JP2003285930A - Transportation schedule making method and its system - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a transportation schedule making method and its system for making an efficient transportation schedule for vehicles on which combined cargoes are loaded to be transported from a physical distribution center to delivery destinations and from collection destinations to the physical distribution center in accordance with time constraints for collection/delivery destinations. <P>SOLUTION: The transportation schedule making method comprises inputting position information for the physical distribution center and collection/delivery destination points, inputting cargo information including the amount of the cargoes to be transported, the collection/delivery destinations, and the time constraints for arrival, and inputting transporting vehicle information including the loading limits of the transporting vehicles. Furthermore, in making the transportation schedule, a table is made for the maximum allowable time for the delay of arrival and the maximum loading amount of the collected/delivered cargoes. The table determines whether the collection/delivery destination points for the cargoes are added or not to the transportation schedule consisting of visiting points for the plurality of vehicles, and then the transportation schedule for the vehicles is made by using various information under conditions in accordance with the time constraints. <P>COPYRIGHT: (C)2004,JPO

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a technique for computer-based scheduling and creation in the field of physical distribution, and more particularly to a method and system for creating a transportation plan for collecting and delivering a large number of locations using a plurality of transportation trucks. .

[0002]

2. Description of the Related Art As a transportation schedule creation technology for a physical distribution system, a technology has already been developed for creating an efficient tour plan for a customer existing in a certain area, which corresponds to the so-called traveling salesman problem or vehicle routing problem. (For example, Japanese Patent Laid-Open No. 5-135070 “delivery scheduling device”).

Further, for example, in Japanese Unexamined Patent Publication No. 10-124588, "Scheduling device based on geographic information", an optimal schedule is created by using a neural network together with geographical information to determine the shortest route between customers. There is. Japanese Unexamined Patent Publication No. 8-115495 “Automatic vehicle allocation device” realizes a method of clustering a large number of delivery destinations and creating a transportation route in each cluster. Furthermore, in Japanese Unexamined Patent Publication No. 9-305669, “Delivery Planning Method and Device”, a so-called sweep method (Tadayuki Masui et al. The technique is based on "OR of Logistics" Maki Shoten 1998). Furthermore, algorithms such as the 2opt method and the LK method have been developed as a method for creating a small-scale transportation route at high speed (Toshihide Ibaraki, "Discrete optimization method and algorithm", published by Iwanami Shoten in 1993). .

[0004]

DISTRIBUTION SCHEDULING FOR TRUCK TRANSPORT AND OTHER PROBLEM TO BE SOLVED BY THE INVENTION In logistics scheduling for truck transportation, etc., cargo is transported accurately at a designated time, and equipment such as trucks and personnel involved in operation and facilities such as logistics centers are used. It aims to improve efficiency and reduce costs.

In general, transportation by truck is generally unidirectional from a distribution center to a customer, and the scheduling preparation method mentioned in the prior art is such a delivery from the distribution center to a customer. Many of them handle only one of the collection from the customer to the distribution center. However, it is difficult to sufficiently improve the efficiency of physical distribution mainly by truck transportation by unidirectional transportation. Therefore, it is necessary to transport the packages from the customer to the distribution center and the packages from the distribution center to the customer in a mixed manner.

[0006] For example, in Japanese Unexamined Patent Publication No. 8-115495, "Automatic Vehicle Distributor", clustering based on the load capacity of trucks is used in order to determine the area range for each destination vehicle. However, this method cannot handle the mixed transportation of the two, because it is not possible to distinguish between the volume of parcels delivered from the distribution center to the customer and the volume of parcels transported from the customer to the distribution center.

Further, Japanese Patent Laid-Open No. 9-305669, "Delivery Planning Method and Device", can handle bidirectional transportation between a distribution center and a delivery destination. However, since the sweep method is used to create a route for truck transportation, the efficiency of the created transportation route itself is low, and there is a problem that sufficient improvement in transportation efficiency cannot be realized. Furthermore, Just In Time
There is also a problem that the sweep method cannot be used to create a route for truck transportation that corresponds to the transportation that specifies the time required for delivery and supply chain management.

Further, when carrying out bidirectional transportation between the distribution center and the customer's destination, due to the structure of the truck, all the parcels from the distribution center to the customer's destination must be delivered before the customer's destination reaches the distribution center. There are many cases where you cannot load your luggage. Under such conditions, the delivery destinations delivered from the distribution center are often traced back to the distribution center in reverse order. There is a problem that such a transportation route cannot be created by the simple sweep method.

The present invention makes it possible to mix luggage transportation from a distribution center to a delivery destination and luggage transportation from a collection destination to a distribution center, and to efficiently comply with time restrictions at the vehicle collection and delivery destination. An object of the present invention is to provide a transportation plan creation method and system for creating a detailed transportation schedule.

[0010]

In order to achieve the above object, the invention according to claim 1 uses a system having at least an input / output device, a processing device, and a storage device to form a distribution center and a collection / delivery destination base. A transportation plan creation method for creating a transportation schedule for a vehicle that collects and delivers parcels between items, including a base information registration step for inputting base information which is position information of a distribution center and a collection / delivery destination base, and a parcel for each parcel A package information registration step of inputting package information including quantity, collection / delivery type, collection / delivery base, and delivery time constraint,
A transportation vehicle information registration step of inputting transportation vehicle information including limit information of the load capacity of the transportation vehicle, a transportation schedule creation step of creating a transportation schedule of the transportation vehicle from the information input by the step, and outputting the created transportation schedule. And a transportation schedule output step for performing the same.

