JP4025652B2 - Transportation planning system and method - Google Patents

Description

[0001]
BACKGROUND OF THE INVENTION
The present invention relates to a transportation plan creation system and a transportation plan creation method for luggage and the like by vehicles in the physical distribution field.
[0002]
[Prior art]
As a transportation plan creation technique in a logistics system, a technique for creating an efficient traveling plan for a customer in a certain area, which corresponds to a so-called traveling salesman problem and a vehicle routing problem, is already known (for example, Patent Document 1).
[0003]
In addition, for example, a method for creating an optimal schedule by using a neural network for determining the shortest route between customer sites together with geographic information is also known (see, for example, Patent Document 2). In addition, clustering a large number of delivery destinations and creating a transportation route within each cluster (see Patent Document 3), creating an efficient dispatch plan while complying with the load capacity constraints and operating time constraints of trucks A method (see Patent Document 4) is known.
[0004]
[Patent Document 1]
JP-A-5-135070 “Delivery Scheduling Device”
[Patent Document 2]
Japanese Patent Laid-Open No. 10-124588 “Schedule Creation Device Based on Geographic Information”
[Patent Document 3]
Japanese Patent Laid-Open No. 8-115495 “Automatic Vehicle Allocation Device”
[Patent Document 4]
Japanese Patent Laid-Open No. 9-305669 “Delivery Planning Method and Apparatus”
[0005]
[Problems to be solved by the invention]
By the way, in a distribution center, which is a baggage accumulation place for transporting a large amount of luggage to a large number of delivery destinations using a limited number of trucks, etc., trucks are usually transported multiple times a day. There are many. Small distribution centers may operate only two flights in the morning and afternoon, but larger distribution centers are operated 24 hours a day, increasing the number of truck flights. In general, such a transportation schedule for vehicles such as trucks at a distribution center is created for each flight. Various schedules are planned so that the total travel distance of the transport vehicle can be minimized by using an optimization algorithm to reduce the transport cost. As a transportation schedule created in this way, a different schedule is created for each time zone. In other words, it is also assumed that the transport vehicle travels to different destinations depending on each time zone. When scheduling is performed using such a method, there is a problem that satisfactory scheduling cannot actually be performed.
An object of the present invention is to provide a scheduling system that is more realistic.
[0006]
[Means for Solving the Problems]
According to one aspect of the present invention, there is provided a transportation plan creation system for creating a transportation schedule for a vehicle that transports luggage while traveling between a distribution center and a delivery destination base, and calculates a transportation density for each time zone. Based on this, the transportation importance calculation means for each time zone for obtaining the transportation importance by time zone, and the most important time for creating a transportation schedule in the most important time zone obtained by the transportation importance calculation means for each time zone Transportation plan creation comprising: zone transportation schedule creation means; and route information transfer means for reflecting the transportation schedule in the most important time zone created by the most important time zone transportation schedule creation means in schedule creation in other time zones A system is provided.
[0007]
By using the above transport plan creation system, a schedule with a proper relationship between luggage and time is created in almost all time zones in order to create a transport schedule in other time zones based on the transport schedule in the most important time zone. It becomes possible to create an efficient transportation schedule.
[0008]
The most important time zone transportation schedule creation means includes means for creating a plurality of initial schedule proposals related to package delivery, and whether or not the schedule proposal can be improved by replacing the vehicle with respect to the initial schedule proposal. Select a decision plan, a measure to improve the schedule plan so that the loading rate is high for vehicles with a low load rate, and a schedule plan that reduces the number of vehicles and the total mileage. It is preferable to include means for performing processing for removing any of the similar schedule proposals. If such a method is used, the schedule creation process becomes efficient, and a transportation schedule plan can be created in a short time.
[0009]
DETAILED DESCRIPTION OF THE INVENTION
In an actual distribution center, a delivery destination group is often fixed and operated to some extent. This form is effective in that the proficiency level of the driving route of the driver of the vehicle is taken into account, and that it can be applied to the case where the transportation is directly performed between the delivery destinations without going through the distribution center. However, a schedule system to which various optimization techniques are applied has a problem that it is difficult to create a schedule for performing such operations.
[0010]
The transportation plan creation technique according to the present invention automatically creates a vehicle operation schedule of a distribution center that performs transportation in a plurality of time zones according to the following procedure. First, a time zone having the highest transport density is found among a plurality of time zones in which transportation is performed. Next, a vehicle operation schedule in that time zone is created, and the delivery destinations that patrol with the same vehicle are registered as one group according to the schedule. Schedule creation in other time zones is performed based on the delivery destination group. When a schedule is created in this way, a schedule in which the delivery destination groups that are transported by the same vehicle in each time zone are the same or similar can be created.
