutes and arrived at the offshore of port (B port) at 10 o'clock on April 16, 2008, at 13:00 on April 16, 2008 This is a voyage that berthed at the berth represented by the code “11” of the unloading port (Port B) and left the unloading port (Port B) at 14:00 on April 18, 2008. In addition, since this ship has a continuous voyage, the continuous voyage ship A sails continuously in the order of loading-lifting-loading-lifting. In other words, voyage No. After leaving the last port of No. 2 (Port D) at 9:00 on February 22, 2008, Heading to the first port of No. 3 (X1 port), sailing for 20820 hours and arriving off the port of X1 at 20:00 on March 7, 2008.

When the planning start date (the first day of the target period for formulating a ship allocation plan) is in the future with respect to the date of planning, the stock status of raw materials is determined by the blending plan created by the blending plan creation device 200. This is information representing the stock status by steelworks (land of unloading) and brand by date at the start of planning. Further, when the planning start date is in the past with respect to the date of execution of planning, the stock status of raw materials is information representing the actual stock status of each material brand input to the database by each steelworks.

The purchase cost is information indicating the purchase cost of raw materials by Yamamoto (loading place) and brand.

The transportation cost is information representing a freight when using a ship listed in the ship list and a stagnation fee for each loading port when using a ship listed in the ship list.

Fig. 5 shows an example of a freight list. As shown in the figure, for each ship listed in the ship list, dredger code, loading port, 1 port, 2 port, 3 port, freight (dollar / ton) are described. For example, the continuous cruise ship A has a freight of 16.00 when sailing from the loading port X1 to the unloading port A, and a freight when sailing from the loading port X1 to the unloading port A to the unloading port B is 16.24. . As can be seen from the freight list, the freight rate is generally cheaper using a continuous cruise ship than using an irregular ship or a spot ship.

Fig. 6 shows an example of a list of berthing charges. As shown in the figure, for each ship listed in the ship list, a dredger code, a lift run (t / Day), and a desdemarate (dollar / day) are described. Lifting rate is the standard capacity for unloading and represents the amount that can be handled in one day. Compared to the case where it is assumed that the cargo can be lifted with the lifting capacity, when the actual lifting time is shortened, the amount set in the desdemaration rate can be received from the shipping company. On the contrary, if it becomes late, the amount set for the death demarcation will be paid to the shipping company. For example, let us consider a case where ETD 11 hours after ETA when a continuous cruise ship A unloads 10,000 tons. Since the lifting run is 20000 (t / Day), if it is unloaded at this rate, it will take 12 hours to unload. On the other hand, since it was actually unloaded in 11 hours, the amount of 1 hour = 16250/24 dollars of the amount stipulated by the death demarrate 16250 ($ / day) is received from the shipping company. On the other hand, when ETD is made 13 hours after ETA, one hour equal to 16250/24 dollars of the amount stipulated by desdemaration rate 16250 ($ / day) will be paid to the shipping company.

(2) Creation of ship financial resources (step S102)
The ship finance generation unit 102a of the macro optimization unit 102 selects a ship based on the ship list (see FIG. 3) taken in step S101, and creates a necessary ship fund.

FIG. 7 is a flowchart for explaining a vessel selection process. First, the ship fund generation unit 102a extracts a continuous sailing ship having an undetermined portion scheduled to be operated during the plan creation period based on the ship list (see FIG. 3) and the ship operation status (see FIG. 4) (step) S201). For example, assuming that the planning start date is March 1, 2008 and a ship allocation plan for three months is to be created, as shown in FIG. 4, the continuous cruise ship A is not yet determined after April 18, 2008. Therefore, the continuous cruise ship A is extracted.

Then, all patterns of combinations of loading and unloading points in the plan creation period are created for the extracted continuous cruise ship A (step S202). At this time, a specific condition may be provided based on the distance between the loading site and the landing site, and all patterns that satisfy this condition may be created. In this case, for example, a pattern having a clearly inappropriate driving distance can be excluded in advance, and the efficiency of the simulation can be increased. FIG. 8 shows the voyage No. already confirmed. 3 (“A-3” in the figure), the pattern of loading port X2-Yangu port A (voyage No. 4) and loading port X1-Yangu port B (voyage No. 5) is being created. FIG. In creating the pattern, each time is obtained using a standard voyage time (distance between ports and a standard knot of the ship A) and a standard loading time. For example, voyage no. The time of landing at Yacht A in No. 4 is [voyage no. 4 at the time of landing at the loading port X2] + [standard loading time] + ([distance between the port X2 and port A]) / [standard knot of the ship A]. Since there are a plurality of combinations of loading place and landing place in the plan creation period for the continuous cruise ship A, all of these patterns (or all the patterns that meet the above specific conditions) are created. The same operation will be performed for the other continuous cruise ships.

Next, based on the ship list (refer to FIG. 3) and the ship operation status (refer to FIG. 4), an irregular ship that can be used in the planning period and has an undetermined portion is extracted (step S203). For example, as shown in FIG. 3, since the scheduled allocation date of the irregular ship 5 is out of the plan creation period, the irregular ship 5 is not extracted. Then, as in the case of a continuous voyage ship, all patterns of combination of loading and unloading sites (or all patterns that meet specific conditions) are created for each extracted irregular ship in the plan creation period (step S204). .