According to a second aspect of the present invention, in the transportation plan creating method according to the first aspect, the transportation schedule creating step includes an initial schedule creating step of creating an initial group of transportation schedules of a plurality of transportation vehicles,
It is characterized in that a transportation schedule is created by using a genetic algorithm including a schedule improvement step of improving the created initial schedule population by a genetic operation.

According to a third aspect of the present invention, in the transportation plan creating method according to the second aspect, the transportation schedule creating step is an ID for uniquely identifying a package to be collected and delivered.
Is to create a transportation schedule by a genetic algorithm that uses a genetic structure that is one-dimensionally arranged in a circular order of vehicles used for transportation, the genetic algorithm includes a crossover process, and the crossover process randomly Two genes, the first gene and the second gene, are selected, the crossover position in the first gene is randomly determined, and the transportation schedule recorded before the crossover position of the first gene is determined. The destination of collection and delivery of the package in the second gene which is not included in the transportation schedule is inserted into the transportation schedule by the NI (Nearest Insertion) method according to the order.

According to a fourth aspect of the present invention, in the transportation planning method according to the second aspect, the genetic algorithm includes a mutation process, and in the mutation process, a randomly selected gene is recorded. Select one package,
In addition, one random distance is selected, and the package of the collection / delivery destination within the random distance from the collection / delivery destination base of the selected package is deleted from the selected gene and is inserted again by the NI method. To do.

The invention according to claim 5 is the invention according to claim 3 or 4.
In the transportation planning method according to any one of
The process of inserting the collection / delivery destination into the transportation schedule by the NI method is performed from the distribution center loaded on each vehicle at each collection / delivery destination base in the transportation schedule to the delivery destination and from the collection base to the distribution center. The step of calculating the vehicle load capacity at the time of departure from each base from the baggage load of the vehicle, the maximum value of the vehicle load capacity at the time of departure from the base that is located ahead of the base in the transportation route of that vehicle, and the The step of pre-calculating the maximum value of the vehicle loading capacity at departure at the base located on the rear side of the transportation route and storing it in the storage device, and storing it in the storage device when a new collection / delivery destination is inserted into the transportation route Calculate the sum of the maximum value of the vehicle loading capacity at the rear of the transportation route and the collection amount at the collection and delivery destination, and that value does not exceed the vehicle loading amount upper limit, and the transportation route stored in the storage device. Forward Calculate the sum of the maximum value of the vehicle loading capacity of the vehicle and the delivery amount to the collection / delivery destination, check that the value does not exceed the vehicle loading upper limit, and only insert it at a point in the transportation route that does not exceed the upper limit And a step of performing.

According to a sixth aspect of the present invention, in the transportation plan creating method according to the fifth aspect, the process of creating the transportation schedule using the NI method adds a new package collection / delivery destination base to the transportation schedule. A step of recalculating the maximum value of the vehicle loading capacity in the front and rear of the transportation route at each of the collection and delivery destination points on the transportation route of the vehicle regarding the transportation vehicle to be added, and storing it in the storage device. It is characterized by including.

The invention according to claim 7 is the invention according to claim 3 or 4.
In the transportation planning method according to any one of
According to the NI method, the process of inserting the collection / delivery destination into the transportation schedule is based on the arrival time of the vehicle and the designated collection / delivery time at each collection / delivery destination base on the transportation route of one vehicle. Calculating the grace time, which is the time allowed for the arrival delay to, and calculating the minimum value of the arrival delay time at the base and the collection and delivery destination base behind the base on the transportation route, Storing the minimum value of the arrival delay time in a storage device for each collection / delivery base;
When a new collection / delivery destination base is inserted in the transportation route of the vehicle by the NI method, the arrival time of the vehicle at the collection / delivery destination immediately after the inserted collection / delivery destination is stored in the storage device by this new collection / delivery destination insertion. The step of inserting a new collection delivery destination at that point in the transportation route is included only when the minimum arrival delay time is not exceeded.

According to an eighth aspect of the present invention, in the transportation plan creating method according to the seventh aspect, the process of inserting the collection / delivery destination into the transportation schedule by the NI method inserts a new collection / delivery destination base into the transportation route. At that time, the grace time of the vehicle arrival delay at each of the collection / delivery bases on the transportation route regarding the inserted vehicle is recalculated and stored in the storage device.

The invention according to claim 9 has at least an input / output device, a processing device, and a storage device, and creates a transportation schedule of a vehicle for collecting and delivering packages between a distribution center and a collection / delivery destination base. A transportation planning system,
Location information registration means to enter location information, which is the location information of the distribution center and collection / delivery destination location, and package information including the amount of each package, collection / delivery location, collection / delivery location, and delivery time constraint Package information registration means, transportation vehicle information registration means for inputting transportation vehicle information including limit information on the load capacity of transportation vehicles, and transportation schedule creation means for producing a transportation schedule of transportation vehicles from the information input by the means. , And a transportation schedule output means for outputting the created transportation schedule.

According to a tenth aspect of the present invention, in the transportation plan creation system according to the ninth aspect, the transportation schedule creation means includes an initial schedule creation means for creating an initial group consisting of transportation schedules of a plurality of transportation vehicles; The transportation schedule is created using a genetic algorithm including a schedule improving means for improving the initial schedule population by genetic manipulation.

[0020]

BEST MODE FOR CARRYING OUT THE INVENTION Embodiments of the present invention will be described below with reference to the drawings. In the present embodiment, it is assumed that a plurality of vehicles (trucks, etc.) belonging to a distribution center depart from the distribution center and create a transportation plan for a plurality of vehicles that transport luggage to a plurality of collection / delivery destinations.