[0011]
Hereinafter, based on the above consideration, a transportation plan creation technique according to an embodiment of the present invention will be described with reference to the drawings. The transportation plan creation technique according to the present embodiment is based on the premise of creating a schedule in which a plurality of vehicles (trucks, etc.) belonging to a distribution center leave the distribution center and deliver packages to a plurality of locations as a transportation form. . The transportation plan creation technique according to the present embodiment can be applied not only to delivery from a distribution center, but also to transportation forms in which packages are collected from a collection destination to a distribution center, and collection and delivery are performed by a single vehicle. .
[0012]
FIG. 1 is a block diagram illustrating a configuration example of a transportation plan creation system according to the present embodiment. As shown in FIG. 1, the transportation plan creation system according to the present embodiment includes an input device 101, an output device 102 such as a printer, a display device 103 such as a display, a processing device 104, and a storage device 109. I have. The processing device 104 includes an input processing unit 106, a transportation schedule creation unit 107, and a result output unit 108. Processing can be performed by executing these series of programs 105.
[0013]
The storage device 109 also stores base information 110, time zone information 111, luggage information 112, transport vehicle information 113, distance information 114, road map 115, group information 116, and transport schedule information 117. Remember. In addition, information used for creating a transportation route, for example, gene information 118 and vehicle management information 119 is also stored.
[0014]
FIG. 2A to FIG. 2F are diagrams illustrating examples of information stored in the storage device 109 illustrated in FIG. The description will be continued with reference to FIG. As shown in FIG. 2A, the base information 110 includes location information of a distribution center and a package delivery destination base. More specifically, a base ID 201 for uniquely identifying a distribution center or a delivery destination base, a base name 202, a base address 203, a latitude 204 and a longitude 205 indicating the base position are stored.
[0015]
As shown in FIG. 2B, the time zone information 111 stores information on a plurality of time zones in which the distribution center is operated. Specifically, a time zone ID 206 that uniquely identifies the time zone, a transport start time 207 that represents the start time of the time zone, and a transport end time 208 that represents the end time are stored. As the time zone information 111, information corresponding to the number of time zones operated within one day (or a schedule management unit period, for example, a half day, a week, etc.) at the distribution center is stored. As shown in FIG. 2C, the package information 112 stores information related to each package to be transported. More specifically, for each package, a package ID 209 that uniquely identifies the package, a handling time zone ID 210 that indicates a time zone during which the package is transported, a delivery destination ID 211 that indicates the delivery destination of the package, and a delivery destination It includes a name 212, a delivery address 213, and a load 214.
[0016]
As shown in FIG. 2D, the transport vehicle information 113 stores information on the number of owned vehicle types that can be used. Specifically, a vehicle type ID 215 that uniquely identifies the vehicle type, a vehicle type name 216, the number of owned vehicles 217, and the upper limit 218 of the vehicle loading capacity are stored. As shown in FIG. 2 (e), the distance information 114 stores information related to the travel distance / time of a transport vehicle such as a truck between bases, which is necessary for creating a transport schedule. Specifically, ID1 (219) of the departure base, ID2 (220) of the arrival base, the distance 221 between the bases, and the travel time 222 between the bases are included. As shown in FIG. 2F, the group information 116 stores information on a group of delivery destinations that the vehicle circulates. Specifically, information including a group ID 223 that uniquely represents a group, the number of delivery destination bases 224 belonging to the group, and base IDs 225, 226,... Of delivery destination bases belonging to the group is stored.
[0017]
FIG. 3 is a diagram showing a configuration example of the transportation schedule information 117 created by the transportation schedule creation unit 107 of FIG. As shown in FIG. 3, the transportation schedule information includes information indicating a base where the vehicle circulates according to the transportation schedule created by the method according to the present embodiment and the order of circulation of the vehicle. Specifically, the vehicle No 301, the number of traveling locations 302 of the vehicle, the location ID 1 (303) of the collection destination location, the arrival time 1 (304) at the location, and the luggage to be transported to that location Index 1 (305) is stored for each site. Another base ID 2 (306) is also included. In addition, the total luggage amount 309 and the total travel distance 310 are stored.
[0018]
FIG. 4 is a diagram showing the structure of the gene information 118. As shown in FIG. 4A, an example of gene expression used in the transport schedule creation step by the genetic algorithm used in the transport schedule creation unit 107 is shown. It is. As gene information, package indexes 1 to N (401 to 403) of packages delivered by a transport vehicle such as a truck are stored. Here, the package index is information indicating the registered position of the package information in the table storing the package information.
[0019]
As shown in FIG. 4B, the vehicle management information 119 stores information indicating the correspondence relationship between the gene information 118 used in the transport schedule creation unit 107 and the vehicle. The gene information 118 is simply an array in which the package indexes are arranged one-dimensionally, and it is not recorded which package is loaded on which vehicle. The vehicle management information 119 stores the following information for each vehicle in order to indicate which part of the genetic information corresponds to the load loaded on each vehicle. That is, it has a vehicle No. 404 that uniquely identifies the vehicle, a gene start position 405 that indicates the start position on the gene of the load loaded on the vehicle, and a gene end position 406 that indicates the end position.