Next, spot ship candidates are extracted based on the ship list (see FIG. 3) and the ship operation status (see FIG. 4) (step S205). Specifically, first, the total take-off target amount in the plan creation period is calculated. Further, the sum of the maximum loading capacity of the continuous voyage ship and the irregular ship extracted in steps S201 and S202 is calculated. As a result, the transport amount to be supplemented by the spot ship can be calculated by subtracting the sum of the maximum loading capacity of the continuous voyage ship and the irregular ship included in the plan creation period from the total take-up target quantity (FIG. 9). See). Based on the transport amount to be supplemented by this spot ship, the maximum load capacity of each spot ship is referred to calculate how many spot ships are required, and the minimum number of each spot ship is obtained. For example, if the transport amount to be supplemented by a spot ship is 250,000 ton, if Australia-PmaxSpot, 450,000 are required as 250,000 ÷ 75000 = 3.33, and four Australia-PmaxSpots are candidates for the spot ship. Similarly, 2 Australia-CapeSpot, 1 Australia-VLSpot, 4 Canada-PmaxSpot, 2 Canada-CapeSpot, 1 Canada-VLSpot, 1 Australia-PmaxSpot and 1 Australia- The minimum number of spot ships is obtained, such as CapeSpot. Here, the minimum number of spot ships obtained is the minimum number of ships when the take-up is supplemented only with the spot ship of the dredger code. As will be described later, more spot ships may be required than the minimum number of ships.

Next, spot ship candidates are extracted based on the ship list (see FIG. 3) and the ship operation status (see FIG. 4). Here, if there is a schedule determined by the ship operation status, the ship is extracted as a candidate for a spot ship, and further, the date set in advance for each charter code for which the contract classification of the ship list is not contracted. Each time a candidate for a spot ship is created for the plan creation period. FIG. 10 shows an example of a route list of spot ships with an interval for creating spot ship candidates for each charter code as 10 days.

Here, if the number of ships created at the same interval is smaller than the number of ships prepared at the same interval, and if the number of ships created at the same interval is smaller, all spot ship candidates were hired. Even so, it may be difficult to realize a take-up amount that satisfies the take-up target amount. Therefore, the spot ship candidates are created by narrowing the interval for creating spot ship candidates so that the number of ships is larger than the calculated minimum number of ships. Then, as in the case of a continuous voyage ship, all the patterns of combination of loading place and landing place in the plan creation period (or all patterns that match the specific conditions) are created for each created spot ship candidate (step S206). ). Here, in macro optimization to be described later, it is determined whether to hire or not to hire each of the spot ship candidates, and the necessary ship types and the number of ship spots are determined. For example, Australia-PmaxSpot-voyage No. After 3 is created as a candidate, it may be planned not to hire in macro optimization.

(3) Setting of a macro mathematical model (step S103)
The mathematical model setting unit 102b of the macro optimization unit 102 sets a mathematical model constructed so as to represent the ship operation restriction, the supply / demand balance restriction of raw materials at the landing, and the take-up target quantity restriction created in step S102. The mathematical model to be set is constructed (formulated) as a model according to mathematical programming such as LP (linear programming), MIP (mixed integer programming), QP (quadratic programming), and the like. Here, as an example, a mathematical model based on the MIP formulation is shown.
Here, the setting of the mathematical model refers to the maximum number of subscripts in each array (for example, the number of ships) for the basic mathematical model that is constructed in an abstract format so that it can cope with changes in the number of ships, the number of ports, etc. ) And the coefficient values in the formula are specifically determined according to the actual plan.

First, define a variable that indicates whether the ship selects or does not select the corresponding port. That is, based on the value of this variable obtained by optimization described later, it is determined whether or not to select the loading port. This variable is an integer variable that takes one of the values 1 for selecting and 0 for not selecting.

For example, the continuous cruise ship A shown in FIG. 4 (“A-4” in the figure), if there are two loading ports X1 and X2 where the ship can call, define the following two integer variables to correspond to each loading port . Here, ETA, which is the third subscript of these integer variables, is the offshore time calculated in step S102.

If it is chosen to stop at X1 as a result of optimization, the variable will take the following values:

In addition, if the corresponding ship stops at the corresponding loading port, the corresponding lifting port, the corresponding calling order (number indicating the number of the landing port, for example, loading port X1-lifting port A-lifting port B, B is called in order of calling 2), that is, after stopping at the corresponding port, whether to stop at the landing port, or not selecting, that is, after stopping at the loading port, Defines a variable that indicates whether or not to stop in the order of arrival. This variable is an integer variable that takes a value of 1 indicating that the port is calling and 0 indicating that the port is not calling. In the example dealt with here, an example in which a maximum of two landing ports can be visited is presented. However, the number of landing ports that can be visited and the number of loading ports that can be called may take on more values.

Also, define a variable that indicates the amount that the relevant ship will load the relevant brand at the relevant loading port.

In addition, a variable is defined that indicates the amount that the relevant ship unloads the relevant brand in the relevant loading port, relevant discharge port, and appropriate calling order.

Furthermore, a variable indicating the stock quantity at the relevant discharge port of the relevant brand on the relevant day is defined.