FIG. 1 is an overall configuration diagram of the system of this embodiment. This system includes an input device (101), an output device (102) such as a printer, and a display device (10) such as a display.
3), a processing device (104), and a storage device (109). The processing device (104) executes a series of programs (105) including an input processing unit (106), a transportation schedule generation unit (107), and a result output unit (108). In addition, the storage device (109), base information (110), baggage information (111), transportation vehicle information (112), distance table (113), road map (114), and transportation route information created from these (115) is stored. In addition, a load capacity table (116), a delay grace time table (117), gene information (118) and vehicle management information (119) used for creating a transportation route are also stored.

2 to 10 show a storage device (109) in FIG.
An example of the information stored in is shown.

FIG. 2 shows an example of the base information (110) in FIG. The location information (110) stores the location information of the distribution center or the delivery destination location of the package. Specifically, a base ID (201) that uniquely identifies a distribution center or a collection / delivery destination base, a base name (202), a base address (203), and a latitude indicating the base position.
(204) and longitude (205) are stored.

FIG. 3 shows an example of the package information (111) shown in FIG. In the package information (111), information on each package to be transported is stored. Specifically, a package index (301) that is sequentially assigned to each package from 1, a package ID (302) that uniquely identifies the package, packages to be sent from the distribution center to the delivery destination, or packages from the collection / delivery base to the distribution center. Delivery / delivery (303) that indicates whether the package is to be shipped, and the base ID (3
04), the amount of cargo (305), and the time constraint (306) indicating the deadline time for package collection and delivery at the collection / delivery destination are stored.

FIG. 4 shows an example of the transportation vehicle information (112) in FIG. The transport vehicle information (112) stores information about the capacity and limit of the available vehicle loading weight and the operating time of the vehicle. Specifically, transport vehicle information (11
In 2), the vehicle number (4
01), transport vehicle type ID (40
2), the payload limit for that type of vehicle (403), and
The departure time (404) of the vehicle from the distribution center and the end time (405) of finishing the delivery work are stored.

FIG. 5 shows an example of the distance table (113) in FIG. The distance table (113) stores information related to the travel distance and time of a transportation vehicle such as a truck between bases, which is necessary for creating a transportation schedule. Specifically, the base ID 1 (501) for the departure base and the base ID 2 (50 for the arrival base)
2) and the distance (503) between the bases and the travel time (504) between the bases are stored.

FIG. 6 shows an example of the loading amount table (116) in FIG. The load capacity table (116) is a table created for each vehicle when the transportation schedule is created. This table stores the maximum front load and the maximum rear load required to calculate the loadable amount of the collected luggage and the delivered luggage at each collection / delivery destination base of each vehicle. The maximum forward load capacity stores the maximum load capacity to be loaded on the vehicle before the vehicle leaves the distribution center and arrives at the base. In other words, it is not possible to load the delivery baggage to the site on the vehicle due to the difference between the maximum loading capacity of the vehicle and the maximum loading capacity at the front. Similarly, the maximum rear load capacity stores the maximum load capacity to be loaded on the truck from the departure of the base to the return to the distribution center. In other words, it is not possible to load the cargo to be collected at that location on the vehicle, because of the difference between the maximum loading capacity of the vehicle and this maximum rear loading capacity.

Specifically, the load table (116) includes a vehicle number (601) for identifying a vehicle, the number of patrol bases (602) assigned to the vehicle, and a patrol destination for the number of patrol bases. Location ID (603), maximum forward loading capacity at that location (60
4) and the maximum rear load (605) are stored. Note that the base ID 0 (603) is the base ID of the distribution center where the vehicle departs. In other words, the maximum load capacity at the front 0 (604) is set to the load capacity at the time of departure from the distribution center, and the maximum load capacity at the rear is 0 (605).
Is set to the maximum load when the vehicle leaves the distribution center and returns to the distribution center again. Subsequent patrol bases have base IDs of 1
(606), ..., Base IDN (609), and the maximum front loading capacity of each base is represented by the maximum front loading capacity 1 (607), ..., Maximum front loading capacity N (610), and the rear of each base. The maximum load capacity is the maximum rear load capacity 1 (608), ..., The maximum rear load capacity N (611).
It is represented by.

FIG. 7 shows the delay grace time table (1
17) shows an example. The delay time table (117) is a table created for each vehicle when creating a transportation route. This stores a grace time indicating how long the arrival time at the collection / delivery destination base of each vehicle is, the time restrictions at the traveling bases after that base are kept. Specifically, the vehicle number (701) that uniquely identifies the vehicle, the number of collection and delivery destination patrol bases that the car patrols (702), the base ID 1 (703) of the base 1, and the grace time 1 (7)
04), the base ID 2 (705) and the grace time 2 (706) of the base 2, and the base ID N (707) and the grace time N (708) of the base N are stored.

FIG. 8 is a transportation schedule creating section in FIG.
An example of the gene expression used in the calculation of the transportation route by the genetic algorithm used in (107) will be shown. The gene includes the luggage index of luggage delivered by transportation vehicles such as trucks.
0 (801) to index N (806) are stored respectively.

FIG. 9 shows an example of the vehicle management information (119) shown in FIG. The vehicle management information (119) stores information indicating the correspondence of the gene information (118) used by the transportation schedule creation unit (107) to the vehicle. The gene information (118) is simply a one-dimensional array of luggage indexes, and which luggage is loaded in which vehicle is not recorded. In this vehicle management information (119), the luggage loaded in each vehicle is
Vehicle management information is stored for each vehicle in order to indicate which part of the gene information (118) corresponds. In particular,
A vehicle number (901) that uniquely identifies the vehicle, a vehicle type (902), a gene start position (903) that indicates the start position on the gene of the luggage loaded in the vehicle, and a gene end position (90) that indicates the end position.
Store 4).