[0020]
FIG. 5 is a flowchart showing an outline of the flow of processing of the program 105 executed by the processing device 104 of FIG. First, in the input processing step 501, the user inputs information necessary for creating a transportation plan including the base information 110, the time zone information 111, the package information 112, and the like. Details of this processing will be described later with reference to FIG.
[0021]
In the next transportation schedule creation processing step 502, the transportation plan considered to be optimal is determined from the information obtained in the input processing step 501. The determination of the transportation plan is the allocation of the luggage loaded on the truck that can deliver all the input luggage to the delivery destination and the route (transport schedule information) that the truck circulates. Refers to making a decision. Details of this processing will be described later with reference to FIG. Next, in the result output process 503, the created schedule is output together with the evaluation value and the like. Details of this processing will be described later with reference to FIG.
[0022]
FIG. 6 is a flowchart showing detailed processing of the input processing step 501 in FIG. First, in the base information registration step 601, the base information 110 (FIG. 1) is registered. Next, in the time zone information registration step 602, the time zone information 111 is registered. Next, in the package information registration step 603, the package information 112 is registered. Next, in transport vehicle information registration step 604, transport vehicle information 113 is registered. Next, in the distance table generation processing 605, the distance between the bases and the required travel time by the truck are generated with reference to the road map 115. The road map 115 is prepared in advance, for example, in which roads used in car navigation, connection relations between roads, and distance information are stored.
[0023]
FIG. 7 is a flowchart showing details of the transportation schedule creation processing 502 in FIG. As shown in FIG. 7, first, in step 701, the transport density for each time zone is calculated. That is, in each time zone, the product of the load amount of the package and the distance to the delivery destination of the package is added for all packages. Details of this processing will be described later with reference to FIG. Next, the schedule of the time zone with the highest transportation density calculated in step 701 is created (step 702). Next, a delivery destination base group to which each transport vehicle circulates, that is, route information takeover processing is performed from the schedule result of the time zone created in step 702 (step 703). Details of this processing will be described later with reference to FIG.
[0024]
Next, it is determined whether or not schedule creation for all time zones has been completed (step 704). In step 704, if the schedule creation for all the time periods has been completed, the transportation schedule creation processing is terminated. If there is a time zone in which the schedule creation for the time zone has not been completed, the transportation time schedule is created by selecting the earliest time zone among the time zones for which no schedule has been created (705). Details of this processing will be described later with reference to FIG.
[0025]
FIG. 8 is a flowchart showing the details of the time-specific transport density calculation processing in step 701 of FIG. As shown in FIG. 8, first, a variable I is set to 1 (step 801). Next, it is determined whether or not processing for all time zones has been completed (step 802). If time zone processing remains, the following processing is performed. First, 0 is set to the variable S [I], and the ID of the time zone with the earliest start time among the unprocessed time zones is set to the variable ID [I] (step 803). Next, the variable T is set to the difference between the transportation end time and the transportation start time in that time zone (step 804). In step 805, it is determined whether or not all the packages have been processed. If the processing has not been completed, the product of the load amount of the packages and the distance from the distribution center is calculated for all packages in that time period. (Step 806), and the process returns to step 805 again. When all the processes are completed, the variable S [I] is divided by T and assigned to S [I] (step 807). Next, 1 is added to the variable I (step 808), and the process proceeds to the next time zone. When the processing for all the time zones is completed, I with the maximum S [I] is obtained and substituted into the variable L (step 809). Next, ID [L] is substituted for variable M (step 810). The time having the time zone ID assigned to M is determined as the time zone when the transport importance by time zone is the highest.
[0026]
FIG. 9 is a flowchart showing a detailed flow of the route information takeover process in FIG. As shown in FIG. 9, 1 is substituted into the variable I (step 901). Next, the following processing is performed on the schedule of all transport vehicles in that time zone. First, the number of loads of the I-th vehicle is substituted for variable N (step 903). Next, 1 is substituted into the variable J (step 904). If J is N or less, the group ID of the delivery destination base of the Jth package of the Ith track is substituted into the variable G [J] (step 906). The group ID is set by searching group information in which the base ID registered in the transportation schedule information is registered. If the base ID is not registered in the group information, “0” is set. Next, 1 is added to the variable J (step 907), and processing relating to the next package is performed. When the processes for all the packages are completed (Yes in step 905), the maximum value Max [G] stored in the array G is compared with the minimum value MIN [G] (step 908).
[0027]
If the maximum value and the minimum value are equal and Max [G] is equal to 0 (YES in step 914), the group ID is newly acquired in step 915, route information is created, and the Ith Add a delivery location for the vehicle. However, the group ID newly acquired here is an integer of 1 or more. If Max [G] is not equal to 0, the delivery destination base of the I-th vehicle is added to the group information of the group ID of Max [G] in step 914 (step 916).