Constraint conditions indicating conditions such as “the load capacity of each ship does not exceed the maximum load capacity”, “the load capacity must be completely unloaded”, etc. are constructed in advance as a basic mathematical model. As will be described later, a series of steps including optimization (steps S103 to S106) and simulation (S107) can be executed by repeating a plurality of loops. In the optimization of the initial loop, the ship operation restrictions are set in the mathematical model based on the data captured in step S101. In optimization after the second loop, the mathematical model is set by reflecting the result of the simulation performed by the simulator 101 in the previous loop.

The constraint that the load capacity of each ship does not exceed the maximum load capacity is expressed by the following constraint expression (Formula 1).

The constraint that the entire loading capacity is unloaded is expressed by the following constraint equation (Equation 2).

Also, as a constraint on the supply and demand balance of raw materials at the landing site, a constraint condition that “the stock amount of each brand is always secured more than the safety stock amount” is constructed as a mathematical model. In the initial loop, based on the data fetched in step S101, the simulation results in the simulator 101 are reflected in the subsequent loops (steps S103 to S107) and thereafter, and the constructed mathematical model is set as shown in FIG.

First, the constraint equation representing the transition of the stock amount of each brand is expressed as (Equation 3) below. That is, a value obtained by subtracting the inventory amount on the previous day and the amount unloaded on the current day from the inventory amount on the current day is the scheduled use amount on the current day.

The constraint that the stock quantity of each brand is always secured above the safety stock is expressed by the following constraint equation (Formula 4).

In addition, the raw materials unloaded at the unloading port are piled up in the yard, but the stock of the unloaded raw materials cannot be docked unless the yard capacity is below the upper limit. However, the raw material stacked in the yard can be unloaded if the date has passed, that is, if the ship is kept waiting. However, if unloading exceeds the yard capacity, the berthing time will be enormous. Thus, for example, it is assumed that unloading is permitted up to an excess of about 1% of the yard capacity. This constraint is expressed by the following constraint equation (Equation 5).

Also, the take-off target amount constraint is set based on the data fetched in step S101, reflecting the simulation result in the simulator 101 after the next loop (steps S103 to S107). The formula model is such that the take-up amount (loading amount) to be optimized is not far from the take-up target amount, and whether or not it can be picked up (as mentioned above, there may be circumstances where a predetermined brand is not picked up for a predetermined period). Has been built. Here, when considering the restriction that the pick-up amount is not far from the pick-up target amount, for example, as shown in FIG. 12A, the upper and lower limit values are simply set for the pick-up target amount every season (or every month), It can be considered that the product amount does not exceed the upper and lower limit values. However, in that case, for example, when the load amount satisfies the lower limit value but continues to be lower than the take-up target amount, the take-up crack may occur if accumulated for the year. Therefore, as shown in FIG. 12B, taking into account the collection target amount accumulation and the collection amount accumulation every season (or every month), the difference between the collection target amount accumulation and the collection amount accumulation is reduced (minimized, upper It is preferable to set a constraint such that the lower limit value is not exceeded. In order to formulate the above constraint equation, variables of overflow amount and deficiency amount from the target collection amount every season are defined.

Also, define a variable for the accumulated amount of each season.

First, the constraint equation that represents the cumulative amount received for each issue is expressed as (Equation 6) below. That is, the accumulated amount of collection is the total amount of unloading of a ship (voyage) in which an ETA is entered during the period from the planning start date to the relevant season.

∙ The constraint equation that expresses the relationship between the accumulation of the target amount for each brand, the overflow amount, and the shortage amount is expressed as (Equation 7) below. That is, if the overflow amount is generated from the take-up cumulative amount, the overflow amount is subtracted, and if the shortage has occurred, the shortage amount is added to match the take-up target cumulative amount. Here, it can be said that the closer the take-up cumulative amount and the take-up target cumulative amount are, the better the plan. That is, the smaller the overflow amount and the insufficient amount, the better. For the above reason, as described later, the overflow amount and the shortage amount are added as items of the objective function, and are minimized.

Here, in order to formulate the minimum of the total amount of freight as an objective function, an integer variable indicating the order of port calls is introduced. This port-calling variable is 1 when a specific ship selects a combination of a specific loading port as a loading port, a corresponding unloading port 1 as a first unloading port, and a specific unloading port 2 as a second unloading port. Take 0 if you don't select.

The method of describing this logical relationship as a mixed integer programming formula is generally well known and can be formulated as follows.

(4) Optimization based on the macro mathematical model and the objective function (step S104)
The optimization calculation unit 102c of the macro optimization unit 102 performs the optimization calculation based on the objective function (evaluation function) set for the transportation cost, using the mathematical model set in step S103. In the optimization calculation, the problem is solved as an optimization problem by mathematical programming such as LP (Linear Programming), MIP (Mixed Integer Programming), and QP (Secondary Programming).

In the optimization calculation here, the following variables are determined using an objective function for the purpose of minimizing the total amount of freight in the transportation cost. As a result, the hull type, the number of ships, the landing site (shipping port), the loading brand, and the amount of loading are selected to make the total amount of freight the cheapest.

Here, the freight applied to the ship is the sum of the standard freight from the loading port to the landing port calling at the first port and the multi-port additional freight applied when calling to another port as described above. Then, it is the product of the total of the multi-port lift additional freight that occurs when an extra call is made from the first port to the second port, and the amount loaded.

