JP2001188984A - Method and system for optimum delivery planning - Google Patents

Abstract

PROBLEM TO BE SOLVED: To make a distribution plan which is effective to the distribution of the positions of all delivery destinations. SOLUTION: The delivery planning system is equipped with an input device which inputs input information regarding a delivery plan such as position information on delivery destinations, a processor which generates the delivery plan such that after delivery vehicles depart from a physical distribution base according to the inputted information and delivers loads to plural delivery destinations, they return to the original physical distribution base with the shortest total travel distance, and an output device which outputs the generated delivery plan; and delivery planning processes by algorithms differing in planning strategy are performed by a single processor on a time-division basis or by plural processors in parallel and the processing result which has the shortest total travel distance among execution results is selected and outputted from the output device.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a delivery planning method for departure from a distribution base or warehouse in a distribution system and creating a delivery plan for delivering a load to a plurality of destinations, and particularly to an efficient delivery method for home delivery and the like. The present invention relates to a method of creating a delivery plan that is used for finding a transportation route for delivery and that realizes reduction of distribution costs and improvement of services.

[0002]

2. Description of the Related Art The problem of distribution planning in a distribution system is that a plurality of vehicles depart from a common distribution base, deliver cargo to a plurality of destinations, and return to the original distribution base. Is the shortest. However, it is assumed that only one vehicle passes through each delivery destination. In addition, each vehicle has an upper limit of the load amount, and there is a load amount restriction that the load amount is larger than the total amount of luggage to the delivery destination visited by the vehicle. Further, there is a travel distance constraint that the travel distance of each vehicle has an upper limit.

[0003] The step of creating a delivery plan created under such restrictions is usually divided into two stages. One is a problem of grouping for distributing a delivery destination to each vehicle.
The other is a problem of optimizing a route in which a vehicle goes around a delivery destination group determined by grouping. This is also called the traveling salesman problem.

For the problem of grouping, a sweep method, a saving method, and the like are known. These methods are described in the following documents. Gilbert Laporte: The Vehicle Routing Problem: An o
verview of exact and approximate algorithms: Europ
ean Journal of Operational Research 59 (1992) 345-
358 In addition, the full solution search method, which is a solution to the traveling salesman problem, and the NN method (Nearest method) used in the 2OPT method and grouping
Knowledge on the Neighbor method) and the NI method (Nearest Insertion method) can be obtained from the following documents. Yoshitsugu Yamamoto, Mikio Kubo: Invitation to traveling salesman problem: Asakura Shoten (1997)

[0005]

However, in the conventional delivery planning method, a single algorithm is used to perform grouping. For example, if the NN method was applied to grouping, relatively good results could be obtained in many cases. However, in parts procurement applications and the like, some delivery destinations may be located far from the distribution base. In such a case, if the simple NN method is used for the grouping problem, first, the delivery destinations that are relatively short from the distribution base are grouped together, and then the delivery destinations that are far from the distribution base are grouped together. Tend to be grouped together. Therefore, this method has a problem that the total traveling distance becomes longer as a whole. In general, the conventional delivery plan creation method may not be suitable for the grouping method depending on the distribution of the location of the delivery destination.

Also, one algorithm has been used for route optimization. However, the number of delivery destinations included in one route varies, and, for example, the 2OPT method is more efficient when the number of delivery destinations is large, but the accuracy is lower than the other methods when the number is small. There is a problem that falls.

An object of the present invention is to provide an optimum delivery plan formulation method and system capable of creating an effective delivery plan for all distributions of delivery destination positions.

[0008]

In order to achieve the above object, an optimal delivery plan formulation method according to the present invention generates a plurality of delivery plans using a plurality of grouping strategies, and determines an optimal delivery plan among them. It is characterized in that it is selected.

More specifically, an input device for inputting input information related to the planning of a delivery, such as location information of a delivery destination, the amount of packages, the number of delivery vehicles, and the like, and a plurality of delivery vehicles depart from a distribution base based on the input information. And a processing device for creating a delivery plan for minimizing the total mileage on the route returning to the original distribution base after delivering the cargo to the destination, and an output device for outputting the created delivery plan. Optimal delivery planning method in a delivery planning system, wherein a plurality of delivery planning processes of algorithms having different planning strategies are executed in a single processing unit in a time-division manner or in parallel by a plurality of processing units, and the execution is performed. A processing result that minimizes the total traveling distance is selected from the results, and output from the output device.

