JP5037927B2 - Information processing apparatus, information processing method, program, and recording medium - Google Patents

Description

  The present invention relates to a technology related to delivery.

  Japanese Patent Application Laid-Open No. 2004-151867 discloses a “package transport management system” for managing the loading efficiency of trucks used for shipping products in a front warehouse where products are replenished from a factory to a store.

According to such a document, the demand amount of the baggage after one to two weeks is predicted based on the “monthly index” or “seasonal index” based on the sales (demand) results for the past year and the latest demand results. . Then, based on this prediction, a shipping loading pattern that minimizes the number of trucks when shipping the package from the factory is determined.
JP 2005-194073 A

  However, just because the number of vehicles is minimized by the conventional technology, there is a problem that the overall cost is not always the lowest.

  There are monthly contracts, daily contracts, and the like as contract forms for procuring necessary vehicles. In some cases, an annual contract or a company-owned contract is used, but for convenience of explanation, it is assumed here that procurement is made in two patterns: a monthly contract and a daily contract.

  In general, monthly contracts are cheaper than day contracts. However, the transportation and delivery plan described in Patent Document 1 considering only the minimum number does not show an ideal distribution between the monthly contract and the daily contract. For this reason, such a contract cannot but depend on the experience and intuition of a contract person, and it is not necessarily the cheapest cost, but becomes a burden of a contract person.

  Alternatively, for example, even if the minimum number of vehicles is simply set as a monthly contract and the required number of vehicles more than that is set as a daily contract based on the required vehicle forecast for one month, the cost may not be the cheapest.

  The present invention has been made in view of the above problems, and reduces the delivery cost when delivering a package to each delivery destination, and reduces the burden of shipping and receiving work by leveling, thereby reducing the labor cost. It is an object of the present invention to provide a technique for achieving reduction and making it easier to plan such delivery.

  In order to achieve the object of the present invention, for example, an information processing apparatus of the present invention comprises the following arrangement.

That is, an information processing apparatus that performs a procurement plan for vehicles necessary for delivering a package to a delivery destination,
Procuring a vehicle with a first unit period contract, which is a unit period constituting the first unit period, and first data indicating a cost required for procuring the vehicle with a contract of the first unit period Storage means for storing second data indicating an expense required for
In order to deliver an estimated amount of luggage, which is estimated to be delivered to a delivery destination for each second unit period, to the delivery destination, the number of vehicles required for each second unit period is determined as a routing plan. A first calculating means obtained by:
Setting means for setting a first number that is the number of vehicles procured in the contract of the first unit period;
The number of vehicles to be procured in the contract for the second unit period using the number of vehicles required for each second unit period determined by the routing plan and the first number. A second calculating means for obtaining the number of
Using the cost indicated by the first data and the first number, the cost required to procure the first number of vehicles under a contract for the first unit period is obtained, and the second data is Expenses required to procure the second number of vehicles in the contract for the second unit period using the indicated expenses and the second number for each second unit period, and the obtained expenses A third calculating means for calculating the sum of the above as the total cost required for the procurement of the vehicle;
The setting means sets a different first number, and the second calculation means and the third calculation means calculate a total cost that is the smallest of the total costs determined for the different first numbers. And specifying means for specifying the first number for the specified total cost as the number of vehicles procured by the contract of the first unit period ,
The first calculation means can pay out the vehicle at each delivery destination, time zone information indicating an acceptable time zone of the vehicle and information indicating the number of vehicles acceptable in the time zone, or at each delivery source. The routing plan is performed so as to satisfy at least one of the time zone information indicating a specific time zone and the information indicating the number of vehicles payable in the time zone .

  In order to achieve the object of the present invention, for example, an information processing method of the present invention comprises the following arrangement.

That is, an information processing method performed by an information processing apparatus that performs a procurement plan for vehicles necessary for delivering a package to a delivery destination,
Procuring a vehicle with a first unit period contract, which is a unit period constituting the first unit period, and first data indicating a cost required for procuring the vehicle with a contract of the first unit period A storage step of storing in the storage means second data indicating an expense required for storage;
In order to deliver an estimated amount of luggage, which is estimated to be delivered to a delivery destination for each second unit period, to the delivery destination, the number of vehicles required for each second unit period is determined as a routing plan. A first calculation step determined by:
A setting step of setting a first number that is the number of vehicles procured by the contract of the first unit period;
The number of vehicles to be procured in the contract for the second unit period using the number of vehicles required for each second unit period determined by the routing plan and the first number. A second calculation step for obtaining the number of
Using the cost indicated by the first data and the first number, the cost required to procure the first number of vehicles under a contract for the first unit period is obtained, and the second data is Expenses required to procure the second number of vehicles in the contract for the second unit period using the indicated expenses and the second number for each second unit period, and the obtained expenses The third calculation step to calculate the sum of the above as the total cost required to procure the vehicle,
A different first number is set in the setting step, and a minimum total cost among the total costs obtained for the different first number in the second calculation step and the third calculation step is determined. A specific step of identifying and identifying a first number for the identified total cost as the number of vehicles procured in the contract of the first unit period ,
In the first calculation step, time zone information indicating an acceptable time zone of the vehicle at each delivery destination and information indicating the number of vehicles acceptable in the time zone, or the vehicle can be paid out at each delivery source The routing plan is performed so as to satisfy at least one of the time zone information indicating a specific time zone and the information indicating the number of vehicles payable in the time zone .

  According to the configuration of the present invention, a vehicle procurement plan for reducing the delivery cost when delivering a package to each delivery destination can be made easier.

  Hereinafter, the present invention will be described in detail according to preferred embodiments with reference to the accompanying drawings.

[First Embodiment]
Normally, procurement of vehicles and the like (charter) is performed in a contract form with a plurality of periods such as a monthly contract and a daily contract. In general, when a vehicle is procured by a monthly (first unit period) contract, if day (month) is the first unit period, “day” is the second unit that is a constituent unit of the first unit period. The procurement cost per day is cheaper than procurement by contract. Similarly, it is time to procure with a day (first unit period) contract (when “day” is the first unit period, “time” is the second unit period which is a constituent unit of the first unit period) The procurement cost per hour is cheaper than procurement by contract. In addition, when the first unit period is “year” and the second unit period is “month”, or when the first unit period is “week” and the second unit period is “day”, etc. But the same is true. In this embodiment, paying attention to such points, when delivering packages using a plurality of vehicles to a plurality of delivery destinations, a delivery plan that takes into account the desired delivery date and time, the number of allowed vehicles, etc. from each delivery destination is created. At the same time, a technique for reducing the cost required for procurement of the vehicle as much as possible will be described.