For example, from FIG. When 1 carries 75,000 tons of cargo from X1 port to the first landing port A, the standard freight is 16.00, and the hiring cost is 16.00 × 75000 = 1,200,000. In addition, after stopping at A as the second port, according to B, the multi-port lift additional freight was (16.24-16.00) = 0.24, and the hiring cost at this time was (16.00 + 0.24). ) × 75000 = 1,218,000.

From the above, when the objective function used in macro optimization (hereinafter referred to as the macro objective function) is expressed by an expression, the following expression (Expression 11) is obtained.

Here, the macro optimization is intended to reduce the difference between the take-up target amount accumulation and the take-up amount accumulation in consideration of the take-up target amount accumulation and the take-up amount accumulation. For this reason, an item for minimizing the total amount of overflow and deficiency from the seasonal collection target cumulative amount is added to the objective function. Therefore, (Expression 11) representing the objective function is changed to the following expression (Expression 12).
Macro optimization optimizes problems related to dredgers as a whole.

Although it has been explained that the objective function is constructed for freight, the objective function may be used for the purpose of minimizing the total amount of freight and the total amount of raw material purchase costs. As described above, the target amount of raw materials is set by contract, and there is no significant change in the purchase cost of raw materials, but among them, the total purchase cost of raw materials can be minimized.

As explained in the above items (3) and (4), the formula to be minimized is formulated as an objective function, and each formula to be satisfied is formulated as a constraint formula. This constraint equation is expressed as a linear equation or an inequality. The objective function is expressed by a linear expression. A mathematical model and an objective function are constructed as models in which there are variables that should be integers. The problem formulated in this way is generally well known as a mixed integer programming problem, and this problem can be (analytical) optimized.

* In macro optimization, time accuracy is calculated as seasonal accuracy. The optimization period is 9 in the first loop (steps S103 to S107), 8 in the next loop (steps S103 to S107), and so on in the last loop (steps S103 to S107). Then, the first January of the optimization period (9th to January) is set as the plan finalization period, and the calculation result in the plan finalization period is output to the micro optimization unit 103.

(5) Setting of a micro mathematical model (step S105)
The mathematical model setting unit 103a of the micro-optimization unit 103 is a stagnation constraint and a raw material at a landing site among the constraints when operating a ship according to the ship allocation plan of the plan confirmation period obtained by the macro optimization unit 102. A mathematical model representing the supply-demand balance constraint is set. The mathematical model used is constructed using mathematical programming such as LP (Linear Programming), MIP (Mixed Integer Programming), and QP (Secondary Programming). Here, as an example, a mathematical model based on the MIP formulation is shown.

揚 The port of call is determined by macro optimization. Here, since there are a plurality of berths (quay walls) for ships to berth at the landing port, a variable for selecting which berth of the corresponding landing port is to be defined is defined. This variable is an integer variable that takes a value of 1 if the corresponding berth is selected, and 0 if it is not selected.

Also, define a variable for the time (ETA) at which the ship will start offshore in order to arrive at the berth. Since the time cannot be directly defined as a variable for formulation by MIP, it is defined as the elapsed time from the planning start date. That is, when the planning start date is 0:00 on January 1, and the ETA is 1:10 on January 1, it is defined as taking 70. Also, this variable is not an integer variable, but defined as a variable that takes a continuous value.

Similarly, define ETB variables.

Similarly define ETD variables.

Furthermore, the variable of the stock quantity at the corresponding discharge port of the corresponding brand for the corresponding part is defined.

Vessel stagnation restrictions are set based on the data fetched in step S101 in the first loop, and further reflecting the simulation results in the simulator 101 after the next loop (steps S103 to S107). Vessel operating conditions (ETB> ETA, ETD> ETB + unloading time, etc.), berth conditions (allowable LOA (full length), DRAFT (full depth), BEAM (full width), loading capacity, yard capacity, etc.) ), Etc. are built into the mathematical model.

船舶 Vessels that have been determined to be unloaded by macro optimization need to berth at one of the berths at that port. This constraint is expressed as the following constraint equation (Equation 13). In other words, one berth must be selected (variable value is 1) among the berths that can be docked for the ship.

ETB needs to be after ETA. This constraint is expressed as the following constraint equation (Equation 14).

ETD needs to be after ETB + unloading time. Here, since the amount of unloading at the berth is determined by macro optimization, the unloading time at the berth can be expressed as unloading time = unloading amount / unloading capacity using the standard unloading capacity at the berth. Become. From the above, the above constraint is expressed as the following constraint equation (Formula 15).

Further, the supply and demand balance of raw materials at the landing site is set based on the data taken in step S101, and the simulation results in the simulator 101 are reflected after the next loop (steps S103 to S107). As shown in FIG. 11, it is constructed as a mathematical model that the stock amount of each brand is always secured at least the safe stock amount. That is, the value obtained by subtracting the inventory amount one minute before the amount unloaded at the time and the amount unloaded at the time becomes the scheduled use amount for the time 1 minute.

In addition, the raw materials unloaded at the unloading port are piled up in the yard, but the stock of the unloaded raw materials cannot be docked unless the yard capacity is below the upper limit. In other words, the inventory amount at the time of ETB needs to be below the upper limit of yard capacity. This constraint is expressed as the following constraint equation (Equation 17).