In the present invention, for example, an algorithm based on the following five grouping strategies is used. (1) Far-priority NN method (2) Far-priority NI method (3) Directional split NN method (4) Directional split NI method (5) Sweep method

Here, the words "distant priority" and "division of area" indicate a delivery destination that a delivery vehicle first goes around, that is, a method of specifying a seed. The NN method, the NI method, and the sweep method are algorithms for generating a route around several delivery destinations. Therefore, the "distant priority NN method"
This is a grouping method in which the method of specifying the seed is “distant priority” and the algorithm for the destination is the “NN” method. Remote priority NI method, area division NN method, area division N
Method I has a similar meaning.

In the remote priority, the destination farthest from the distribution base among the undelivered destinations is designated as a seed. In the direction division, for example, for each fan-shaped subspace (hereinafter referred to as a direction) generated by dividing a space (delivery target area) by a straight line passing through several logistics bases, a delivery destination located in the direction is designated. Assign a number of seeds to the area in proportion to the sum of the load.

In the NN method, starting from a seed, a delivery destination closest to the last delivery destination is visited one after another while satisfying the constraint condition. When the constraint condition is no longer satisfied, the generation of one route is completed at the delivery destination visited so far.
While the undelivered destination remains, as before, it continues to generate routes starting from the seed.

The NI method is the same as the NN method except that the delivery destination with the smallest increase in the traveling distance when inserted at an appropriate position on the route is extracted.

The sweep method is the same as the NN method except that the destinations are taken in ascending order of the angle between the x-axis (or y-axis) and the straight line connecting the destination and the distribution base.

Further, the present invention is characterized in that a route optimization method is changed according to the number of delivery destinations included in the route. More specifically, a highly accurate full solution search method is applied to a route with a relatively small number of delivery destinations, and a high-speed 2OPT method is applied to a route with a large number of destinations, thereby optimizing the route efficiently. To achieve.

[0017]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, the present invention will be described in detail based on embodiments shown in the drawings. FIG. 1 is a block diagram of a hardware configuration showing an embodiment of an optimum delivery planning system according to the present invention. The optimal delivery planning system according to this embodiment includes a keyboard 10, a display 20, a central processing unit 30, a main storage device 40, and a secondary storage device 50. The central processing unit 30 is connected to the keyboard 10 and the display 20, and uses the keyboard 10 to provide parameter information such as the position of the delivery destination in the delivery target area, the amount of luggage, the upper limit of the load of the vehicle, and the upper limit of the mileage;
Various commands related to creation of a delivery plan are input. The input parameter information and command are displayed on the display 20. The optimal delivery route created based on the input parameter information is displayed on the display 20 by text or a map. The main storage device 40 stores a delivery planning program group 41 for planning a delivery route based on a plurality of planning strategies. Further, a work area 42 for processing data is secured. The secondary storage device 50 stores data such as the location of the delivery destination, the amount of luggage, and the upper limit of the load capacity of the vehicle, and the processing result of the command.

FIG. 2 is an overall configuration diagram of an optimal delivery plan formulation process using five grouping strategies. In this embodiment, first, a delivery plan formulation process of the far-priority NN method (step 101), a delivery plan formulation process of the far-priority NI method (step 102), a delivery plan formulation process of the area-division NN method (step 103), The delivery destination is assigned to each vehicle by the delivery plan formulation process of the area division NI method (step 104) and the delivery plan formulation process of the sweep method (step 105). Next, route optimization processing (steps 106 to 106)
At 110), a delivery plan is created for each delivery destination group so that the vehicle route is the shortest. Finally, this 5
An optimal delivery plan is selected from the two delivery plans (step 111), and the process is terminated.