FIG. 10 shows the transportation route information (115) in FIG.
An example is shown. The transportation route information (115) stores information indicating a location where a vehicle circulates in a transportation schedule created by the method according to the present invention and a traveling order of the vehicle. Specifically, the vehicle number (1001), the time of departure from the vehicle's distribution center (1002), and the number of patrol bases for collecting and delivering (100
3), the package index (1004), the site ID (1005) of the collection / delivery destination site of the package, and the arrival time (1006) at the site are stored. In addition, the total collected baggage (1007) and delivered baggage (100
8), the total mileage (1009) is stored.

FIG. 11 is a flow chart showing an outline of the program (105) executed by the processing device (104) in FIG. First, in the input processing step (1101), the site information (11
The user inputs information necessary for creating a transportation plan such as 0), baggage information (111), and transportation vehicle information (112).
Details of this processing will be described later with reference to FIG. In the next transportation schedule creation step (1102), the input processing step (110
From the information obtained in 1), determine the optimal transportation plan. Here, the determination of the transportation plan means deciding the allocation of the packages to the truck and the schedule (transportation schedule information) that the truck capable of delivering all the inputted packages to the delivery destination. Details of this processing will be described later with reference to FIG. Next, in the result output processing step (1103), the created transportation schedule is output together with its evaluation. Details of this processing will be described later with reference to FIG.

FIG. 12 shows the input processing step (1
It is a flow chart which shows the details of (101). First, in the base information registration step (1201), the base information (110) shown in FIG.
To register. Next, in the package information registration step (1202), the package information (111) shown in FIG. 3 is registered. Next, in the transportation vehicle information registration step (1203), the transportation vehicle information shown in FIG.
Register (112). Next, distance table generation processing (1204)
Then, the distance between the bases and the travel time required by the truck are calculated with reference to the road map (114), and the distance table (113) shown in FIG. 5 is generated. It is assumed that the road map (114) is prepared in advance to store roads used in car navigation, connection relationships between the roads, and distance information.

FIG. 13 is a flow chart showing details of the transportation schedule creation process (1102) in FIG. The transportation schedule creation processing complies with GA (genetic algorithm). First, a group of different initial solutions is created by a method including randomness (step 130).
1). Details of this processing will be described later with reference to FIG. Then the variable I
1 is substituted for (step 1302). Next, the solution population is improved by genetic processing (step 1303). Details of this processing will be described later with reference to FIG. At least a part of the solution group is changed by the improvement processing of the solution group, and the step 1303 is performed.
The solution group gradually approaches the optimum solution by repeating the processing of ~ 1305. After the processing of step 1303, the solution group is evaluated (step 1304). The evaluation is performed on various factors such as the mileage of the vehicle calculated from the route information and the number of vehicles used, and the results rank the individual solutions in the solution group. The solution with the higher evaluation result is selected as the next-generation solution (step 1305). Next, 1 is added to the variable I (step 1306).

It is checked whether the variable I exceeds a preset number of processing generations G_MAX of GA or whether the evaluation value of the best solution in the next-generation population is better than a preset target value (step 1307). ). If the condition is satisfied, the best solution in the current solution group is output (step 1308), and the process ends. If the condition is not satisfied in step 1307, step 13
Return to 03 and repeat improvement of the solution group by genetic processing.

FIGS. 14 and 15 show NI (used in the initial solution group creation (step 1301) of the transportation schedule creation process using the genetic algorithm of FIG. 13 and the improvement of the solution group by the genetic processing (step 1303). It is a flow chart which shows details of transportation route construction processing by the Nearest Insertion method. The details of the initial solution group creating process (step 1301) and the solution group improving process (step 1303) are described in FIGS.
See below.

The process according to the NI method is a process of adding the transportation of one package to the collection / delivery destination base in the transportation schedule of a plurality of transportation vehicles. In this process, gene information (118) corresponding to the transportation schedule and vehicle management information
Suppose (119) is built. It is also assumed that the load capacity table (116) and the delay grace time table (117) are constructed in advance for each vehicle. A method of constructing both tables will be described later with reference to FIGS.

First, the collection / delivery destination base ID of the package to be inserted to which a new transportation schedule is to be assigned is assigned to the variable P.
(Step 1401). Next, the load amount of the load to be inserted is substituted into the variable L with reference to the load information (111) (step 1402).
Next, assign 0 to the variable Truck and 0 to Pos (step 140
3). Also, for the variable D_MIN, the maximum number M that can be represented by the computer is
Set ax_Value and assign 1 to variable I (step 140
Four).

Next, the variable I is compared with the value of the variable Max_Truck representing the number of trucks already assigned to the transportation schedule (step 1405). The value of the variable I is set to Max in order to determine the optimum base insertion position for luggage transportation in the transportation route of the vehicle for which the transportation route has already been created.
While it is smaller than _Truck, the processes of steps 1406-1423 are repeated.

First, referring to the vehicle management information (119), the gene start position of the I-th vehicle is taken out and assigned to the variable J (step 1406). Next, in the transportation route of the I-th vehicle, it is determined by the following procedure whether the transportation to the site P can be assigned for new cargo transportation.

First, it is determined whether or not the value of the variable J exceeds the gene end position +1 of the I-th vehicle in the vehicle management information (119) (step 1407). If the value of the variable J does not exceed the gene end position of the I-th vehicle + 1, it is determined whether the variable J is equal to the start gene position of the I-th load (step
1408). When the variable J is equal to the start gene position of the I-th package, the base ID of the distribution center is substituted into the variable P1 (step 1410). If the variable J is different from the start gene position of the I-th package, the ID of the collection / delivery destination base of the (J-1) -th package of the I-th vehicle is substituted (step 1409).