(6) Optimization based on the micro mathematical model and the objective function (step S106)
The optimization calculation unit 103b of the micro optimization unit 103 performs optimization calculation based on the objective function (evaluation function) constructed for the transportation cost, using the mathematical model set in step S105. In the optimization calculation, the problem is solved as an optimization problem by mathematical programming such as LP (Linear Programming), MIP (Mixed Integer Programming), and QP (Secondary Programming).

In the optimization calculation here, an objective function for the purpose of minimizing the total amount of the berthing fee is used, δ (ship, berth) indicating whether or not the ship berths, and ETA (ship, berth) indicating the ETA time. ), ETB representing the ETB time (ship, berth), and ETD representing the ETD time (ship, berth). As a result, the berth and entry / exit timing for the lowest transportation cost is selected.

Here, the berthing charge on the ship is compared to the ETD-ETA and the contracted standard berth time. If the berth is longer than the standard berth time, that is, if ETD-ETA> the standard berth time, You pay the contracted costs, and in the opposite case, you receive the contracted costs as a death demarcation. The standard berthing time is calculated in the lift / lift run using the lift run, which is the capacity set in the contract. For example, continuous cruise ship A-voyage No. When 1 unloads 10,000 tons at the port and it takes 11 hours from ETA to ETD, the standard berth time = 10000/20000 = 0.5 days, which is 1 hour earlier than 12:00 hours. / 24 amount will be received from the shipping company. From the above, when the objective function used in the micro optimization (hereinafter referred to as the micro objective function) is expressed by an equation, the following equation (Equation 18) is obtained.

Although the constant part is included in the above formula, the constant part does not affect the minimum, so the following formula (Formula 19) is the objective function.

As explained in the above items (5) and (6), the formula to be minimized is formulated as an objective function, and each formula to be satisfied is formulated as a constraint formula. This constraint equation is expressed by a linear equation or an inequality. The objective function is expressed by a linear expression. A mathematical model and an objective function are constructed as models in which there are variables that should be integers. The problem formulated in this way is generally well known as a mixed integer programming problem, and this problem can be (analytical) optimized.

In the micro optimization, the optimization period is 10 days (1st) and the time accuracy is calculated as minute accuracy.

(7) Simulation (step S107)
The simulator 101 executes a simulation based on the solution for the mathematical model obtained by the micro optimization unit 103, and finalizes the ship allocation plan for the plan finalization period (in the first season). The time accuracy of the simulation is minute accuracy. In this simulation, it is possible to create a ship allocation plan that takes into account even the fine constraints actually required by incorporating constraints that could not be incorporated into the macro mathematical model and the micro mathematical model.

For example, one example of constraints that are difficult to handle with macro / micro optimization is the number of unloaders used to unload a single ship. This radix varies depending on the position of the hatch where the brand to be unloaded is loaded, whether or not there is a ship handling at another berth at the same port. The unloading capacity varies depending on the number of unloaders used for unloading. For example, a situation such as “when unloading with one unit is lifted with a capacity of 100% at 1500 t / h” and “when two units are unwound with a capacity of 70% with 1500 t / h × 2 units” can be exemplified. In macro / micro optimization, these unloader radixes are not taken into consideration, so the time calculated by optimization is taken into consideration by the simulator up to the unloader radix, and the time lag of the optimization is taken into the simulation and accurately simulated. This makes it possible to create production and logistics plans that take into account the fine constraints required for actual operations.

In the simulator 101, if the micro-optimization unit 103 adjusts the time by changing the timing of entering / leaving the ship, the time is corrected by reflecting it in a spillover manner. In particular, in the case of a continuous voyage ship, if time adjustment is made at a certain port, it will affect the subsequent voyage, so the time will be corrected by the simulator 101 and reflected in the processing by the macro optimization unit 102 thereafter. ing.

(8) Planning start date update (step S109)
In step S108, it is determined whether or not plans for the plan creation period (3 months (9 months)) have been finalized. If it has not been confirmed yet, the first day of the next season in which the plan is confirmed, for example, if the plan for N season is confirmed, the first day of N + 1 season is updated as the plan update date (step S109), and the process returns to step S103. In the next loop starting from step S103, the inventory transition in the season (N season) when the plan is finalized and the operational status of the ship are updated to finalize the plan for the next season (N + 1 season). By repeating this, the plan for the plan creation period (3 months) is fixed (see FIG. 13).

(9) Shipment plan output (step S110)
The ship allocation plan created as described above is displayed on a screen (not shown) by the output unit 105 or transmitted to an external device.

As described above, the macro optimization unit 102 and the micro optimization unit 103 first set a mathematical model based on the initial conditions, perform optimization calculation, and calculate an instruction for the simulator 101. When the simulator 101 completes the simulation for the plan finalization period (in the first season), the macro optimization unit 102 and the micro optimization unit 103 provide information on changes in the stock of raw materials in the final state of the plan finalization period and the ship operation status. To give. The macro optimization unit 102 and the micro optimization unit 103 set a mathematical model based on the given information, perform optimization calculation, and calculate an instruction for the simulator. In this way, by linking the simulator 101 and the optimization units 102 and 103, it is possible to create a ship allocation plan for the plan creation period (3 months (9th September)).