Here, the five delivery plan planning processes of steps 101 to 105 are constituted by corresponding programs and stored in the main storage device 40 as the delivery plan planning program group 41 of FIG. In this embodiment, since there is one processing unit 30, the central processing unit 30
Performs time-division planning for five delivery plans, further performs route optimization processing (steps 106 to 110) in a time-division manner, and finally selects an optimal delivery plan from the results of the five delivery plan planning processes. I do.

The present invention is not limited to the one in which the five delivery schedule planning processes are performed in a time-division manner, but instead prepares five central processing units 30 to execute the planning process and the optimization process in parallel. One central processing unit may be configured to select an optimal delivery plan from among the five delivery plan formulation results.

FIG. 3 is a flow chart for explaining the algorithm of the delivery plan making process of the distant priority NN method. First, in step 201, the vehicle t is set to the 0th vehicle. Next, in step 202, it is determined whether there is an undelivered delivery destination. If not, end. If so, step 203
Then, the delivery destination that is farthest from the distribution base among the undelivered delivery destinations is set as the delivery destination i, and the delivery destination i is set as the delivery destination. The delivery destination i is a seed that is given priority in the distant place. Information about each vehicle t is represented by an element of a structure array T [t]. (1) T [t] .L is the amount of luggage carried by the vehicle t. (2) T [t] .D is the traveling distance of the vehicle t. (3) T [t] .R is an array of delivery destinations, which is the route of the vehicle t.

In step 204, a route connecting the distribution base of the vehicle t and the seed is generated as follows. (1) Substitute the amount of package at the delivery destination i into T [t] .L. (2) The value obtained by doubling the distance between the delivery destination i and the distribution base is substituted into T [t] .D. (3) Substitute the delivery destination i at the beginning of T [t] .R.

Next, at step 205, it is determined whether there is a delivery destination that has not been delivered. If not, end. If so, in step 206, the delivery destination which is closest to the delivery destination i among the undelivered delivery destinations is set as the delivery destination ii. Next, at step 207, the delivery destination ii
Is added to the route of the vehicle t to calculate the increment D = d (i, ii) + d (depot, ii) d (depot, i). Here, d is a distance function, and depot indicates a distribution base.

Next, at step 208, it is determined whether the load capacity constraint and the travel distance constraint are satisfied. The loading capacity constraint is a constraint that, even when the vehicle t loads the cargo of the delivery destination ii, the given upper limit of the loading capacity is not exceeded. The travel distance constraint is a constraint that, even if the vehicle t further increases the travel distance by D, the given travel distance does not exceed the upper limit. If these two constraints are satisfied, in step 209, the setting is made so that the vehicle t passes through the delivery destination ii. That is, the array of structures T
[t] is changed as follows. (1) Add the package amount of the delivery destination ii to T [t] .L. (2) Add D to T [t] .D. (3) Add delivery destination ii at the end of T [t] .R.

Further, the delivery destination ii is set as delivered. Next, in step 210, the delivery destination ii is substituted for the delivery destination i, and the process returns to step 205 and loops. In step 208, 2
If the two constraints are not satisfied, at step 211, the vehicle t
Is set as the next vehicle, and the flow returns to step 202 to loop.

FIG. 4 is a flowchart for explaining the algorithm of the far priority NI method. In step 301, the vehicle t is set to the 0th vehicle. Next, step 302
Then, a seed i with a long-distance priority is obtained, and the seed i is delivered. Next, in step 303, it is set so that the vehicle t passes through the seed i. The algorithm after step 304 is 3
There are heavy loops. The loop of steps 304 and 317
The loop of steps 306, 307, 316 for i and j
It is a loop of steps 309, 310, and 315 regarding the above.