[0028]
In step 908, if the maximum value and the minimum value of the array G are different, the following processing is performed. First, one new group ID is acquired and new group information is created (step 909). Next, the delivery destination base registered in the group information other than the group ID registered in the array G is copied to the new group information (step 910). Next, in step 910, the original group information that has already been copied to the new group information is deleted (step 911). Next, the delivery destination base whose group ID is 0 in the array G is added to the group information created in step 909 (step 912). Next, 1 is added to the variable I (step 913), and the process proceeds to the next vehicle process (step 902).
[0029]
FIG. 10 is a flowchart showing an outline of the transportation schedule creation processing of the maximum transportation density time zone schedule creation processing 702 and the schedule creation processing 705 for each time zone in the flowchart of FIG. This schedule creation process is performed based on, for example, GA (genetic algorithm). As shown in FIG. 10, first, different initial solution groups are generated by a method including randomness (step 1001). Details of this processing will be described later with reference to FIG. Next, 1 is substituted into the variable I (step 1002). Next, the solution population is improved by genetic processing (step 1003). Details of this processing will be described later with reference to FIG. By the processing in step 1003, at least a part of the solution population changes. By repeating the processing in steps 1003 to 1005, the solution population gradually approaches the optimal solution. After the processing in step 1002, the solution group is evaluated (step 1004). The evaluation of the solution group is performed on various factors such as the travel distance of the vehicle calculated from the route information and the number of vehicles used, and the ranking is given to each solution in the solution group according to the result. A solution having a higher evaluation result is selected as a next-generation solution (step 1005). Next, 1 is added to the variable I (step 1006). In step 1007, it is determined whether I exceeds the preset GA processing generation number G_MAX or whether the evaluation value of the best solution in the next generation population is better than a predetermined target value. If I> G_MAX or the condition that the evaluation value satisfies the target value is satisfied, the best solution is output in the current solution group (step 1008), and this process is terminated. If the above conditions are not satisfied in step 1007, the process returns to step 1003 and the solution population improvement by genetic processing is repeated.
[0030]
FIG. 11 shows a process for adding a package to the package transportation schedule used in the creation of the initial solution group (step 1001) and the solution group improvement process (step 1003) of the transportation schedule creation process using the genetic algorithm shown in FIG. It is a flowchart figure which shows the detail. This process is a process of adding collection and delivery of one package to a transportation schedule composed of a plurality of transportation vehicles.
[0031]
First, Max_Value is set to the variable Delta_MIN, −1 is set to T_No, and −1 is set to Pos (step 1101). Here, Max_Value represents the maximum numerical value that can be expressed by the computer used. Next, 1 is substituted for I (step 1102). Next, I is compared with the value of the variable Max_Truck (step 1103). Here, Max_Truck is a variable representing the number of vehicles already incorporated in the schedule. If I is small, the following processing is performed. First, it is checked whether or not the luggage L can be added to the I-th vehicle (step 1104). Details of this processing will be described with reference to FIG. In this process, when the specified vehicle can be loaded, it can be used on the transportation route that minimizes the increment of the travel distance (travel time) of the vehicle after satisfying various restrictions in the route of the vehicle. Returns the order in the variable P. In addition, an increase in travel time is set in the variable Delta. Next, it is checked whether or not the value of the variable Delta is smaller than Delta_Min (step 1105). If Delta is smaller, Delta (1106) is substituted for Delta_Min, then the value of I is substituted for T_No (step 1107), and P (step 1108) is substituted for Pos. Values of the variables Delta and P are set in the process of Step 1104 described later with reference to FIG. Next, 1 is added to the variable I (step 1109), and the process proceeds to the next vehicle check process. If Yes in step 1103, it means that all the vehicles have been checked, and the following processing is performed. First, it is determined whether or not Delta_MIN is equal to Max_Value (step 1110). If it is equal, it is determined that there is no vehicle to which luggage can be added, a new vehicle is added (step 1112), and 1 is set to the variable Max_Truck. In addition (step 1113), the luggage L is added to the added vehicle (step 1114). More specifically, the index of the luggage L is added to the last part of the gene information, and the vehicle management information of the additional vehicle is added. If the delivery destination base of the package is not registered in the group information, a new group ID is acquired, new group information is created, and the delivery destination base is registered in the group information. If Delta_MIN is not equal to Max_Value in step 1110, the process proceeds to step 1111 and the luggage L is added to the Posth of the T_No vehicle.
[0032]
FIG. 12 is a flowchart showing details of the package addition check in step 1114 of FIG. In this process, it is assumed that the index of the baggage to be added to the schedule is set in advance in the variable L, and the vehicle No of the vehicle to check whether it can be added is set in the variable T_No. When this processing is completed, if the transportation of the luggage L is possible with the vehicle T_No, an additional position is set in the variable P, and an increment value of the traveling time of the vehicle due to the addition of the luggage is set in Delta. If it cannot be added, P is set to -1, and Delta is set to Max_Value.