According to the ship allocation plan creation apparatus (method) according to the present embodiment, the simulator 101 (the inventory transition simulator, the inventory transition simulator, the calculation instruction based on the result of the optimization calculation performed by the macro optimization unit 102 and the micro optimization unit 103). (Ship operation status transition simulator). As described above, since the simulation is performed based on the result of the optimization calculation, it is possible to surely obtain a theoretical optimum solution. Thereby, it is not necessary to evaluate the simulation result and repeat the simulation many times as in the prior art, and the simulation result can be created quickly and with high accuracy.

Even if the scale of the simulator 101 is very large or the constraint conditions are very large and complicated, only the important part of the constraints described in the simulator 101 that has a large influence on the creation of the ship allocation plan is expressed by the mathematical model. In this case, it is possible to perform the optimization calculation within a practical time by setting the scales of the mathematical model setting units 102b and 103a to an appropriate range. Since all the restrictions to be considered can be described in the simulator 101, it is guaranteed that a ship allocation plan created by executing one simulation can be actually executed.

In addition, when preparing a ship allocation plan, brands transported from a distant place such as Brazil may be received only once every two or three months. is there. On the other hand, there are brands that are transported in a few days among brands that are frequently transported, such as China. Furthermore, management of berths is subject to berthing charges and is performed on a minute-by-minute basis, so a plan with minute accuracy is required. In response to these requirements, the macro optimization unit 102 selects the ship type, the number of ships, the landing site (shipping port), the loading brand, the amount of loading, and the port order, while the micro optimization unit 103 uses the berth used. The calculation was divided to select the entry / exit timing. For this reason, the calculation load can be suppressed and the solution can be obtained with high accuracy. In other words, by coordinating macro optimization and micro optimization and repeatedly executing them, we can consider usable ships, landing sites, brands, and quantities that need to be specified over a long period (three months) at the same time. The use berth and the entry / exit timing that require fine time accuracy can be optimized with fine time accuracy. *

In the embodiment described above, each brand is handled as an individual one, but a plurality of brands with similar properties (brands having a certain chemical property in common: brands that can be used even if they are replaced with each other) are grouped. May be handled. In actual operation, if a brand with a property close to the brand that was originally intended for use is transported, the brand that was transported as an alternative may be used instead of the brand that was originally used. Therefore, the above handling can be performed. By grouping brands in this way and treating them as one, it is possible to reduce the number of variables and the amount of calculation. In addition, by grouping, it becomes possible to transport brands that can be transported only by ships having a high freight rate, and brands that can be arranged by cheaper freight ships of the same group, thereby reducing transportation costs.
In this case, the constraint that the stock amount of each brand represented by (Equation 4) in macro optimization is always secured to the safety stock or more is changed to the following constraint equation (Equation 20).

... (Formula 20)
Also in the micro optimization, the safety stock constraint equation is changed so that the total stock of brands in the same group (group brands) satisfies the safety stock. In this case, the constraint that the stock amount of each group brand is always secured at or above the safety stock is expressed by the following constraint equation (Formula 21).

.... (Formula 21)

In addition, when preparing a ship allocation plan, the user may be able to individually fix the ship type, the number of ships, the landing site (shipping port), the loading brand, and the loading amount. In this case, for example, it is possible to cope with a situation where a predetermined ship is used or a predetermined loading port is used in advance. In particular, after drafting a ship allocation plan based on the amount set as the target amount for collection, negotiations with Yamamoto will proceed and the amount will be finalized. The brand name and volume (loading volume) cannot be changed due to contractual reasons. However, with regard to the landing site, there is often a room for changing the landing site, the brand name, and the lifting amount after judging the stock status. For this reason, if operation which can fix a loading place (loading port), a loading brand, and a loading amount collectively is enabled, it will become convenient for a user.

(Second Embodiment)
In the above-described embodiment, the example in which the optimization calculation is performed based on the objective function (evaluation function) constructed with respect to the transportation cost or the like by the optimization calculation unit 102c of the macro optimization unit 102 has been described. May be added.

For example, in order to level the load at the loading area, avoid the concentration of ships entering and leaving the same loading area at the same time, or conversely, the period when the ships do not enter or leave the port, that is, the same loading area. Therefore, it is required to allocate ships as evenly as possible during the planning period.

Therefore, as shown in FIG. 14, the collected amounts of all the brands are summed in seasonal units (or monthly units) for each loading place, and the accumulation up to that point is considered (collected amount accumulation). In addition, the collection target amount of all brands is summed in seasonal units (or monthly units) for each loading place, and the accumulation up to that time is set as a target value (collection target amount accumulation). Then, an objective function is constructed for the purpose of minimizing the difference between the accumulated amount of collected items and the accumulated amount of collected items.

This will allow the volume of loading at the season to approach each other evenly (or each month), in other words, evenly distribute ships.

Similarly, in order to level the load at the landing site, it is avoided that the vessels entering and leaving the same landing site are concentrated at the same time, or conversely, the period when the vessels do not enter and leave the port continues. At the landing site, it is required to distribute ships as evenly as possible during the planning period.

Therefore, as shown in FIG. 15, the amount of unloading of all brands is summed in seasonal units (or monthly units) for each landing site, and the accumulation up to that point is considered (unloading amount accumulation). In addition, the standard unloading capacity for each landing site is summed in seasonal units (or monthly units), and the accumulation up to that time is set as a target value (cumulative standard unloading capacity accumulation). Then, the difference is defined as the remaining unloading amount, and an objective function for the purpose of minimizing the remaining unloading amount is constructed.