  FIG. 1 is a block diagram showing a schematic configuration of a system according to the present embodiment. As shown in the figure, the system according to this embodiment includes a server device 101 provided in a delivery center 100 as a package delivery source and terminal devices provided in package delivery destinations 110, 120, and 130, respectively. 111, 121, 131. Further, the server device 101 and the terminal devices 111, 121, and 131 are each connected to a network 140 such as the Internet, and are configured to be capable of data communication with each other. The server apparatus 101 is not necessarily provided in the distribution center 100, and may be installed in another base, for example, a business office (not shown). In this case, the distribution center 100 is provided with a terminal device for performing data communication with the server device 101.

  FIG. 2 is a block diagram illustrating a hardware configuration of the server apparatus 101.

  The CPU 201 controls the entire apparatus using programs and data stored in the RAM 202 and the ROM 203 and executes each process described later that is performed by the server apparatus 101.

  The RAM 202 has an area for temporarily storing programs and data loaded from the external storage device 206, data received from the outside via an I / F (interface) 207, and the like. The RAM 202 also has a work area used when the CPU 201 executes various processes. That is, the RAM 202 can provide various areas as appropriate.

  The ROM 203 stores setting data and a boot program for the apparatus.

  The operation unit 204 is configured by a keyboard, a mouse, and the like, and can input various instructions to the CPU 201 when operated by an operator of the apparatus.

  The display unit 205 is configured by a CRT, a liquid crystal screen, or the like, and can display a processing result by the CPU 201 using an image, text, or the like.

  The external storage device 206 is a large-capacity information storage device represented by a hard disk drive device. The external storage device 206 stores an OS (operating system) and programs and data for causing the CPU 201 to execute each process described later that is performed by the server device 101. Various table data and various logs described later are also stored here. Programs and data stored in the external storage device 206 are appropriately loaded into the RAM 202 under the control of the CPU 201 and are processed by the CPU 201.

  The I / F 207 is used to connect the apparatus to the network 140, and the apparatus performs data communication with the terminal apparatuses 111, 121, and 131 connected to the network 140 via the I / F 207. It can be carried out.

  A bus 208 connects the above-described units.

  Next, the operation of the system having the above configuration will be described. Each delivery destination requests the delivery center 100 in advance to deliver the desired package and its amount in order to deliver the package to its place in advance. Upon receiving this request, the delivery center 100 transports the requested package to the delivery destination by the amount of the requested amount.

  Therefore, on the delivery side, it is essential to secure personnel for the work of unloading luggage from this vehicle when it arrives according to the arrival date and time of this vehicle, the number of vehicles, the amount of luggage, etc. It becomes. Therefore, when requesting delivery of a package, it is essential for the delivery destination side to notify the delivery center 100 in advance of the delivery date and time, the maximum number of vehicles that can be handled by this delivery destination, and the like. This notification is sent to the server apparatus 101 as electronic data from its own terminal device (the terminal device 111 for the delivery destination 110, the terminal device 121 for the delivery destination 120, and the terminal device 131 for the delivery destination 130). May be. Further, the delivery center 100 may be notified to the delivery center 100 using a fax or the like. In this case, the staff on the delivery center 100 side sees the notified content and inputs the content to the server device 101 using the operation unit 204.

  FIG. 3 is a diagram showing a configuration example of a table in which the result of totaling the number of vehicles that can be accepted in the morning and in the afternoon at each delivery destination is registered. In the figure, A delivery destination can accept a maximum of three vehicles in the morning and can accept a maximum of three vehicles in the afternoon. At the delivery destination B, a maximum of three vehicles can be received in the morning, and a maximum of three vehicles can be received in the afternoon. At the C delivery destination, a maximum of one vehicle can be received in the morning, and a maximum of one vehicle can be received in the afternoon.

  The A delivery destination is, for example, the delivery destination 110. The B delivery destination is, for example, the delivery destination 120. The C delivery destination is, for example, the delivery destination 130. In the following description, the delivery destination 110 is used as the A delivery destination, the delivery destination 120 is used as the B delivery destination, and the delivery destination 130 is used as the C delivery destination.

  The data in the table is created, for example, by inputting necessary information using the operation unit 204 by the staff on the delivery center 100 side and stored in the external storage device 206. As data, you may register the allowable number of vehicles by capturing the time with a finer mesh than the unit of morning and afternoon (for example, 1 hour unit, 30 minute unit, etc.), or after the previous vehicle arrives You may also register the minimum time required to accept a vehicle. Of course, acceptable vehicle conditions (for example, weight limit, height limit, function condition) and the like can also be set. Further, although not shown, it is of course possible to set vehicle conditions used for delivery for each product to be delivered. For example, the condition is set such that a fresh food product is delivered by a refrigerated vehicle and a frozen food product is delivered by a frozen vehicle.

  In the external storage device 206, data of a table in which the amount of packages delivered in the past to each of the delivery destinations 110, 120, and 130 is registered for the past several months. FIG. 4 is a diagram showing a configuration example of a table (delivery amount registration table) in which amounts of packages delivered in the past for each delivery destination (A delivery destination, B delivery destination, C delivery destination) are registered by month. .

  In the figure, 401 indicates the amount of packages delivered to the A delivery destination, the B delivery destination, and the C delivery destination on each day from September 1 to September 30, 2006, and September 1 to September 30, 2006. It is a table in which the result (total amount) of the total amount of packages delivered to each delivery destination for each delivery destination is registered.

  402 indicates the amount of the package delivered to the A delivery destination, the B delivery destination, and the C delivery destination on each day from August 1 to August 31, 2006, and each of the quantities from August 1 to August 31, 2006. It is a table in which the result (total amount) of the total amount of packages delivered to the delivery destination is registered for each delivery destination.

  403 indicates the amount of packages delivered to the A delivery destination, the B delivery destination, and the C delivery destination on each day from July 1 to July 31, 2006, and each of the items from July 1 to July 31, 2006. It is a table in which the result (total amount) of the total amount of packages delivered to the delivery destination is registered for each delivery destination.