[0033]
First, in step 1201, the collection / delivery destination base ID of the additional package L is substituted for the variable Z. Next, the load amount of the load L is set to the variable V (step 1202). Next, −1 is set to the variable P, and Max_Value is set to Delta (step 1203). Next, with reference to the vehicle management information 117 (FIG. 1), the gene start position of the T_Noth vehicle is set to the variable I (step 1204). Next, the value of the variable I is compared with the value of the gene end position + 1 of the vehicle (step 1205). If I is larger, the process is terminated.
[0034]
If I does not exceed the above value, the following processing is performed. First, I is compared with the gene start position of the T_Noth vehicle (step 1206). If both are equal, the base ID representing the distribution center from which the vehicle departs is substituted for the variable X (step 1208). If the two are not equal, the base ID of the collection / delivery destination of the (I-1) th package in the genetic information of the vehicle is substituted (step 1207). Next, in any of steps 1207 and 1208, I is compared with the value of the gene end position + 1 of the T_Noth vehicle (step 1209). If they are equal, the base ID of the distribution center is substituted for variable Y (step 1211). If they are not equal, the ID of the collection / delivery destination base of the I-th package of the T_No-th vehicle is substituted for Y (step 1210).
[0035]
After either processing of steps 1210 and 1211, it is checked whether or not the base Z is the same as the group to which the delivery destination base of the package already loaded on the T_Noth vehicle belongs (step 1212). That is, if the base Z is already registered in the group information, it is checked whether or not the group ID of the base Z is equal to the group ID of the delivery destination base of the package already loaded on the T_Noth vehicle. If they are different, the addition is impossible. If both are equal or the base Z is not registered in any group information, it can be added. Next, the load amount check is performed (step 1213). It is checked whether or not the load V can be added to the T_No-th track (whether or not the load exceeds).
[0036]
Next, a value obtained by subtracting the required time between X and Y from the sum of the required time between X and Z and the required time between Z and Y is substituted into T (step 1214). In the flowchart, Time (X, Y) represents the travel time of the vehicle from the base X to Y. Next, T and Delta are compared (step 1215). If T is smaller than Delta, T is substituted for Delta and I is substituted for P (Step 1216). When T is greater than or equal to Delta, the values of Delta and P are not changed. Next, 1 is added to the variable I and the process returns to step 1205 to move to the check of the next additional position.
[0037]
Here, the outline of the genetic process will be briefly described. First, an outline of schedule creation using GA will be described with reference to FIGS. 13 to 17. Next, a more detailed process will be described with reference to FIG.
[0038]
FIG. 17 is a diagram showing a relationship between the distribution center 31 and the respective bases 41, 43, 45, 51, 53, 55, 57, 61, 63, 65, and 67 (hereinafter collectively referred to as the bases 41 to 67). It is. Scheduling is performed on the assumption that the bases 41 to 67 are delivered by three trucks from truck 1 to truck 3 with the distribution center 31 as the center.
[0039]
FIG. 13 is a flowchart showing an outline of schedule creation processing using GA (genetic algorithm). The method shown in FIG. 13 is a method of simultaneously optimizing the allocation of a load to a truck and the route of the truck using a GA. Create multiple schedules, combine the schedules (cross processing), make partial improvements (mutation), and create a better schedule. As shown in FIG. 13, first, in step S1, an initial schedule plan group is created. A plurality of schedules are created using route creation giving priority to a delivery destination far from the distribution center 31 (FIG. 17) and heuristics such as a sweep method. Next, in step S2, as an intersection process, for example, the vehicle is replaced by two schedule plans to improve the schedule. In step S3, as a mutation, for example, a track with a low loading rate is deleted, and loading on another track is performed to improve the schedule. As a selection in step S4, a schedule plan with a small number of trucks and a schedule plan with a small total travel distance are selected. In step S5, as a process for creating the next generation group, a similar schedule plan is removed, and optimization efficiency is increased. By repeating the above processing, a better schedule plan can be created.
[0040]
FIG. 14 describes the expression of the schedule. A traveling route of a plurality of tracks is represented by one arrangement as shown in FIG. For example, truck 1 carries luggage 1-7, and truck 2 carries luggage 8-13.
[0041]
FIG. 15 is a diagram showing how the initial schedule plan is created. A GA initial schedule is created by combining various types of heuristics, such as giving priority to far-off delivery destinations and assigning tracks according to the direction. As an example, the allocation of tracks by direction is performed as follows. As shown in FIG. 15, the sweep method uses polar coordinates with the depot 15 (distribution center) as the origin, and expresses the positions of all the delivery destinations in terms of distance and angle, and creates a straight line 17-1 centered on the depot 15. While rotating, a delivery group is created so as to satisfy the luggage amount of each base 15 and the daily travel distance limit of the truck.