Next, the value of the variable J is compared with the gene end position +1 of the I-th vehicle (step 1411). If both are the same, set the base ID of the distribution center in variable P2
(Step 1413). If different, the ID of the collection / delivery destination base of the J-th package of the I-th vehicle is substituted into the variable P2 (step 1412).

Next, it is determined whether the package to be newly added to the transportation schedule is a package to be delivered from the distribution center to the delivery destination or a package to be transported from the collection / delivery destination to the distribution center (step 1414). In the case of collected luggage, the rear maximum load amount at the Jth destination site is substituted for the variable M by referring to the load amount table (116) of the Ith vehicle (step 1415). In the case of delivered parcels in step 1414, the maximum forward loading amount is substituted into the variable M (step 1416).

Next, the sum of M and L is the maximum load capacity of the vehicle.
It is checked whether it is below LMax (step 1417). If the sum of M and L exceeds the maximum load capacity, it is determined that the luggage cannot be inserted into the Jth position of the Ith vehicle, the process proceeds to step 1422, and the variable J is set to 1 Then, the procedure returns to step 1407, and the possibility of inserting the I-th vehicle at the next point is checked.

If it is determined in step 1417 that the load is less than or equal to the maximum load amount, then it is checked whether or not the collection / delivery time constraint is satisfied by the insertion. First, referring to the distance table (113), the required travel time between bases P1 and P and the bases P and P2
The required travel time between the bases P1 and P2 is subtracted from the sum of the required travel times between them and is substituted into the variable T (step 1418). Where Time
(X, Y) represents the time required to move from the base X to Y.

Next, the value of the variable T is compared with the delay grace time at the collection / delivery destination base of the Jth package of the Ith vehicle (step 1419). If T is small, inserting the baggage will not break the time constraint at the other destination of the I-th vehicle. At that time, the value of T and the value of the variable D_MIN are compared (step 1420). If the value of T is small, the value of T is set to D_MIN because the increment of the running time of the vehicle is the smallest among the insertion positions that have satisfied the load capacity constraint of the vehicle and the time constraint of the tour destination base so far. , I values are substituted for Truck and J values are substituted for Pos (step 1421) and the process proceeds to step 1422.
If the conditions are not satisfied in steps 1419 and 1420, the process proceeds to step 1422, 1 is added to the variable J (step 1422), the process returns to step 1407 to check the next insertion point.

When J exceeds the end position of the gene of the I-th vehicle in step 1407, 1 is added to I (step 142
3), returning to step 1405, the next vehicle insertion process is performed.

If I exceeds the value of Max_Truck in step 1405, the possibility of insertion into all routes of the transportation route of the existing vehicle has been checked, so the following processing is performed to transport the package. The vehicle to be operated and the insertion position of the vehicle in the patrol route are determined.

First, the values of the variables Truck and Pos are both 0.
It is checked (step 1501). If both variables are 0, there are no existing vehicles that can be inserted while observing the load constraint and time constraint, so add a new vehicle.
(Step 1502). In other words, at the end of the gene information (118),
While adding the index of the baggage to be added, a new entry is added to the vehicle management information (119), and at the gene start position and end position of the vehicle management information (119), on the gene of the added baggage on the gene The position of is stored (step 1503). Next, added vehicle load table (116)
(Step 1504). Further, the delay grace time table (117) of the added vehicle is constructed (step 150).
Five). Next, 1 is added to Max_Truck (step 1506), and the process ends.

If both variables are not 0 in step 1501, a new package is inserted next to the Pos-th collection / delivery package of the Truck-th vehicle No. truck (step 150).
7). That is, add luggage to the above position in the gene information (118), add 1 to the gene end position of the Truckth vehicle, and add Truc
1 is added to the gene start position and end position of the vehicle management information (119) after the (k + 1) th vehicle. Next, the loading table (116) of the Truck-th vehicle is reconstructed (step 1508). Next, the delay grace time table (117) of the Truck-th vehicle is reconstructed (step 1509), and the process ends.

FIG. 16 is a flow chart showing the processing for creating the loading amount table (116). In this process, L []
Is an array used to store the amount of baggage loaded in the vehicle at the time of departure at each base, Lfor [] is the maximum front load at each base calculated in this process, and Lbak [] is the maximum rear load. An array that stores the quantity. That is, the value of this array is the value stored in the load table (116).

First, the number of patrol bases of the vehicle for which the load table (116) is created is substituted into the variable N (step 160).
1). Next, the total delivery load delivered from the distribution center by the vehicle is substituted into the variable TS (step 1602). Then L
Substitute the value of TS for [0] and 1 for variable I (step 16
03).

It is determined whether I is N or less (step 160
Four). If I is N or less, calculate the load of the truck at the departure of each collection / delivery destination. First, it is determined whether the I-th package to be transported by the vehicle is a collected package or a delivered package (step 1605).
In the case of a package to be collected, the load amount of the package is substituted into the variable R, and 0 is substituted into the variable S (step 1606). For delivered parcels, the variable R is 0, the variable S is the volume of the parcel,
Substitute (step 1607).

Next, L [I-1] + RS is calculated and substituted for the variable L [I] (step 1608). Then add 1 to the variable I
(Step 1609) returns to step 1604 to process the next package.

If I exceeds N in step 1604,
The load capacity of each truck at each destination is array L []
Since it has been stored in, the maximum front load and the maximum rear load are obtained. First, 1 is assigned to the variable I (step 1610). Then check if I does not exceed N (step 1
611). If I does not exceed N, the maximum value of L [] from the Ith to the Nth is obtained and substituted for Lfor [I] (step 1612). Next, the maximum value of L [] from the 0th to the Ith is obtained and substituted for Lbak [I] (step 1613). Then in variable I
Add 1 (step 1614) and return to step 1611 to continue processing. If the value of I exceeds N in step 1611, the process ends.