  These tables are created by the staff on the delivery center 100 side inputting them using the operation unit 204, and the data of the created tables is stored in the external storage device 206. The amount of luggage may be captured on a weight basis or may be captured on a volume basis. However, the method for calculating the amount of luggage is not limited to a specific method. Of course, the amount of luggage may be calculated by other methods, and a plurality of calculation methods may be used.

  Next, using such a delivery amount registration table, the amounts of packages delivered to the A delivery destination, the B delivery destination, and the C delivery destination on each of the days from October 1 to October 31, 2006 are shown. The estimation process will be described below with reference to FIG. 5 showing a flowchart of the process. Note that a program and data for causing the CPU 201 to execute processing according to the flowchart of FIG. These programs and data are loaded into the RAM 202 as appropriate under the control of the CPU 201 before executing this process. Then, when the CPU 201 executes processing using the loaded program and data, the server apparatus 101 executes each processing described below.

  First, in step S501, the variable k is initialized to 1. In step S502, the variable i is initialized to 7 and the variable j is initialized to 1. In step S503, the total amount of packages delivered to the kth delivery destination between October 1 and October 31 is estimated. Here, the first delivery destination is A delivery destination, the second delivery destination is B delivery destination, and the third delivery destination is C delivery destination. Various processes can be considered in this step. For example, “August 2006” obtained by referring to the above table 401 and “the total amount of packages delivered to the k-th delivery destination from September 1 to September 30, 2006” and the table 402 described above. The average value of “the total amount of packages delivered to the kth delivery address from 1st to August 31st” is obtained. The obtained average value is set as the total amount of packages delivered to the kth delivery destination between October 1 and October 31.

  In the present embodiment, for the sake of easy explanation, the explanation is made in the form of prediction of the total amount of luggage. However, as a matter of course, it is also possible to use a method of predicting the demand of each handled product for each delivery destination and predicting the amount of package delivered to each delivery destination in the form of the sum of the prediction results. Further, the prediction method is not limited to the moving average model, and may be calculated using another prediction model such as an exponential smoothing model, an ARIMA model, or a neural network model.

  Of course, when a method is used for forecasting demand for each product and calculating the total amount, it is possible to set a prediction model for each product. In addition, demand prediction is performed on a single product using a plurality of models, and among the prediction results calculated by each prediction model, the prediction value calculated by the model with the best prediction result for the most recent predetermined period is used. Of course, a method of adopting the prediction result may be adopted.

  In step S504, the delivery amount registration table illustrated in FIG. 4 stored in the external storage device 206 is loaded into the RAM 202, and the total amount X of the kth delivery destination registered in the i month delivery amount registration table X. Get the data indicating. When this step is performed for the first time, since i = 7, data indicating the total amount X of the kth delivery destination registered in the delivery amount registration table 403 illustrated in FIG. 4 is acquired.

  In step S505, data indicating the amount Y of the package delivered to the kth delivery destination on i month j is acquired in the i month delivery amount registration table.

  In step S506, Zi = Y / X is calculated. That is, in the variable Zi, the result of dividing the amount of the package delivered to the kth delivery destination on i month j by the total amount of the kth delivery destination registered in the i month delivery amount registration table is registered. Will be.

  In step S507, it is checked whether i = 9. As a result of this check, if i ≠ 9, the process proceeds to step S508, and the variable i is incremented by one. And the process after step S504 is performed using the incremented variable i. On the other hand, if i = 9, the process advances to step S509. In step S509, (Z7 + Z8 + Z9) / 3 is obtained as “the distribution coefficient for the k-th delivery destination on October j”.

  In step S510, it is checked whether j = 31. That is, for each day from October 1 to October 31, it is checked whether or not a distribution coefficient for the kth delivery destination has been obtained. As a result of this check, if j ≠ 31, the process proceeds to step S511 to increment the variable j by 1 and initialize the variable i to 7. Then, the process returns to step S504, and the subsequent processes are repeated.

  On the other hand, if j = 31, the process proceeds to step S512. In step S512, the distribution coefficients are normalized so that the sum of the distribution coefficients obtained for each day from October 1 to October 31 is 1. For example, normalization is performed by dividing the distribution coefficient obtained for each day from October 1 to October 31 by the sum of all the distribution coefficients.

  Then, in step S513, an estimated package amount estimated to be delivered to the kth delivery destination on each day from October 1 to October 31 is obtained using the normalized apportioning coefficient. For example, if the apportioning coefficient obtained for October 1 is g and the amount of luggage estimated in step S503 is f, the estimated luggage amount estimated to be delivered to the k-th delivery destination on October 1 is It can be obtained by calculating g × f. Therefore, in step S513, such calculation processing is performed for each day from October 1 to October 31.

  In step S514, it is checked whether k = 3. That is, it is checked whether or not “distribution coefficient and estimated luggage amount for each day from October 1 to October 31” are obtained for all of the A delivery destination, the B delivery destination, and the C delivery destination. If it is determined as a result of such check, this processing is terminated.

  On the other hand, if it is not obtained as a result of the check, the process proceeds to step S515. In step S515, the variable k is incremented by 1, and the processes in and after step S502 are performed.

  By executing the process according to the flowchart shown in FIG. 5 described above, the table illustrated in FIG. 6 is created. FIG. 6 is a diagram illustrating a configuration example of a table in which the estimated luggage amount estimated to be delivered to each delivery destination on each day from October 1 to October 31 is registered.

  Next, processing for determining how many vehicles are required on each day from October 1 to October 31 will be described. FIG. 7 is a diagram schematically showing processing for obtaining the number of vehicles required on October 1 in the table illustrated in FIG.

  First, the estimated amount of baggage estimated to be delivered to each of the A delivery destination, the B delivery destination, and the C delivery destination on October 1 is acquired from the table illustrated in FIG. 701 in the figure is information indicating the estimated amount of luggage estimated to be delivered to each delivery destination, acquired from the table illustrated in FIG. On the other hand, the table illustrated in FIG. 3 is acquired. This is indicated by reference numeral 702 in FIG. Then, using these two pieces of acquired information, that is, the information indicated by 701 in the figure and the table indicated by 702, how many vehicles are used by using a well-known vehicle routing planning technique. Then, a vehicle dispatch plan is prepared for whether to deliver the package to each delivery destination.