[0042]
Next, the intersection process will be described with reference to FIG. For example, as an example of the intersection process, different schedule plans are selected with a certain probability from the initial group, and tracks are exchanged between two schedule plans.
[0043]
In the mutation, for example, a truck with a low loading rate is deleted, repackaging of another truck, assignment of a new truck, and the like are performed. In the selection process, a plurality of schedule proposals are evaluated according to the following criteria, and only excellent ones are selected. The evaluation is performed using, for example, a fixed cost of the truck, a variable cost proportional to the operation time, and various penalties such as a loading rate and a travel distance. With the above processing, for example, the following schedule selection can be realized. 1) The smaller the number of tracks, the better. 2) The smaller the total distance traveled, the better. 3) The load capacity of the truck is not excessive. 4) There is no excess of the mileage of the truck.
[0044]
Hereinafter, with reference to FIG. 18 to FIG. 22, a more detailed description regarding the above processing will be given. FIG. 18 is a flowchart showing details of the initial solution group generation process (step 1001) in FIG. In addition, a traveling schedule of a plurality of vehicles is registered in one solution. The solution group includes a plurality of solutions. The variable SIZE is set in advance with the number of genes registered in the initial population.
[0045]
First, 0 is substituted for variable I (step 1301). Next, while I is smaller than SIZE, the processing from steps 1303 to 1306 is repeated to generate the number of genes specified by SIZE. An area for storing an empty gene in which no value is set for each element is generated (step 1303). Next, an array T [] in which packages of package information are rearranged in a random order is created (step 1304). Next, the packages registered in the array T [] are sequentially added to the empty gene in this order by the adding procedure shown in FIG. 11 (step 1305). Next, 1 is added to the variable I (step 1306), and the next initial solution is created.
[0046]
FIG. 19 is a flowchart showing details of the solution group improvement (step 1003) 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 1401). Next, C_NUM is multiplied by SIZE by a preset intersection rate, and a maximum integer not exceeding the value is substituted (step 1402). Next, 1 is substituted into the variable I. If the variable I is less than or equal to C_NUM, the following process is repeated to perform the intersection process (step 1404). First, two different solutions (genes) are selected from the solution population (step 1405). Next, a cross process is performed based on the two selected solutions (genes), and a solution (gene) newly generated by the cross process is added to the solution group (step 1406). Details of this intersection processing will be described later with reference to FIG. Next, 1 is added to I (step 1407), and the process returns to step 1404 and proceeds to the next intersection process.
[0047]
In step 1404, if I exceeds C_NUM, the process proceeds to the mutation process. That is, the variable M_NUM is multiplied by the preset mutation rate by SIZE, and the maximum integer not exceeding the value is substituted (step 1408). Next, 1 is substituted into the variable I (step 1409). Next, while I is less than or equal to the value of M_NUM, the next process is repeated (step 1410). First, one solution (gene) is randomly selected from the solution population (step 1411). Next, mutation processing is performed on the selected solution, and the solution (gene) is added to the solution population (step 1412). Details of this processing will be described later with reference to FIG. Next, 1 is added to the variable I (step 1413), and the process returns to step 1410 to continue the processing.
[0048]
FIG. 20 is a flowchart showing details of the intersection process (step 1406). First, the number of packages registered in each solution (gene) is substituted for variable N (step 1501). Next, the two solutions (genes) to be crossed are copied to the sequences G1 [] and G2 [] (step 1502). Next, in order to determine the intersection, an integer of 2 or more and N-1 or less is randomly generated and substituted into the variable P (step 1503). Next, the P1 and subsequent package data indexes of G1 are deleted (step 1504). That is, −1 is substituted for the element to be deleted. Next, 1 is substituted into the variable I (step 1505). Next, while I is N or less (step 1506), the following processing is performed. It is checked whether G2 [I] is not included in the array G1 [] (step 1507). If not included, the package information is added to the transport schedule of G1 [] by the processing procedure shown in FIG. 7 (step 1508). Next, 1 is added to the variable I (step 1509), and the next process is started. If the condition is not satisfied in step 1507, the process proceeds to step 1509 and the following processing is performed. If I exceeds N in step 1506, the process is terminated.