At step 304, it is determined whether there is a delivery destination that has not been delivered. If not, it exits the loop and ends. If so, in step 305, a large constant M suitable for the variable min_d
Substitute AX. Next, in step 306, the 0th delivery destination is substituted for the delivery destination i. In step 307, i is compared with the number max_i of all delivery destinations. If i is not smaller than max_i, the process exits the loop for i and proceeds to step 317.
If it is smaller, it is determined in step 308 whether the delivery destination i has been delivered. If it has been delivered, the process proceeds to step 316. If it has not been delivered yet, it enters a loop for j. First, in step 309, 0 is substituted for a variable j. Next, in step 310, the variable j is compared with the length of the array T [t] .R. If j is large, the process exits the loop for j and returns to step 316.
Proceed to. If j is not large, the increment D of the traveling distance is calculated in step 311. As described above, T [t] .R is an array of delivery destinations and represents a route through which the vehicle t passes. T
[t] .R [j] is the (j + 1) th delivery destination visited by the vehicle t after leaving the distribution base. D represents the increment of the traveling distance when the delivery destination i is inserted between the j-th and (j + 1) -th. In terms of the expression, D = d (T [t] .R [j1], i) + d (i, T [t] .R [j]) d (T [t] .R [j1], T [t ].
R [j]).

Next, at step 312, the variable min_d is compared with the increment D of the traveling distance. If min_d is not larger than D, the process proceeds to step 315, and the loop for j is repeated. If min_d is greater than D, proceed to step 313,
It is determined whether the load capacity constraint and the traveling distance constraint are satisfied.
If not, the process proceeds to step 315, and the loop for j is repeated. If it satisfies, D, I, and j at this time are substituted for min_d, min_I, and min_j, respectively, and the process proceeds to step 315 to repeat the loop for j. After all, i and j
Get the minimum value of D for

FIG. 5 is a flowchart showing details of step 317 to be executed next after exiting the loop relating to i in step 307. First, in step 401, min_
Determine whether d is MAX. If it is MAX, it means that min_d has not changed, that is, the delivery destination to be inserted has not been found, and therefore the extension of the route of the vehicle t is stopped. Then, in step 402, the vehicle t is set as the next vehicle. Further, at step 403, a seed i with a long-distance priority is acquired, and the seed i is set to be delivered. Next, at step 404, it is set so that the vehicle t passes through the seed i. On the other hand, if min_d is not MAX, the vehicle t
Sets the delivery destination min_i to be the (min_j + 1) th visit and makes the delivery destination min_i delivered.

The direction division NN method calls procedures allocate and reallocate for allocating a vehicle to a direction. In the following,
Describe the algorithm of locate and reallocate.

FIG. 6 is a flowchart for explaining the allocate algorithm. In step 501, a direction i including each delivery destination is calculated, and FL [i] is obtained by adding the amount of package at the delivery destination. Next, in step 502, an initial value N of the number of vehicles is assigned to each direction i by a number FT [i] proportional to FL [i]. Here, the value of the division is an integer. Further, a sum TFT for i of the number FT [i] just allocated is obtained.
Next, in step 503, the remainder of the division is put into W [i]. Then, in steps 504 and 505, the remaining (N-TFT) vehicles that have not been allocated are allotted one by one to the area i in the descending order of W [i]. That is, FT [i] is increased by one. Next, in step 506, a delivery destination for the number of vehicles FT [i] in each direction i is randomly selected, and a route connecting the distribution base and the seed is generated using the delivery destination as a seed. Further, the delivery j is set as delivered. Finally, in step 507, the variable maxtruck
Substitute N.

In the reallocate, when the number of available vehicles is exhausted, the number of vehicles in proportion to the total remaining baggage amount TRL is obtained and allocated to each area. reallocate is basically al except for the following two points
Same as locate. First, the total number of allocated vehicles is not the initial value N, but a number IN obtained by dividing the total remaining baggage amount TRL by the upper limit of the load capacity of the vehicle. Second, the number of vehicles in each direction i is proportional to the remaining baggage amount RFL [i] instead of the baggage amount FL [i] in each region. It should be noted that the total remaining baggage amount TRL is the total baggage amount of the undelivered delivery destination, and the remaining baggage amount RFL [i] of each area i is the total baggage amount of the undelivered delivery destination in the area i.

FIG. 7 is a flowchart for explaining the algorithm of the area division NN method. The direction division NN method includes a double loop. The outer loop ends when all destinations are included in the vehicle's route. The inner loop starts at step 603 when the vehicle t is 0 and ends when all the delivery destinations are included in the route of the vehicle or when the vehicle t becomes maxtruck. First, allocate is called in step 601 to allocate a total of N vehicles to the area. next,
In step 602, it is determined whether there is an undelivered delivery destination. If not, end. If so, the vehicle t is set to 0 in step 603.