This will allow the unloading capacity of the landing site to approach evenly in each season (or each month), in other words, evenly ship. That is, even in macro optimization, it becomes possible to suppress a stagnation.

FIG. 16A shows a ship planning result planned by a skilled artisan by a conventional method. In this figure, a heavy vessel awaits a ship when the first ship is scheduled to berth at a particular berth at a particular port at a particular time. Occurs when anchored at the berth. When a heavy ship is awaited, the first ship needs to stay at the offshore of the loading port until the second ship leaves the port, that is, until the ETD of the second ship.
In addition, when the first ship is scheduled to berth at a specific berth at a specific unloading port at a specific time, the stock of raw materials on the yard will have a yard capacity. Occurs when it exceeds the limit and cannot be handled. When a yard waiting ship occurs, the first ship needs to stay in the offshore port until the yard capacity is available.
In ship assignment planning, the following situation may occur before a specific ship arrives off the port at the ETA and leaves the port at the ETD.
(1) Arrival, no stagnation, cargo handling, departure from port (2) Arrival, heavy vessel lagging ship, cargo handling, departure from port (3) Arrival, yard lagging vessel, cargo handling, departure from port (4) Arrival, heavy vessel lagging ship and According to FIG. 16A, according to the result of the ship allocation plan planned by the conventional method, a heavy ship awaiting ship and a yard waiting ship are generated at a certain rate. In order to eliminate such a stagnation, it is necessary to repeat the plan correction work by a planner having skill. In addition, it is very difficult for a skilled planner to accurately estimate how much a berthing can be solved in principle.
On the other hand, FIG. 16B shows a ship assignment planning result planned using the ship assignment plan creating apparatus and method according to the present embodiment. In FIG. 16B, compared with FIG. 16A, most of the heavy ship waiting boat and the yard waiting boat are eliminated. As a result, in addition to the effect of reducing excess costs related to stagnation, stable ship assignment planning is possible without depending directly on the skill of the planner.

The ship allocation plan creation apparatus of the present invention can be specifically configured by a computer system including a CPU, a ROM, a RAM, and the like, and is realized by the CPU executing a program. Moreover, the ship allocation plan creation apparatus of this invention may be comprised from one apparatus, or may be comprised from several apparatus.

The object of the present invention can also be achieved by supplying a storage medium storing software program codes for realizing the functions of the above-described embodiments to a system or apparatus. In this case, the computer (or CPU or MPU) of the system or apparatus reads and executes the program code stored in the storage medium. In this case, the program code itself read from the storage medium realizes the functions of the above-described embodiments, and the program code itself and the storage medium storing the program code constitute the present invention. As a storage medium for supplying the program code, for example, a flexible disk, a hard disk, an optical disk, a magneto-optical disk, a CD-ROM, a CD-R, a magnetic tape, a nonvolatile memory card, a ROM, or the like can be used.

According to the present invention, it is possible to create a ship allocation plan for transporting a plurality of brands of raw materials from a plurality of loading sites to a plurality of landing sites quickly and with high accuracy. In addition, it is optimal for minimizing transportation costs, including the types of ship contracts (continuous voyage ships, irregular ships, spot ships), and the fleet structure (ship funding) with the decision to hire / not hire each ship. Can be realized.
Furthermore, by considering brands with similar properties, it is possible to further optimize out-of-stock control and minimizing transportation costs compared to considering individual brands individually.

DESCRIPTION OF SYMBOLS 100: Ship allocation plan preparation apparatus 101: Simulator 102: Macro optimization part 102a: Ship financial resource preparation part 102b: Formula model setting part 102c: Optimization calculation part 103: Micro optimization part 103a: Formula model setting part 103b: Optimization Calculation unit 104: Data acquisition unit 105: Output unit 200: Formulation plan creation device 300: Database 400: Host computer

Claims (16)