FIG. 17 is a flow chart showing the process of creating the delay grace time table (117). In this process,
T [] is an array that stores the arrival time of the vehicle at each base, and D [] is an array that stores the delay grace time at each base.

First, the variable N is substituted with the number of traveling bases of the vehicle for which the load table (116) is to be created (step 170).
1). Next, referring to the transportation vehicle information (112), the departure time of the distribution center of the vehicle is set in T [0], and 1 is substituted into the variable I (step 1702).

Next, it is checked whether I is N or less (step 1703). If I is less than or equal to N, the distance table shows the travel time between T [I-1] and the (I-1) th package collection / delivery destination of the truck and the Ith package collection / delivery destination base. And add the value obtained by referring to and set the result to T [I] (step
1704). Next, 1 is added to the variable I (step 1705), the process returns to step 1703 to continue the processing.

If I exceeds N in step 1703,
Since the arrival time of the vehicle at each destination location has been obtained in the array T [], the delay grace time at each location is obtained next. First, delay grace time D [N] at the final destination of the vehicle
Is calculated by subtracting the value of T [N] from the constraint time at that base
(Step 1706). Next, subtract 1 from I (step 1707).

Next, it is checked whether I is 1 or more (step 1708). When I is 1 or more, the variable D is I of the vehicle.
Obtain and substitute the delay grace time at the delivery / delivery destination of the th package. Specifically, it is obtained by subtracting the arrival time T [I] at the base from the constraint time of the I-th bag (step 170).
9). Next, delay delay time D [I] for the I-th shipment
As a value, the smaller value of the value of the variable D and the value of D [I + 1] is substituted (step 1710). Next, the value of the variable I is decremented by 1 (step 1711), the process returns to step 1708 to continue the processing. In step 1708, if I reaches 0, the process ends.

FIG. 18 shows the initial solution group generation process in FIG.
It is a flowchart showing details of (1301). One solution is a traveling schedule for multiple vehicles (transport route)
Are registered, and the solution group further includes a plurality of solutions. When this process is executed, the number of individuals of genes to be registered in the initial population is set in advance in the variable SIZE. In addition, the number of packages (delivery destination bases) registered in the route is set in the variable LEN.

First, 1 is assigned to the variable I (step 180
1). Then while I is less than SIZE, steps 1803 to 180
Repeat the process of 6 to generate the number of genes specified by SIZE. First, an area for storing an empty gene in which no value is set for each element is generated (step 1803). Next, an array T [] in which the packages of the package information (111) are rearranged in a random order is created (step 1804). Then, the empty gene is loaded with the packages registered in the sequence T [] in this order.
Sequential insertion is performed by the insertion method shown in FIG.
Five). Next, add 1 to the variable I (step 1806), and step 18
Returning to 02, genes are sequentially generated.

FIG. 19 is a flow chart showing details of the improvement (step 1303) of the solution group in FIG. Genetic processing is the central processing of GA that changes and improves the solution population.

First, the number of solutions registered in the solution group is substituted into the variable SIZE (step 1901). Then C_NUM, S
The maximum integer that does not exceed the value obtained by multiplying the preset intersection ratio is substituted for IZE (step 1902). Next, 1 is assigned to the variable I (step 1903). It is determined whether I exceeds C_NUM (step 1904). If I is less than or equal to C_NUM, the processing of steps 1904 to 1907 is repeated to perform the intersection processing. The intersection process is a process of creating a new solution that inherits the properties of the two different solutions. Whether or not the intersection process is performed is randomly determined with a predetermined probability. When performing crossover processing, two solutions (genes) that differ from the solution population
(Step 1905) and select the two selected solutions (genes)
Based on the above, intersection processing is performed, and the solution (gene) newly generated by the intersection processing is added to the solution group (step 1906). Details of this intersection processing will be described later with reference to FIG. Next, 1 is added to I (step 1907), the process returns to step 1904, and the next crossover process is performed.

If I exceeds C_NUM in step 1904, the mutation process is started. The mutation process is a process of partially changing one solution to create a new solution. Whether or not the mutation process is performed is also randomly determined with a predetermined probability. When performing mutation processing, the variable M_NUM
Substitute the maximum integer that does not exceed the value obtained by multiplying SIZE by the preset mutation rate (step 1908), and set variable I to 1
Is substituted (step 1909).

Next, it is determined whether I exceeds M_NUM (step 1910). When I is less than or equal to M_NUM, the processing of steps 1910 to 1913 is repeated to perform the mutation processing.
First, one solution (gene) is randomly selected from the solution group (step 1911). Next, the selected solution is subjected to mutation processing, and the solution (gene) is added to the solution group (step 1912). Details of this processing will be described with reference to FIG. Subsequently, 1 is added to the variable I (step 1913) and the process returns to step 1910 to continue the mutation process.

FIG. 20 is a flow chart showing the details of the intersection processing in step 1906 of FIG. First, the variable N
The number of packages registered in each solution (gene) is substituted into (step 2001). Next, next, two solutions (genes) to be crossed are copied to the arrays G1 [] and G2 []. (Step 20
02). Next, in order to determine the intersection, an integer of 2 or more and N-1 or less is randomly generated and assigned to the variable P (step 20
03). Next, the P-th and subsequent package data indexes of G1 are deleted (step 2004). That is, -1 is assigned to the element to be deleted. Next, 1 is assigned to the variable I (step 2005).