  Information necessary for this routing plan, for example, location information of the delivery center and each delivery destination, data indicating the required time between the delivery destination and each delivery destination, and between each delivery destination, or data for calculation (distance information and road information , The road speed regulation information) is recorded in the external storage device 206. Furthermore, vehicle routing planning techniques are described in, for example, the literature G. Laporte, M. Gendreau, J. Potvin and F. Semet, "Classical and modern heuristics for the vehicle routing problem", International Transactions in Operational Research, Vol. 7, 2000, pp285. Techniques such as saving method, sweep method, tabu search method described in -300 may be used.

  At this time, the number of vehicles allowable at each delivery destination for each time slot shown in FIG. 3 is used as a constraint condition when planning a vehicle routing. In addition, if there are other conditions (vehicle conditions that can be accepted at each delivery destination described above and vehicle conditions that are used when goods are delivered), these are also used as constraint conditions. 703 in the figure is a plan table obtained as a result of this plan. With such planning technology, the number of vehicles required on October 1 can be obtained.

  In the figure, reference numeral 704 denotes a table obtained by recalculating the plan table for each delivery destination. The arrival date and time information (date, delivery time) to each delivery destination calculated as a result of this routing plan is transmitted from the server device 101 of the delivery center 100 to the terminal device (delivery destination 110, 120, 130) of each delivery destination (delivery destination 110, 120, 130). If it is the delivery destination 110, it is transmitted to the terminal device 111, if it is the delivery destination 120, it is transmitted to the terminal device 121, and if it is the delivery destination 130, it is transmitted to the terminal device 131). Further, departure time information from each delivery source calculated as a result of the routing plan is accumulated in the server device 101 of the delivery center 100.

  The arrival time at each delivery destination calculated at this time and the information on the number of vehicles arriving at that time, or the departure time from each delivery source and the information on the number of vehicles departing at that time are the numbers of confirmed packages to be actually delivered. It can also be used as a constraint condition when performing a routing plan. In this way, the delivery center and each delivery destination can know in advance the time zone in which personnel must be secured for the work of paying out and receiving packages. Since the quantity of the package is based on the order quantity of each delivery destination, the person in charge of the delivery destination naturally recognizes it. By arranging the personnel in advance at the scheduled arrival time of the package, it is not necessary to restrain the personnel required for paying out / receiving unnecessarily for a long time.

  In the above-mentioned conventional technology, only the costs related to transportation and delivery can be reduced by minimizing the vehicle, and the work and labor costs that occur at the delivery center and each delivery destination for the delivery and receipt of packages. There is also a problem that it is not always the best because it is not included. This is because the above conventional technique is a technique for reducing the procurement cost of the vehicle, but does not take into consideration the work system on the package paying / receiving side. The system notifies the delivery destination in advance of the arrival time and the number of vehicles so that the system for payout / acceptance work is not in place, and there is no work load or waste of labor costs due to excessive load, stagnation, or excessive personnel. By confirming the departure time and the number of vehicles at the distribution center, it is possible to arrange the order for receiving the ordered product at each delivery destination and the state of payout at the distribution center.

  Such processing is similarly performed for each day from October 2 to October 31, and the number of vehicles required for each day from October 1 to October 31 can be obtained. However, it should be noted that the number obtained here is used for each processing described below and is not final.

  Next, processing for determining the number of vehicles chartered for one month in October and the number of vehicles chartered for one day in October will be described below with reference to FIG. 8 showing a flowchart of the processing. Note that a program and data for causing the CPU 201 to execute processing according to the flowchart of FIG. These programs and data are loaded into the RAM 202 as appropriate under the control of the CPU 201 before executing this process. The CPU 201 executes processing using the loaded program and data, so that the server apparatus 101 executes each processing described below.

  First, in step S801, data indicating the cost M required to charter one vehicle for one month in October and data indicating the cost D required to charter one vehicle for one day are stored externally. Load from device 206 into RAM 202. In step S802, the variable L is initialized to 1. Subsequently, in step S803, the variable i is initialized to 1, and the variable x is initialized to 0.

  In step S804, the number K determined in the flowchart of FIG. 5 is loaded from the external storage device 206 to the RAM 202 as the number required on October i.

  In step S805, the result obtained by adding the value of (KL) to the value indicated by the variable x is stored again in the variable x. However, when K <L, the value of (K−L) is 0.

  In step S806, it is checked whether i = 31 (the last day is 31 since October). If i = 31 is not the result of the check, the process proceeds to step S807. In step S807, the variable i is incremented by one, and the processing from step S804 is performed. On the other hand, as a result of this check, if i = 31, the process advances to step S808.

In step S808, to calculate the x × D + L × M, and stores the calculation result to a variable _{S L.} Here, S _L is the L of vehicles chartered one month, indicating the total cost which occurs when the chartered for a day the x of vehicles.

  In step S809, it is checked whether or not the value of the variable L is the same as the maximum number among the numbers obtained from the flowchart of FIG. 5 for each day from October 1 to October 31. If the result of such check is not the same, the process advances to step S810. In step S810, the variable L is incremented by 1, and the processes in step S803 and subsequent steps are performed. On the other hand, if the result of the check is the same, the process advances to step S811.

In step S811, _L that minimizes SL is specified. That is, S ₁ , S ₂ ,... S _N are obtained by the above processing, and when S ₂ is the minimum, “2” is specified in step S811. That is, L identified in step S811 charters L vehicles for one month and minimizes the total cost incurred when x vehicles are chartered for one day. “Number of vehicles chartered for one month” Is shown.

  FIG. 9 is a diagram specifically showing processing according to the flowchart of FIG. In the figure, for example, the number of units obtained from the flowchart of FIG. 5 for October 1 is five, and the number of units obtained from the flowchart of FIG. 5 for October 15 is four. In the figure, the cost M required to charter one vehicle for one month in October is ¥ 500000, and the cost D required to charter one vehicle for one day is ¥ 40000.

  In such a situation, first, the number L of vehicles to be chartered for one month in October is set to one. In this case, the cost required for chartering one vehicle for one month in October is 1 unit × ¥ 500000 = ¥ 500000.

  Taking October 1 as an example, the number of cars required for this day is five, so if one of those cars is chartered for the past month, the remaining four cars ((K-L ) Will be chartered for one day this day. Taking October 18 as an example, the number of vehicles required for this day is three, so if one of those vehicles is chartered for the past month, the remaining two vehicles (above (K-L ) Will be chartered for one day this day. In this way, the number of charters for one day on each day can be obtained, and the total value x is 101 in FIG. Therefore, the cost required for chartering 101 units for one day is 101 units × ¥ 40000 = ¥ 4040000.