1. A ship allocation plan creation device for creating a ship allocation plan for transporting raw materials of multiple brands from a plurality of loading sites to a plurality of landing sites,
   A planned use amount of the raw material, a take-up target amount of the raw material, an inventory status of the raw material, a purchase cost of the raw material, a ship list listing a plurality of ships operated based on a plurality of types of chartering contracts, respectively Data fetching means for fetching data including the ship operating status of the ship and the transportation cost when using each ship;
   Ship financial resource creation means for selecting a necessary ship from the ship list based on the ship operational status and creating a ship financial resource;
   A mathematical model setting means for setting a mathematical model that represents at least the operational restrictions of the ship included in the financial resources of the ship and the supply and demand balance restrictions of the raw materials at the landing site;
   An optimization calculation means for performing optimization calculation based on at least an objective function constructed with respect to the transportation cost, using the set mathematical model;
   A simulator including an inventory transition simulator that operates based on a result of the optimization calculation and that simulates the transition of the inventory status; and a ship operation status transition simulator that simulates the transition of the vessel operation status;
   Output means for outputting a ship assignment plan which is a simulation result by the simulator;
   A ship allocation plan creation device characterized by comprising:
2. The ship allocation plan creation device according to claim 1, wherein when the raw materials are handled, a plurality of brands of the raw materials included in a range defined by chemical properties are grouped.
3. 2. The ship allocation plan creating apparatus according to claim 1, wherein the mathematical model setting means further sets a mathematical model that represents a target amount restriction of the raw material.
4. The ship allocation plan creation device according to claim 1, wherein the chartering contract type of the ship included in the ship list includes a continuous voyage ship, an irregular ship, and a spot ship.
5. The ship funding creation means extracts the continuous voyage ship that has an undetermined portion of operation in a plan creation period based on the ship list and the ship operation status, and for each extracted continuous voyage ship, the plan creation period 5. The ship allocation plan creation device according to claim 4, wherein all the patterns that match a predetermined condition are created among the combination patterns of the loading place and the landing place.
6. The ship financial source creation means extracts the irregular ship that can be used in the plan creation period and has an undecided part of operation based on the ship list and the ship operation status, and the extracted irregular ship The ship allocation plan creation device according to claim 5, wherein all of the combinations of the loading place and the landing place in the plan creation period that meet a predetermined condition are created.
7. The ship fund generation means is transported by the spot ship on the basis of the total sum of the take-up target quantities in the plan preparation period and the total of the maximum loading capacity of the extracted continuous cruise ship and the irregular ship. The ship allocation plan creation device according to claim 6, wherein an amount of the raw material to be calculated is calculated, and the spot ship candidates are extracted based on the ship list.
8. Setting the mathematical model with a predetermined macro time accuracy within a preset optimization period, performing the optimization calculation, and outputting a calculation result in a part of the plan confirmation period of the optimization period, A macro optimization unit comprising the mathematical model setting means and the optimization calculation means;
   Using the calculation result in the plan decision period obtained by the macro optimization unit, setting the mathematical model with micro time accuracy finer than the macro time accuracy in the plan decision period, and performing the optimization calculation 8. A micro-optimization unit comprising a mathematical model setting unit and an optimization calculation unit that performs and delivers the result of the optimization calculation to the simulator. 8. Ship allocation plan creation device described in 1.
9. When a change occurs in the operation time of one voyage of the continuous voyage ship, the simulator corrects the operation time of subsequent voyages of the continuous voyage ship by reflecting the change spilloverly. 8. The ship allocation plan creation device according to claim 1, wherein processing by the mathematical model setting unit and the optimization calculation unit is performed based on the processing.
10. The optimization calculation means is:
    For each of the loading points, the accumulated amount of the brand is calculated by summing up the accumulated amounts of all the brands in a seasonal unit or a monthly unit, thereby calculating the accumulated amount of the collected items;
    For each loading place, the collection target amount accumulation is calculated by totaling and accumulating the collection target amount of all the brands in a seasonal unit or a monthly unit;
    Performing the optimization calculation on the basis of an objective function for further minimizing the difference between the collected amount of picked up and the accumulated amount of picked up target;
    The ship allocation plan creation device according to any one of claims 1 to 7.
11. The optimization calculation means is:
    For each said landing, calculate the accumulated amount of unloading by totaling and accumulating the amount of unloading of all the brands in seasonal units or monthly units;
    For each landing site, the standard unloading capacity amount is calculated by summing up and accumulating the standard unloading capacity amount in seasonal units or monthly units;
    Performing the optimization calculation based on an objective function for minimizing the difference between the unloading accumulation and the standard landing capacity unloading;
    The ship allocation plan creation device according to any one of claims 1 to 7.
12. 2. The apparatus according to claim 1, further comprising an input unit that makes it possible to individually fix a ship type, a number of ships, a loading place, a landing place, a loading brand, a lifting brand, a loading quantity, and a lifting quantity according to a user's intention. 8. The ship allocation plan creation device according to any one of items 7.
13. The ship allocation plan creation according to any one of claims 1 to 7, further comprising an input unit that makes it possible to fix the loading place, the brand name, and the loading amount collectively according to a user's intention. apparatus.
14. 8. The ship allocation plan creation device according to any one of claims 1 to 7, wherein the transportation cost includes a freight rate and a berthing fee.
15. A ship allocation plan creation method for creating a ship allocation plan for transporting raw materials of multiple brands from a plurality of loading sites to a plurality of landing sites,
    The data fetching means lists the planned usage amount of the raw material, the target collection amount of the raw material, the stock status of the raw material, the purchase cost of the raw material, and a plurality of vessels operated based on a plurality of types of chartering contracts. Fetching data including a ship list, a ship operation status of each ship, and a transportation cost when using each ship;
    A step of selecting a ship from the ship list based on the ship operation status and generating a ship financial resource by a ship financial resource creating means;
    Setting a mathematical model representing at least the operational restrictions of the ship included in the ship's financial resources and the supply and demand balance restrictions of the raw materials at the landing site by means of mathematical model setting means;
    A step of performing an optimization calculation based on an objective function constructed at least with respect to the transportation cost using the set mathematical model by an optimization calculation means;
    Simulating the inventory status and the vessel operation status based on the result of the optimization calculation by a simulator;
    Outputting a ship allocation plan as a simulation result by the simulator by an output means;
    A ship allocation plan creation method characterized by comprising:
16. A program for causing a computer to execute a process for creating a ship allocation plan, the program causing the computer to function as the ship allocation plan creation device according to claim 1.

Images