Next, at step 604, it is determined whether there is a delivery destination that has not been delivered and the vehicle t is smaller than maxtruck. Otherwise, the process returns to the start step 602 of the outer loop.
If so, it is determined in step 605 whether the vehicle t is valid. The vehicle t is valid by default. In step 611, the vehicle t becomes invalid. If not valid, the value of t is incremented by one in step 615, and the process returns to the start step 604 of the inner loop. If valid, step 606
Then, the last of T [t] .R is set to i, and the undelivered delivery destination closest to i is set to ii. Next, at step 607, the increment of the traveling distance D = d (i, ii) + d (depot, ii) d (depot, i) is calculated.

Next, at step 608, it is determined whether the load capacity constraint and the travel distance constraint are satisfied. If these two constraints are satisfied, in step 609, the setting is made so that the vehicle t passes through the delivery destination ii. Further, the delivery destination ii is set as delivered. Next, at step 610, the value of t is increased by one. If the two constraints are not satisfied, t is invalidated in step 611, and it is determined in step 612 whether there is another valid vehicle. If there is, at step 613, the value of t is increased by one. If not,
Step 614 calls reallocate to reallocate the vehicle. In any case, the first step 6 of the inner loop
Return to 04.

FIG. 8 is a flowchart for explaining the algorithm of the area division NI method. First, step 701
Call allocate above to allocate N vehicles to the area. Next, in step 702, it is determined whether there is a delivery destination that has not been delivered. If not, end. If there is, the vehicle t is set to 0 in step 703. Next, in step 704, it is determined whether there is a delivery destination that has not been delivered and the vehicle t is smaller than maxtruck. Otherwise, return to step 702. If so, it is determined in step 705 whether the vehicle t is valid. The vehicle t is valid by default. Also, at step 718, the vehicle t may become invalid.
If not valid, the value of t is incremented by one in step 719, and the process returns to step 704. If valid, step 70
Proceed to 6.

Steps 706 to 717 are exactly the same as steps 305 to 316 of the far priority NI method. D is an increment of the traveling distance when the delivery destination i is inserted between the j-th and (j + 1) -th delivery destinations of the route through which the vehicle t passes. Here, the minimum value of D is obtained by a double loop for i and j, and D, I, and j at that time are min_d, min_I, and min, respectively.
_j, and proceeds to step 718.

FIG. 9 is a flowchart showing the details of step 718 to be executed next after exiting the loop relating to i in step 708. First, in step 801, min_
Determine whether d is MAX. If it is MAX, it means that min_d has not changed, that is, the delivery destination to be inserted has not been found, so that the vehicle t is invalidated in step 802 to stop extending the route of the vehicle t. . Next, in step 803, it is determined whether there is another valid vehicle. If there is, at step 804, the value of t is increased by one. If not, reallocate as described earlier in step 805
To reassign the vehicle.

On the other hand, if min_d is not MAX, step 8
At 06, the vehicle t is set to visit the delivery destination min_i at the (min_j + 1) th position, and the delivery destination min_i is set to be delivered. Next, in step 807, the value of t is increased by one.

FIG. 10 is a flowchart for explaining the algorithm of the sweep method. In the sweep method, vehicles pass through the delivery destination in ascending order of angle. What is the delivery angle?
This is the angle between the x-axis (or y-axis) and the straight line connecting the delivery destination and the distribution base. In addition, the x-axis passes through the logistics base,
A straight line pointing in the east-west direction.

At step 901, the vehicle t is set to the 0th vehicle. Next, in step 902, it is determined whether there is an undelivered delivery destination. If not, end. If there is, in step 903, the delivery destination with the smallest angle among the delivery destinations that have not been delivered is set as the delivery destination i, and the delivery destination i is set as the delivery destination. Step 9
At 04, a route connecting the distribution base of the vehicle t and the delivery destination i is generated. Next, in step 905, it is determined whether there is a delivery destination that has not been delivered. If not, end. If any, step 90
In 6, the smallest delivery destination that has not been delivered is designated as a delivery destination ii.