As a result of the above processing, if the number L of vehicles to be chartered for one month in October is 1, and the remaining required number x of vehicles is chartered for one day, the total cost is ¥ 500000 + ¥ 400000 = 4540000 ( S ₁₎ to become.

Such calculation processing is performed for L = 2, L = 3, and so on. In this figure, “the maximum number of units obtained from the flowchart of FIG. 5 for each day from October 1 to October 31” is “8”. become. Therefore, in this case, S ₁ , S ₂ ,..., S ₈ are obtained. In the figure, S ₁ = 4540000, S ₂ = 3800000, S ₃ = 3260000, S ₄ = 3000000, S ₅ = 2940000, S ₆ = 316000, S ₇ = 3540000, S ₈ = 4000000. Since these minimum is the _{S 5,} to identify the L = 5 in the step S811. That is, the number L of vehicles to be chartered for one month in October is five, and if the number of vehicles required for each day exceeds five, the charter is chartered for one day for the excess.

  Each process described above is performed in order to procure vehicles required for October in a month prior to October.

  Next, a process for creating a delivery plan on October x as a few days after the current date will be described with reference to FIG. 10 showing a flowchart of the process. Note that a program and data for causing the CPU 201 to execute processing according to the flowchart of FIG. These programs and data are loaded into the RAM 202 as appropriate under the control of the CPU 201 before executing this process. The CPU 201 executes processing using the loaded program and data, so that the server apparatus 101 executes each processing described below.

  From each delivery destination, a request (ordering information) stating “Please deliver the package amount Z on the day x month y” is sent to the delivery center 100 asynchronously. As described above, such a request is received on the delivery center 100 side as non-electronic information such as a fax, or is received on the delivery center 100 side as electronic information such as an e-mail. In the former case, the staff on the delivery center 100 side sees this non-electronic information and inputs the contents to the server apparatus 101 using the operation unit 204. On the other hand, in the latter case, a server as electronic information is sent from each delivery destination terminal device (the terminal device 111 for the delivery destination 110, the terminal device 121 for the delivery destination 120, and the terminal device 131 for the delivery destination 130). Sent to the device 101.

  In any form, the server apparatus 101 always accepts information indicating “when and what amount of parcels are delivered to which delivery destination” (order information for the server apparatus 101). The received information is stored in the external storage device 206 as needed.

  Accordingly, first, in step S1001, information indicating “amount of packages to be delivered to each delivery destination on October x (determined package amount)” among the information stored in the external storage device 206 is loaded into the RAM 202.

  In step S1002, information about October x is acquired from the table (delivery destination arrival / departure time table) 704 shown in FIG. That is, information indicating which vehicle delivers to each delivery destination on October x is obtained.

  Next, in step S1003, the converted vehicle number obtained by dividing the “estimated luggage amount for each delivery destination on October x day” obtained according to the flowchart of FIG. 5 by the vehicle-mounted amount, and “October x day” obtained in step S1001 above. The number of converted vehicles obtained by dividing “the amount of packages delivered to each delivery destination (determined luggage amount)” by the in-vehicle amount is compared for each corresponding delivery destination. Then, it is checked for each delivery destination whether or not the converted number of vehicles of estimated luggage amount = the number of converted vehicles of fixed baggage amount at the same delivery destination. As a result of this check, if the estimated baggage amount = determined baggage amount at all the delivery destinations, the process proceeds to step S1004, and even if there is one delivery destination where the estimated baggage amount converted vehicle number ≠ the confirmed baggage amount converted vehicle number If it exists, the process proceeds to step S1005.

  Regarding the number of converted vehicles, it is logically necessary when one vehicle makes one turn (from the delivery center to the delivery center where the packages are loaded onto the vehicle and delivered to the delivery destination and returned to the delivery center). When routing is performed according to a rule that the number of vehicles (for example, 0.4 units or the like) is delivered to only one delivery destination, it is possible to calculate and compare the physically necessary number of vehicles. Of course, it is not limited to this method.

  In step S1004, the delivery time information of the corresponding delivery destination is added from the table 704 to the confirmed package amount data of each delivery destination, and stored in the external storage device 206 again. For example, data indicating the delivery time of five vehicles to the delivery destination A is added to the data of the confirmed package amount of the delivery destination A. The same applies to the B delivery destination and the C delivery destination. It should be noted that the result of adding delivery time information to the confirmed package quantity data may be delivered via the network 140 to the corresponding delivery destination. When the process in step S1004 is performed, the process according to this flowchart is terminated.

  On the other hand, in step S1005, processing similar to that in step S1004 is performed according to the up / down shake rule. The up-and-down movement rule is, for example, that the converted number of vehicles for the confirmed baggage amount exceeds the converted number of vehicles for the estimated baggage amount, that is, when the vehicle is up, the desired time (corresponding to When the delivery time is set in the earliest time zone of the designated delivery destination) and the converted number of vehicles for the confirmed baggage is lower than the estimated number of vehicles for the estimated baggage, that is, when the vehicle falls In the process of deleting a package from a later time zone, the point is to adjust the delivery time of each vehicle.

  In step S1006, it is checked whether there is a violation in the desired time zone. As a result of this check, if there is no violation, the process proceeds to step S1008. If there is a violation, the process proceeds to step S1007. In step S1007, the desired time zone of the violation delivery destination is adjusted. Such adjustment may be performed by the operator of the server apparatus 101 using the operation unit 204, or may be performed by the CPU 201 according to a predetermined rule. In the former case, this step accepts and reflects this operation.

  In step S1008, a vehicle allocation routing plan for October x is performed for the vehicle (first vehicle) to be chartered for one month. This technique is as described above. The constraints used at this time are the same as those used for the delivery routing plan when delivering estimated packages to each delivery destination in order to calculate the number of vehicles that would be required. However, as described above, for example, the result of the estimated package delivery routing plan is shown in FIG. 7 in such a way that the delivery time to each delivery destination obtained as a result of routing of the estimated package is given a certain margin. If the result is as shown, 3 units in the morning, 3 units in the afternoon, 3 units from 9 am to 10 pm, 3 units from 12 pm to 1 pm Of course, it is possible to make it a constraint.