Next, while I is equal to or less than N, steps 2007 to 20
Repeat the process of 09. First, it is determined whether G2 [I] is included in the array G1 [] (step 2007). If it is not included, the delivery destination of the package of the package index represented by G2 [I] is added to G1 (step 2008). This processing is performed by the processing procedure of the NI method shown in FIG. Then add 1 to the variable I (step 2009) and return to step 2006,
Repeat the process. If G2 [I] is included in the array G1 [] in step 2007, add 1 to the variable I without adding the delivery destination of the package of the package index represented by G2 [I] to G1 (step 2009), return to step 2006 and repeat the process. When I reaches N in step 2006, the process ends.

FIG. 21 is a flow chart showing details of the mutation process of step 1912 of FIG. Variable in advance
Set the gene size in N.

First, a solution (gene) randomly selected as a mutation target is copied to the array G [] (step 21).
01). Next, the distance between the distribution center and the farthest delivery destination base from the distribution center is substituted into the variable L (step 2102). A real number between 0 and 0.3 is randomly selected and set as the variable R (step 2103). Next, the variable L1
The product of the value of R and the value of R is substituted (step 2104). Next, to determine the mutation site on the gene, set the variable P to 1 or more.
A positive integer less than or equal to N is randomly selected and substituted (step 2105). Next, 1 is assigned to the variable I and 0 is assigned to the variable J (step 2106). The value of G [P], that is, the index value of the package information is substituted for the variable K (step 2107).

While the value of I is N-1 or less, steps 2109 to 21
Repeat the process of 12. First, the distance between the collection / delivery destination base for packages with index K and the delivery / delivery base position for packages with G [I] is L1.
It is determined whether it is below (step 2109). If the condition is met, substitute the value of G [I] for T [J] and -1 for G [I].
(Step 2110). Then add 1 to J (step 2111) and add 1 to I.
Is added (step 2112) and the process returns to step 2108 to continue the processing. If the value of I exceeds the value of N in step 2108, the element with a value of -1 in the array G is deleted and the elements are padded forward. At this time, if there is a flight that does not carry any luggage in the transport flight index, it is deleted (step 2113).
Next, the packages of J package indexes stored in the array T are added to G (step 2114). This additional processing uses the sequential NI method shown in FIG. When the addition processing is completed, the mutation processing is ended.

FIG. 22 is a flow chart showing details of the result output processing of step 1103 of FIG. First, the passing points of each transportation vehicle are displayed on the map (step 2201). Next, a table and a graph are created and displayed together with the schedule obtained by the transportation schedule creation process and the values such as the number of required vehicles and mileage (step 2202). next,
If there is a print instruction, print the graph or table (step 2
203).

[0074]

As described above, according to the present invention,
Efficiently using multiple vehicles, even if there is a constraint on the arrival time of the vehicle from the distribution center to the delivery destination base and from the collection and delivery destination base to the distribution center It is possible to create a transportation schedule for carrying out various types of transportation.

[Brief description of drawings]

FIG. 1 is an overall configuration diagram showing a system according to an embodiment.

FIG. 2 is a diagram showing an example of a configuration of base information.

FIG. 3 is a diagram showing an example of a configuration of package information.

FIG. 4 is a diagram showing an example of a configuration of transportation vehicle information.

FIG. 5 is a diagram showing an example of a distance table of a base question.

FIG. 6 is a diagram showing an example of a loading amount table.

FIG. 7 is a diagram showing an example of a delay grace time table.

FIG. 8 is a diagram showing an example of gene expression of a transportation route.

FIG. 9 is a diagram showing an example of vehicle management information.

FIG. 10 is a diagram showing a structure of transportation route information.

FIG. 11 is a flowchart showing an overall processing flow.

FIG. 12 is a flowchart showing details of input processing.

FIG. 13 is a flowchart showing details of transportation schedule creation processing.

FIG. 14 is a flowchart (part 1) of a process of adding a collection / delivery destination base to a transportation schedule using the NI method.

FIG. 15 is a flowchart (part 2) of the process of adding the collection / delivery destination base to the transportation schedule using the NI method.

FIG. 16 is a flowchart showing details of a load amount table creation process.

FIG. 17 is a flowchart showing details of delay grace time table creation processing.

FIG. 18 is a flowchart showing details of initial group generation processing.

FIG. 19 is a flowchart showing an outline of genetic processing.

FIG. 20 is a flowchart showing details of intersection processing.

FIG. 21 is a flowchart showing details of mutation processing.

FIG. 22 is a flowchart showing details of result output processing of the present invention.

[Explanation of symbols]

101 ... Input device, 102 ... Output device, 103 ... Display device, 104 ... Processing device, 105 ... Program, 106 ...
Input processing unit, 107 ... Transportation schedule creation unit, 108
... Result output unit, 109 ... Storage device, 110 ... Base information,
111 ... Luggage information, 112 ... Transport vehicle information, 113 ... Distance table, 114 ... Road map, 115 ... Transport route information, 116 ... Load table, 117 ... Delay grace time table, 118 ... Gene information, 119 ... Vehicle management information .