Next, at step 907, the delivery destination ii is set to the vehicle t.
Calculate the increment of the mileage D = d (i, ii) + d (depot, ii) d (depot, i) when added to the route. Next, in step 908, it is determined whether the load capacity constraint and the traveling distance constraint are satisfied. If these two constraints are satisfied, in step 909, the setting is made so that the vehicle t passes through the delivery destination ii. Further, the delivery destination ii is set as delivered. Next, in step 910, the delivery destination ii is substituted for the delivery destination i, and the process returns to step 905 and loops. At step 908,
If the two constraints are not satisfied, at step 911, the vehicle
With t as the next vehicle, the process returns to step 902 and loops.

Next, the route optimization will be described. The vehicle routes resulting from the grouping are not always optimal. Therefore, it is necessary to rearrange the order of the delivery destinations included in the route to generate a shorter route. For routes with relatively few destinations, the full solution search method gives a fully optimized answer. For many routes, the full solution search is too slow. On the other hand, the accuracy of the 2OPT method decreases, but the processing is fast. Therefore, in the route optimization, the optimum solution is obtained by applying the full solution search method when the number of delivery destinations included in the route is equal to or less than a certain threshold, and by applying the 2OPT method when exceeding the threshold.

The algorithms of the full solution search method and the 2OPT method are known in the literature, and therefore the description thereof will be omitted.

The programs for the five delivery planning processes are not limited to the form executed by one processing unit in a time-sharing manner. For example, the programs executed by five central processing units in parallel. , Two central processing units, three
It is also possible to adopt a form in which two planning processes are shared and executed.

[0046]

As is clear from the above description, according to the present invention, grouping using a plurality of delivery planning strategies and route optimization processing using two types of methods are performed.
Since the delivery plan is made, an effective delivery plan can be made for the distribution of positions of a wide range of delivery destinations.

[Brief description of the drawings]

FIG. 1 is a hardware configuration diagram showing an embodiment of an optimal delivery planning system according to the present invention.

FIG. 2 is an overall configuration diagram of an optimal delivery planning process using five grouping strategies.

FIG. 3 is a flowchart illustrating an algorithm of a far-priority NN method.

FIG. 4 is a flowchart illustrating an algorithm of a far priority NI method.

FIG. 5 is a flowchart showing details of step 317 in FIG. 4;

FIG. 6 is a flowchart showing an algorithm for allocating a vehicle to a direction.

FIG. 7 is a flowchart illustrating an algorithm of a direction division NN method.

FIG. 8 is a flowchart showing an algorithm of the area division NI method.

FIG. 9 is a flowchart showing details of step 718 in FIG. 8;

FIG. 10 is a flowchart showing an algorithm of a sweep method.

[Explanation of symbols]

DESCRIPTION OF SYMBOLS 10 ... Keyboard, 20 ... Display, 30 ... Central processing unit, 40 ... Main storage device, 41 ... Delivery planning program group, 42 ... Work area, 50 ... Secondary storage device.

 ────────────────────────────────────────────────── ─── Continuing from the front page (72) Inventor Takashi Onoyama 6-81 Onoe-cho, Naka-ku, Yokohama-shi, Kanagawa Prefecture Hitachi Software Engineering Co., Ltd. In-house F-term (reference) 3F022 LL05 MM42 PP03 PP06 5B049 AA02 BB33 CC40 EE31 5B056 AA00 AA04 BB00 HH00 5H180 AA15 EE02 FF01

Claims (9)