  When such a rule is adopted, the rule is recorded in the external storage device 206 in advance. For example, 30 minutes before and after the arrival time calculated at the time of the estimated package delivery routing plan is set as a constraint condition or within one hour from the arrival time. In addition, if the restricted time period is earlier than the package acceptance start time at the delivery destination, or if the restricted time period is later than the package acceptance end time, etc. It is even better if you also record the rules.

  In this way, as soon as the estimated package delivery routing plan results, the delivery time information is sent to each delivery destination, so it becomes easy to secure personnel for receiving the package in advance at each delivery destination. At the same time, it is not necessary to make a staffing plan that restrains the personnel for an unnecessarily long time, so that there is a derivative effect that it is possible to reduce the cost required to secure the personnel for receiving the luggage. Therefore, it is possible to reduce the cost required for delivery of the package. With this process, it is possible to determine how much luggage is loaded on each of the first vehicles required on October x.

  Next, in step S1009, the remainder obtained by subtracting the amount of luggage determined to be delivered by the first vehicle in the dispatching routing plan in step S1008 from the confirmed luggage amount delivered to each delivery destination on October x is obtained.

  In step S1010, a dispatching routing plan for delivering the remainder by the second vehicle (a vehicle chartered for one day on October x) is performed.

  Note that the dispatch routing plan result obtained in steps S1008 and S1010 is output in the form of the table 704, but the table is stored in the external storage device 206. However, the handling of such a table is not particularly limited, and the table may be distributed to a corresponding delivery destination via the network 140.

  As described above, according to the present embodiment, it is possible to reduce the cost related to the delivery of the package and reduce the burden on the person in charge of contracting the vehicle for the processing.

  The above description is based on an example of determining the number of vehicles chartered for one month and the number of vehicles chartered for one day among vehicles required for one month in October. However, it is clear from the above description that the essence of the above description is the same even if this “October” is replaced with another month. In addition, the above explanation is based on the cost of chartering for one month to procure the necessary vehicles and the cost of chartering for one day, but these are charters for one year. However, even if it is chartered by time, or even if it uses expenses for owning it, if there are multiple expenses required to procure the vehicle, it can be changed to them It is clear that they are the same.

  Further, the handling of data and tables required in the course of each processing in the above description has not been particularly mentioned in the above description, but such handling is not particularly limited and can be considered as appropriate.

[Second Embodiment]
In this embodiment, another processing mode for determining the number of vehicles chartered for one month in October and the number of vehicles chartered for one day in October will be described below with reference to FIG. 11 showing a flowchart of the processing. explain. Note that a program and data for causing the CPU 201 to execute processing according to the flowchart of FIG. These programs and data are loaded into the RAM 202 as appropriate under the control of the CPU 201 before executing this process. The CPU 201 executes processing using the loaded program and data, so that the server apparatus 101 executes each processing described below.

  In steps S801 to S803, processing as described in the first embodiment is performed.

  In step S1104, the number K 'of charters for one day on October i is obtained as follows.

  In the present embodiment, the data of the number of days required for each past month, such as the number of days required for each day in September, the number required for each day in August, the number required for each day in July, etc. It is recorded in the external storage device 206 as a log. At the same time, the estimated number of vehicles that were estimated to be delivered on each day of September using the dispatch routing planning technology, the estimated number of devices that were determined for each day in August, and the estimated number that was determined for each day in July. The number of units is also recorded in the external storage device 206.

  Accordingly, in step S1104, first, the number of units required for each day in a past fixed period (optionally specified using the operation unit 204 or the like) and data on the estimated number are read from the external storage device 206. Then, the difference (prediction error) between the number of vehicles actually required and the estimated number of vehicles on that day is taken, and the square root of the average value of the sum of squares of these differences is taken to obtain the standard deviation σ of the error. Then, the estimated number μ of one day on October i is obtained by the method described above with reference to FIGS. 5 and 7, and the distribution data of the number of vehicles in which the error is distributed around the estimated number μ with the standard deviation σ. To get. The distribution data at this time is equivalent to a normal distribution with an average value μ and a standard deviation σ. Note that the distribution data may be generated by reading out the data of the number required for the i day of each past month from the external storage device 206 and obtaining the average value and standard deviation of each number. In this case, the average value μ is an average value of the number of units required on day i in the past several months. The standard deviation σ is the standard deviation of the number of units required on the i day of each month in the past several months. The extent to which past data is used is appropriately determined.

  Next, assuming that the probability density function of a normal distribution having an average of μ and a standard deviation of σ is f (x, μ, σ), (x−L) × f (x, μ, σ) is changed from L with respect to x. The value integrated up to ∞ is obtained as the expected value K ′ of the number of charters for one day on October i.

  In this way, if it is assumed that L vehicles are chartered with a charter for one month in October, the number of vehicles that have to be chartered for one day on October i can be obtained. The optimum number K ′ can be obtained by quantitatively reflecting the influence of the above.

  The integral calculation can be performed by using a well-known numerical integration method (such as a trapezoidal method).

  In step S1105, the result of adding the value of K 'to the value indicated by the variable x is stored again in the variable x.

  In steps S806 and S807, processing as described in the first embodiment is performed.

  In step S1109, it is determined whether or not the value of the variable L is the same as a value obtained by adding a preset value α to the average value μ obtained in step S804 for each day from October 1 to October 31. To check. If the result of such check is not the same, the process advances to step S810. In step S810, the variable L is incremented by 1, and the processes in step S803 and subsequent steps are performed. On the other hand, if the result of the check is the same, the process advances to step S811.

In step S811, _L that minimizes SL is specified. That is, S ₁ , S ₂ ,... S _N are obtained by the above processing, and when S ₂ is the minimum, “2” is specified in step S811. That is, L identified in step S811 charters L vehicles for one month and minimizes the total cost incurred when x vehicles are chartered for one day. “Number of vehicles chartered for one month” Is shown.

  In step S1109, if the value of f (L, μ, σ) becomes a sufficiently small value (for example, 0.01) or less, the process proceeds to step S811, where the value is smaller than the sufficiently small value. If it is larger, the process may proceed to step S810.