Continued front page    (72) Inventor Sen Kubota             6-81 Onoe-cho, Naka-ku, Yokohama-shi, Kanagawa             Hitachi Software Engineering Stock Association             In-house (72) Inventor Takuya Maekawa             6-81 Onoe-cho, Naka-ku, Yokohama-shi, Kanagawa             Hitachi Software Engineering Stock Association             In-house

Claims (10)

[Claims] 1. A transportation plan creation method for creating a transportation schedule of a vehicle for collecting and delivering a package between a distribution center and a collection / delivery destination base using a system having at least an input / output device, a processing device, and a storage device. In addition, it includes a base information registration step for inputting base information, which is the positional information of the distribution center and the collection / delivery destination base, and the package amount of each package, collection / delivery type, collection / delivery destination base, and delivery time constraint. A package information registration step of inputting package information, a transport vehicle information registration step of entering transport vehicle information including limit information on the load capacity of the transport vehicle, and a transport schedule of the transport vehicle created from the information input in the step. Transportation comprising a schedule creation step and a transportation schedule output step for outputting the created transportation schedule Image creation method. 2. The transportation plan creation method according to claim 1, wherein the transportation schedule creation step includes an initial schedule creation step of creating an initial group of transportation schedules of a plurality of transportation vehicles, and the created initial schedule group. A transportation plan creation method, wherein a transportation schedule is created using a genetic algorithm including a schedule improvement step of improving by a manual operation. 3. The transportation plan creating method according to claim 2, wherein the transportation schedule creating step arranges IDs for uniquely identifying packages to be collected and delivered in a one-dimensional manner in a patrol order of vehicles used for transportation. The transport schedule is created by a genetic algorithm using the gene structure described above, and the genetic algorithm includes a crossover process, and the crossover process randomly selects two genes, a first gene and a second gene. Select and randomly determine the crossover position in the first gene, and for the transport schedule recorded before the crossover position of the first gene, in the second gene not included in the transport schedule In accordance with the order of the collection and delivery destinations of the parcels of
st Insertion) method for transport planning. 4. The transportation planning method according to claim 2, wherein the genetic algorithm includes a mutation process, and the mutation process selects one package recorded in a randomly selected gene. In addition, one random distance is selected, and the package of the collection / delivery destination within the random distance from the collection / delivery destination base of the selected package is deleted from the selected gene and is inserted again by the NI method. How to make a transportation plan. 5. The transportation plan creation method according to claim 3, wherein the process of inserting the collection / delivery destination into the transportation schedule by the NI method is performed by each collection / delivery destination base in the transportation schedule. In the step of calculating the vehicle load capacity at the time of departure from each distribution point from the distribution amount loaded from each distribution vehicle to the delivery point and from the collection point to the distribution center, The maximum value of the vehicle loading capacity at the time of departure from the base located in the front of the vehicle transportation route and the maximum value of the vehicle load at the departure of the base located at the rear of the vehicle transportation route are calculated and stored in advance. The step of storing in the device and, when inserting a new collection / delivery destination into the transportation route, calculate the sum of the maximum value of the vehicle loading capacity behind the transportation route stored in the storage device and the collection amount at the transportation destination. And that Does not exceed the vehicle load upper limit, and calculates the sum of the maximum value of the vehicle load in the front of the transportation route stored in the storage device and the delivery amount to the delivery destination, and the calculated value is the vehicle load. A method of making a transportation plan, comprising the step of checking that the upper limit is not exceeded and only inserting into points in the transportation route that do not exceed the upper limit. 6. The transportation plan creation method according to claim 5, wherein the process of creating a transportation schedule by using the NI method is a target to be added when a new collection / delivery destination base of the package is added to the transportation schedule. Regarding the transportation vehicle, the method further comprises the step of recalculating the maximum value of the vehicle loading amount in the front and rear of the transportation route at each collection and delivery destination point on the transportation route of each vehicle and storing the maximum value in the storage device. How to create a transportation plan. 7. The transportation plan creation method according to claim 3, wherein the NI method is used to insert the collection / delivery destination into a transportation schedule for each transportation route of one vehicle. At the collection / delivery destination base, the step of calculating a grace time, which is the time allowed for the arrival delay of the vehicle to that base, from the arrival time of the vehicle and the designated collection / delivery time, and on the transportation route, Calculating the minimum value of the arrival delay time at the site and the collection / delivery site behind the site, and storing the minimum value of the above arrival delay time in the storage device for each collection / delivery site; and the NI method. Therefore, when a new collection / delivery destination base is inserted into the transportation route of the vehicle, the arrival time of the vehicle at the collection / delivery destination immediately after the insertion / delivery destination is stored in the storage device by this new collection / delivery destination insertion. Exceeds the minimum delay time In the case have only, in the transportation route, transportation planning method, characterized in that it comprises the step of performing the insertion of the new collection and delivery destination to that point. 8. The method of creating a transportation plan according to claim 7, wherein the process of inserting the collection / delivery destination into the transportation schedule by the NI method is performed when a new collection / delivery destination base is inserted into the transportation route. A transportation plan creating method characterized by recalculating a grace time of a vehicle arrival delay at each collection / delivery base on a transportation route and storing the grace time in a storage device. 9. A transportation plan creation system, which has at least an input / output device, a processing device, and a storage device, and creates a transportation schedule of vehicles for collecting and delivering packages between a distribution center and a collection / delivery destination base. Location information registration means for inputting location information, which is the location information of the distribution center and the collection / delivery destination base, and package information including the package volume of each package, collection / delivery site, collection / delivery site, and delivery time constraint. And a transportation vehicle information registration means for inputting transportation vehicle information including limit information on the load capacity of the transportation vehicle, and a transportation schedule creation for creating a transportation schedule of the transportation vehicle from the information input by the means. A transportation plan preparation system comprising means and a transportation schedule output means for outputting the prepared transportation schedule. 10. The transportation plan creation system according to claim 9, wherein the transportation schedule creation means creates an initial schedule creation means for creating an initial population consisting of transportation schedules of a plurality of transportation vehicles, and the created initial schedule population. A transportation plan preparation system for preparing a transportation schedule by using a genetic algorithm including a schedule improving means for improving the transportation schedule by a physical operation.