[0049]
FIG. 21 is a flowchart showing details of the mutation process (step 1412) in FIG. Assume that the number of packages registered in the solution is set in the variable N. First, the solution (gene) to be mutated is copied to the sequence G [] (step 1601). Next, the collection / delivery destination base farthest from the distribution center and the distance from the distribution center are substituted into a variable L (step 1602). A real number in the range between 0 and 0.3 is randomly generated and assigned to the variable R (step 1603). Next, the product of the value of the variable L and the value of R is substituted into the variable L1 (step 1604). Next, in order to determine the mutation location on the gene, an integer of 1 to N is randomly selected and substituted for variable P (step 1605). Next, 1 is substituted into variable I and 1 is substituted into J (step 1606). The value of G [P], that is, the index value of the package information is substituted for variable K (step 1607). While I is N or less (step 1608), the next process is repeated. It is checked whether the distance between the collection / delivery destination base for the package with index K and the collection / delivery destination base for the package with G [I] is less than or equal to L1 (step 1609). If the condition is satisfied, the value of G [I] is substituted for T [J], and -1 is substituted for G [I] (step 1610). Next, 1 is added to the variable J (step 1611), 1 is also added to I (step 1612), and the process returns to step 1608 to continue the processing.
[0050]
If the value of I exceeds the value of N in step 1608, the element having the value of -1 in the array G is deleted and packed forward (step 1613). Next, the collection and delivery destination bases of J packages stored in the array T are sequentially added from the top to the transport schedule of the array G by the processing procedure shown in FIG. 11 (step 1614).
[0051]
FIG. 22 is a flowchart showing details of the result output process (step 503) in FIG. First, the transit base of each transport flight is displayed on the map (step 1701). Next, a table and a graph are created and displayed together with the schedule obtained by the transportation schedule creation process and the values of the number of required vehicles and the travel distance (step 1702). Next, if there is a print instruction, a graph or a table is printed (step 1703).
As a result, as shown in FIG. 17, it is possible to create a schedule plan in which bases assigned to each track are grouped.
[0052]
As described above, according to the schedule creation system according to the present embodiment, the arrival of a vehicle at each collection and delivery destination base by the package from the distribution center to the delivery destination base and the baggage from the collection and delivery destination base to the distribution center. It is possible to create a transportation schedule for efficient transportation while satisfying restrictions on time and loading capacity of vehicles.
As mentioned above, although demonstrated along embodiment, this invention is not restrict | limited to these. It will be apparent to those skilled in the art that other various modifications, improvements, and combinations can be made.
[0053]
【The invention's effect】
As described above, according to the present invention, the baggage from the distribution center to the delivery destination base and the baggage from the collection / delivery destination base to the distribution center are limited by the arrival time of the vehicle at each collection destination location. It is possible to create a transportation schedule for efficient transportation while satisfying the load capacity constraint.
[Brief description of the drawings]
FIG. 1 is a block diagram showing a configuration example of a transportation plan creation system according to an embodiment of the present invention.
FIG. 2A to FIG. 2F are diagrams showing examples of the structure of data included in the storage device of FIG.
FIG. 3 is a diagram showing a configuration example of transportation schedule data included in the storage device of FIG. 1;
FIG. 4 is a diagram showing a configuration example of genetic information in a genetic algorithm used in a transportation schedule creation step in the transportation plan creation system according to one embodiment of the present invention.
FIG. 5 is a flowchart showing an overall processing flow in the transportation plan creation system according to the embodiment of the present invention.
FIG. 6 is a flowchart showing an outline of input processing in the transportation plan creation system according to the embodiment of the present invention.
FIG. 7 is a flowchart showing a flow of a transportation schedule creation process in the transportation plan creation system according to the embodiment of the present invention.
FIG. 8 is a flowchart showing the flow of transportation importance calculation processing by time zone in the transportation plan creation system according to one embodiment of the present invention.
FIG. 9 is a flowchart showing the flow of group information takeover processing in the transportation plan creation system according to one embodiment of the present invention.
FIG. 10 is a flowchart showing a flow of a transportation schedule creation process in the transportation plan creation system according to the embodiment of the present invention.
FIG. 11 is a flowchart showing a flow of a package addition check process in the transportation plan creation system according to the embodiment of the present invention.
FIG. 12 is a flowchart showing a flow of a package addition position determination process in the transportation plan creation system according to the embodiment of the present invention.
FIG. 13 is a flowchart showing an example of a scheduling process in the transportation plan creation system according to the embodiment of the present invention, and shows a flow of a process example using a GA algorithm.
FIG. 14 is a diagram showing the traveling routes of a plurality of trucks as one array in the scheduling process in the transportation plan creation system according to the embodiment of the present invention.
FIG. 15 is a diagram showing an example of processing by a sweep method as an example of scheduling processing in the transportation plan creation system according to one embodiment of the present invention.
FIG. 16 is a diagram showing a concept of intersection processing in scheduling processing in the transportation plan creation system according to one embodiment of the present invention.
FIG. 17 is a conceptual diagram showing an outline of grouping in the transportation plan creation system according to one embodiment of the present invention.
FIG. 18 is a flowchart showing the flow of initial group generation processing of the present invention in the transportation plan creation system according to one embodiment of the present invention.
FIG. 19 is a flowchart showing an outline of the flow of genetic processing in the transportation plan creation system according to one embodiment of the present invention.