  In this embodiment, the average value μ and the standard deviation σ are obtained when the number K ′ of charters for one day in October is obtained. The average value μ and the standard deviation σ are September. It may be obtained at the time of completion and recorded in the external storage device 206 as data (distribution data). That is, at the end of each month, the average value μ and standard deviation σ for each day are obtained and recorded in the external storage device 206 as data (distribution data).

  In step S1104, the data of the average value μ and the standard deviation σ for day i is read from the external storage device 206. Then, a value obtained by integrating (x−L) × f (x, μ, σ) from L to ∞ with respect to x is obtained as the number K ′ of charters for one day on October i.

  By doing in this way, the process in step S1104 can be simplified and the entire process can be performed at a higher speed.

  In this embodiment, the normal distribution function is used as the distribution function. However, other distribution functions may be used.

[Other Embodiments]
Needless to say, the object of the present invention can be achieved as follows. That is, a recording medium (or storage medium) that records a program code of software that implements the functions of the above-described embodiments is supplied to a system or apparatus. Then, the computer (or CPU or MPU) of the system or apparatus reads and executes the program code stored in the recording medium. In this case, the program code itself read from the recording medium realizes the functions of the above-described embodiment, and the recording medium on which the program code is recorded constitutes the present invention.

  Further, by executing the program code read by the computer, an operating system (OS) or the like running on the computer performs part or all of the actual processing based on the instruction of the program code. Needless to say, the process includes the case where the functions of the above-described embodiments are realized.

  Furthermore, it is assumed that the program code read from the recording medium is written in a memory provided in a function expansion card inserted into the computer or a function expansion unit connected to the computer. After that, based on the instruction of the program code, the CPU included in the function expansion card or function expansion unit performs part or all of the actual processing, and the function of the above-described embodiment is realized by the processing. Needless to say.

  When the present invention is applied to the recording medium, program code corresponding to the flowchart described above is stored in the recording medium.

1 is a block diagram showing a schematic configuration of a system according to an embodiment of the present invention. 2 is a block diagram illustrating a hardware configuration of a server apparatus 101. FIG. It is a figure which shows the structural example of the table in which the result which totaled the number of vehicles which can accept | permit each morning in the morning in each delivery destination when the delivery destination was seen for every delivery destination was registered. It is a figure which shows the structural example of the table (delivery amount registration table) which registered the quantity of the package delivered in the past with respect to each delivery destination (A delivery destination, B delivery destination, C delivery destination). In the flowchart of the process which estimates the quantity of the package delivered to each of A delivery destination, B delivery destination, and C delivery destination on each day of October 1 to October 31, 2006 using the delivery amount registration table. is there. It is a figure which shows the structural example of the table in which the estimated baggage amount estimated to deliver with respect to each delivery address from October 1 to October 31 was registered. It is the figure which showed typically about the process for calculating | requiring the number of vehicles required on October 1 in the table illustrated in FIG. It is a flowchart of the process which determines the number of vehicles chartered for one month in October, and the number of vehicles chartered for one day in October. It is the figure which showed the process according to the flowchart of FIG. 8 concretely. It is a flowchart of the process for creating the delivery plan in October x day after several days from the present day. It is a flowchart of the process in another process form which determines the number of vehicles chartered for one month in October, and the number of vehicles chartered for one day in October.

Claims (11)