[Claims] 1. An input device for inputting input information related to planning of a delivery plan, such as location information of a delivery destination, a package amount, and the number of delivery vehicles, and a plurality of delivery vehicles depart from a distribution base based on the input information. And a processing device for creating a delivery plan for minimizing the total mileage on the route returning to the original distribution base after delivering the cargo to the destination, and an output device for outputting the created delivery plan. Optimal delivery planning method in a delivery planning system, wherein a plurality of delivery planning processes of algorithms having different planning strategies are executed in a single processing unit in a time-division manner or in parallel by a plurality of processing units, and the execution is performed. An optimal delivery plan formulation method, wherein a processing result that minimizes the total mileage is selected from the results and output from the output device. 2. As one of the plurality of delivery plan planning processes, a delivery destination which is farthest from a distribution base among non-delivery destinations is selected as a delivery destination to which a delivery vehicle first travels, and thereafter, the delivery destination concerned is selected. 2. The optimal delivery planning method according to claim 1, wherein a delivery planning process comprising an algorithm for sequentially selecting a delivery destination closest to the destination and searching for a delivery route is used. 3. As one of the plurality of delivery plan drafting processes, a delivery destination which is farthest from a distribution base among non-delivery destinations is selected as a delivery destination to which a delivery vehicle first goes, and thereafter, a delivery route is selected. 2. An optimal delivery plan making method according to claim 1, wherein a delivery plan having an algorithm for searching for a delivery route by sequentially selecting delivery destinations with the smallest increase in the mileage when inserted into the delivery route is used. Method. 4. As one of the plurality of delivery plan planning processes, an omnidirectional delivery target area centered on a delivery base is divided into a plurality of areas, and a delivery destination to which a delivery vehicle first travels for each area. From the undelivered destinations, select a number of destinations that is proportional to the sum of the amount of packages of the destinations located in that area, and then select the destination closest to the destination in turn and deliver The method according to claim 1, wherein a delivery planning process configured with an algorithm for searching for a route is used. 5. As one of the plurality of delivery plan planning processes, an omnidirectional delivery target area centered on a delivery base is divided into a plurality of areas, and for each area, a delivery destination to which a delivery vehicle first travels. As a result, the number of delivery destinations that are proportional to the sum of the baggage amounts of the delivery destinations located in the area are selected from the undelivered delivery destinations, and thereafter, the increment of the traveling distance when inserted into the delivery route is the smallest. 2. The optimal delivery planning method according to claim 1, wherein a delivery planning process including an algorithm for sequentially selecting a destination and searching for a delivery route is used. 6. As one of the plurality of delivery plan planning processes, a straight line connecting a delivery destination and a distribution base and x on the delivery area map are displayed.
2. The method according to claim 1, further comprising the step of selecting a delivery destination in ascending order of an angle between the axis and the y-axis and searching for a delivery route. 7. The method according to claim 7, wherein a plurality of delivery plan drafting results are obtained.
A single delivery vehicle departs from a logistics base, delivers cargo to multiple destinations, and then returns to the original logistics base. The method according to any one of claims 1 to 6, wherein the optimization is performed by applying the 2OPT method to a large number of delivery routes. 8. An input device for inputting input information relating to the planning of a delivery plan, such as location information of a delivery destination, a package amount, the number of delivery vehicles, etc., and a plurality of delivery vehicles depart from a distribution base based on the input information. And a processing device for creating a delivery plan for minimizing the total mileage on the route returning to the original distribution base after delivering the cargo to the destination, and an output device for outputting the created delivery plan. The processing device, wherein the processing device executes a plurality of delivery plan planning processes of algorithms having different planning strategies in a time-sharing manner, and selects a processing result that minimizes the total mileage among the execution results. And a processing means for outputting from the output device. 9. An input device for inputting input information related to the planning of a delivery, such as location information of a delivery destination, a luggage quantity, the number of delivery vehicles, etc., and a plurality of delivery vehicles depart from a distribution base based on the input information. And a processing device for creating a delivery plan for minimizing the total mileage on the route returning to the original distribution base after delivering the cargo to the destination, and an output device for outputting the created delivery plan. A delivery plan drafting system, wherein the processing device is configured by a plurality of processing devices, each processing device includes a processing unit that executes a plurality of delivery schedule planning processes of algorithms having different planning strategies in parallel, An optimal delivery plan planning system, characterized in that one processing device is provided with a means for selecting a processing result with the shortest total traveling distance from among the execution results of the plurality of processing devices and outputting the result from the output device.