FIG. 20 is a flowchart showing the flow of intersection processing in the transportation plan creation system according to one embodiment of the present invention.
FIG. 21 is a flowchart showing the flow of mutation processing in the transportation plan creation system according to one embodiment of the present invention.
FIG. 22 is a flowchart showing a flow of result output processing in the transportation plan creation system according to one embodiment of the present invention.
[Explanation of symbols]
DESCRIPTION OF SYMBOLS 101 ... Input device, 102 ... Output device, 103 ... Display device, 104 ... Processing device, 105 ... Program, 106 ... Input processing unit, 107 ... Transport schedule creation unit, 108 ... Result output unit, 109 ... Storage device, 110 ... Base information, 111 ... Time zone information, 112 ... Luggage information, 113 ... Transportation vehicle information, 114 ... Distance information, 115 ... Road map, 116 ... Group information, 117 ... Transportation schedule information, 118 ... Gene information, 119 ... Vehicle management information.

Claims (4)

A transportation plan creation system that creates a transportation schedule for vehicles that transport luggage while traveling between a distribution center and a delivery destination base.
Location information including the location information of the distribution center and the delivery destination location, time zone information in a plurality of operation time zones relating to operations in which the transport vehicle departs from the distribution center and returns to the distribution center, and luggage of each piece of luggage A storage device for storing baggage information including an amount and a delivery destination base, transport vehicle information including a load capacity limit of the transport vehicle, and distance information regarding travel distance / time in the transport vehicle between bases;
Based on each information stored in the storage device, the product of the load amount of each package and the distance to the delivery destination of each package in each time zone of the plurality of operation time zones, By calculating for all the packages to be transported in the time zone, and finding the value obtained by dividing the total sum by the time width of the time zone (hereinafter referred to as “transportation density by time zone”), A means of calculating the importance of transportation by time zone to obtain the importance of transportation by time zone,
Of the transport importance by time zone obtained by the transport importance by time zone calculation means , the time zone having the largest sum is set as the most important time zone, and the transport schedule in the most important time zone is set to a plurality of time zones. The most important time zone transportation schedule creation means to be created first ,
The delivery destination bases are grouped together with the delivery destination bases that one transport vehicle circulates through the delivery destination bases based on the transport schedule in the most important time zones created by the most important time zone transportation schedule creation means. And a route information transfer means for applying the transportation schedule in the most important time zone to other time zones by assigning different vehicles to the respective delivery destination groups and reflecting the schedules in other time zones. Transportation planning system. The most important time zone transportation schedule creation means is:
A means of creating multiple initial schedule proposals for package delivery;
Means for determining whether the schedule proposal can be improved by replacing the vehicle with respect to the initial schedule proposal;
A means to improve the proposed schedule so that the loading rate is high for vehicles with low loading rates,
A means for selecting a schedule plan that reduces the number of vehicles and the total mileage,
The transportation plan creation system according to claim 1, further comprising means for performing processing for removing any of the similar schedule plans. A transportation plan creation method for creating a transportation schedule for a vehicle that transports luggage while traveling between a distribution center and a delivery destination base,
Location information including the location information of the distribution center and the delivery destination location, time zone information in a plurality of operation time zones relating to operations in which the transport vehicle departs from the distribution center and returns to the distribution center, and the luggage of each piece of luggage A step of inputting package information including an amount and a delivery destination base, transport vehicle information including a load capacity limit of the transport vehicle, and distance information regarding travel distance / time in the transport vehicle between the bases;
Based on the input information, the product of the load amount of each package and the distance to the delivery destination of the package in each of the plurality of operation time zones is transported in each time zone. Time for calculating the importance of transportation by time zone by calculating the value for all the parcels to be performed and finding the value obtained by dividing the total sum by the time width of that time zone ("transport density by time zone") The transport importance calculation step by band,
Of the transport importance by time zone obtained by the transport importance by time zone calculation step, the time zone having the largest sum is set as the most important time zone, and the transport schedule in the most important time zone is set to a plurality of time zones. The most important time zone transportation schedule creation step to be created first,
The delivery destination bases are grouped together with the delivery destination bases visited by one transport vehicle as a group based on the delivery schedule in the most important time zone created in the most important time zone transportation schedule creation step. A route information transfer step for applying a transportation schedule in the most important time zone to other time zones by assigning a different vehicle to each delivery destination group and reflecting the schedule in other time zones;
A transportation plan creation method. The most important time zone transportation schedule creation step includes:
Creating multiple initial schedule proposals for package delivery;
Determining whether the schedule proposal can be improved by replacing the vehicle with respect to the initial schedule proposal;
Improving the proposed schedule to increase the loading rate for vehicles with low loading rates;
Selecting a schedule plan that reduces the number of vehicles and the total mileage,
Performing a process to remove any of the similar schedule proposals;
The transportation plan creation method according to claim 3, comprising:

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