An information processing apparatus that performs a procurement plan for vehicles necessary for delivering a package to a delivery destination,
Procuring a vehicle with a first unit period contract, which is a unit period constituting the first unit period, and first data indicating a cost required for procuring the vehicle with a contract of the first unit period Storage means for storing second data indicating an expense required for
In order to deliver an estimated amount of luggage, which is estimated to be delivered to a delivery destination for each second unit period, to the delivery destination, the number of vehicles required for each second unit period is determined as a routing plan. A first calculating means obtained by:
Setting means for setting a first number that is the number of vehicles procured in the contract of the first unit period;
The number of vehicles to be procured in the contract for the second unit period using the number of vehicles required for each second unit period determined by the routing plan and the first number. A second calculating means for obtaining the number of
Using the cost indicated by the first data and the first number, the cost required to procure the first number of vehicles under a contract for the first unit period is obtained, and the second data is Expenses required to procure the second number of vehicles in the contract for the second unit period using the indicated expenses and the second number for each second unit period, and the obtained expenses A third calculating means for calculating the sum of the above as the total cost required for the procurement of the vehicle;
The setting means sets a different first number, and the second calculation means and the third calculation means calculate a total cost that is the smallest of the total costs determined for the different first numbers. And specifying means for specifying the first number for the specified total cost as the number of vehicles procured by the contract of the first unit period ,
The first calculation means can pay out the vehicle at each delivery destination, time zone information indicating an acceptable time zone of the vehicle and information indicating the number of vehicles acceptable in the time zone, or at each delivery source. An information processing apparatus that performs a routing plan so as to satisfy at least one condition of time zone information indicating a specific time zone and information indicating the number of vehicles payable during the time zone . The second calculation means is necessary for delivering the estimated number of packages obtained by the routing plan for the first unit and each second unit period in a certain past period to a delivery destination. Distribution of the difference between the number of vehicles and the number of vehicles actually required for delivery of the parcel for each of the second unit periods in the past certain period, and obtained by the routing plan The second number of vehicles, which is the number of vehicles procured in the contract of the second unit period, is obtained using a distribution of differences obtained by averaging the number of vehicles required for each unit period of claim 2. Item 4. The information processing apparatus according to Item 1. Storage means for storing a confirmed package amount that is a package amount actually delivered to the delivery destination;
A routing plan means for performing a routing plan for delivering the fixed amount of packages to each delivery destination;
The routing plan means calculates the arrival time at the delivery destination of the vehicle obtained as a result of the routing plan for the estimated luggage amount by the first calculation means, and the number of vehicles arriving at the delivery destination at the arrival time. and constraint information processing apparatus according to claim 1 or 2, characterized in that the routing plan for delivering the confirmation luggage amount of cargo to satisfy the constraint condition. A determination means for comparing the determined baggage amount with the estimated baggage amount and determining whether to change the constraint condition;
The routing planning unit changes the constraint condition based on a predetermined rule when the determination unit determines to change the constraint condition, and sets the determined package amount so as to satisfy the changed constraint condition. The information processing apparatus according to claim 3 , wherein a routing plan for delivering a package is performed. An information processing apparatus that performs a procurement plan for vehicles necessary for delivering a package to a delivery destination,
Procuring a vehicle with a first unit period contract, which is a unit period constituting the first unit period, and first data indicating a cost required for procuring the vehicle with a contract of the first unit period Storage means for storing second data indicating an expense required for
In order to deliver an estimated amount of luggage, which is estimated to be delivered to a delivery destination for each second unit period, to the delivery destination, the number of vehicles required for each second unit period is determined as a routing plan. A first calculating means obtained by:
Setting means for setting a first number that is the number of vehicles procured in the contract of the first unit period;
The number of vehicles to be procured in the contract for the second unit period using the number of vehicles required for each second unit period determined by the routing plan and the first number. A second calculating means for obtaining the number of
Using the cost indicated by the first data and the first number, the cost required to procure the first number of vehicles under a contract for the first unit period is obtained, and the second data is Expenses required to procure the second number of vehicles in the contract for the second unit period using the indicated expenses and the second number for each second unit period, and the obtained expenses A third calculating means for calculating the sum of the above as the total cost required for the procurement of the vehicle;
The setting means sets a different first number, and the second calculation means and the third calculation means calculate a total cost that is the smallest of the total costs determined for the different first numbers. Identifying means for identifying and identifying the first number for the identified total cost as the number of vehicles procured by the contract of the first unit period;
With
Storage means for storing a confirmed package amount that is a package amount actually delivered to the delivery destination;
A routing plan means for performing a routing plan for delivering the fixed amount of packages to each delivery destination;
The routing plan means calculates the arrival time at the delivery destination of the vehicle obtained as a result of the routing plan for the estimated luggage amount by the first calculation means, and the number of vehicles arriving at the delivery destination at the arrival time. As a constraint condition, a routing plan for delivering the fixed amount of packages to satisfy the constraint condition is performed.
An information processing apparatus characterized by the above. A determination means for comparing the determined baggage amount with the estimated baggage amount and determining whether to change the constraint condition;
The routing planning unit changes the constraint condition based on a predetermined rule when the determination unit determines to change the constraint condition, and sets the determined package amount so as to satisfy the changed constraint condition. Make a routing plan to deliver the package
The information processing apparatus according to claim 5. The second calculation means is necessary for delivering the estimated number of packages obtained by the routing plan for the first unit and each second unit period in a certain past period to a delivery destination. Distribution of the difference between the number of vehicles and the number of vehicles actually required for delivery of the parcel for each of the second unit periods in the past certain period, and obtained by the routing plan The second number of vehicles, which is the number of vehicles procured in the contract of the second unit period, is obtained using a distribution of differences in which the number of vehicles required for each unit period is an average.
The information processing apparatus according to claim 5 or 6. An information processing method performed by an information processing apparatus that performs a procurement plan for vehicles necessary for delivering a package to a delivery destination,
Procuring a vehicle with a first unit period contract, which is a unit period constituting the first unit period, and first data indicating a cost required for procuring the vehicle with a contract of the first unit period A storage step of storing in the storage means second data indicating an expense required for storage;
In order to deliver an estimated amount of luggage, which is estimated to be delivered to a delivery destination for each second unit period, to the delivery destination, the number of vehicles required for each second unit period is determined as a routing plan. A first calculation step determined by:
A setting step of setting a first number that is the number of vehicles procured by the contract of the first unit period;
The number of vehicles to be procured in the contract for the second unit period using the number of vehicles required for each second unit period determined by the routing plan and the first number. A second calculation step for obtaining the number of
Using the cost indicated by the first data and the first number, the cost required to procure the first number of vehicles under a contract for the first unit period is obtained, and the second data is Expenses required to procure the second number of vehicles in the contract for the second unit period using the indicated expenses and the second number for each second unit period, and the obtained expenses The third calculation step to calculate the sum of the above as the total cost required to procure the vehicle,
A different first number is set in the setting step, and a minimum total cost among the total costs obtained for the different first number in the second calculation step and the third calculation step is determined. A specific step of identifying and identifying a first number for the identified total cost as the number of vehicles procured in the contract of the first unit period ,
In the first calculation step, time zone information indicating an acceptable time zone of the vehicle at each delivery destination and information indicating the number of vehicles acceptable in the time zone, or the vehicle can be paid out at each delivery source An information processing method, wherein a routing plan is performed so as to satisfy at least one condition of time zone information indicating an appropriate time zone and information indicating the number of vehicles payable in the time zone . An information processing method performed by an information processing apparatus that performs a procurement plan for vehicles necessary for delivering a package to a delivery destination,
Procuring a vehicle with a first unit period contract, which is a unit period constituting the first unit period, and first data indicating a cost required for procuring the vehicle with a contract of the first unit period A storage step of storing in the storage means second data indicating an expense required for storage;
In order to deliver an estimated amount of luggage, which is estimated to be delivered to a delivery destination for each second unit period, to the delivery destination, the number of vehicles required for each second unit period is determined as a routing plan. A first calculation step determined by:
A setting step of setting a first number that is the number of vehicles procured by the contract of the first unit period;
The number of vehicles to be procured in the contract for the second unit period using the number of vehicles required for each second unit period determined by the routing plan and the first number. A second calculation step for obtaining the number of
Using the cost indicated by the first data and the first number, the cost required to procure the first number of vehicles under a contract for the first unit period is obtained, and the second data is Expenses required to procure the second number of vehicles in the contract for the second unit period using the indicated expenses and the second number for each second unit period, and the obtained expenses The third calculation step to calculate the sum of the above as the total cost required to procure the vehicle,
A different first number is set in the setting step, and a minimum total cost among the total costs obtained for the different first number in the second calculation step and the third calculation step is determined. A specifying step of specifying and specifying the first number of vehicles for the specified total cost as the number of vehicles procured by the contract of the first unit period;
With
A routing planning step of performing a routing plan for delivering a package of a fixed package amount, which is a package amount actually delivered to the delivery destination, to each delivery destination;
In the routing planning step, the arrival time at the delivery destination of the vehicle obtained as a result of the routing plan for the estimated luggage amount in the first calculation step, and the number of vehicles arriving at the delivery destination at the arrival time are calculated. As a constraint condition, a routing plan for delivering the fixed amount of packages to satisfy the constraint condition is performed.
An information processing method characterized by the above. The computer program for functioning a computer as each means which the information processing apparatus of any one of Claims 1 thru | or 7 has. A computer-readable recording medium storing the computer program according to claim 10 .

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