GB2615143A - Logistics distribution path optimization method based on multiple vehicles and multiple tasks - Google Patents

Logistics distribution path optimization method based on multiple vehicles and multiple tasks Download PDF

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GB2615143A
GB2615143A GB2202270.1A GB202202270A GB2615143A GB 2615143 A GB2615143 A GB 2615143A GB 202202270 A GB202202270 A GB 202202270A GB 2615143 A GB2615143 A GB 2615143A
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Li Xiaoping
Lin Shicheng
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Hainan Yile Iot Tech Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

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Abstract

The present invention relates to the technical field of logistics distribution. Disclosed is a logistics distribution path optimization method based on multiple vehicles and multiple tasks. The method comprises the following steps: A, importing or inputting a plurality of distribution points into a system, and collecting order distribution points; B, entering, into the system, the number of supporting vehicles for executing this batch of distribution tasks and the maximum number of distribution tasks which can be carried by each vehicle; C, (1) traversing the distribution points, and calculating center coordinates of all the distribution points, and (2) drawing a two-dimensional coordinate system by means of the calculated center coordinates, and performing regional classification on all the distribution points; and D, traversing a set of the distribution points, which have been classified to regions, so as to find the point in each region that is farthest away from the center coordinates. By means of the present invention, a multi-vehicle multi-task automatic allocation algorithm is used, and a distance is calculated after a location is classified, without the need to manually set allocation points; and a repeated and numerous computation amount is processed by using a multi-thread concurrent computing framework fork/join, and the correlation between a point and a distance is recorded by using a map data structure, thereby greatly improving the allocation efficiency.

Description

A LOGISTICS DISTRIBUTION PATH OPTIMIZATION METHOD BASED ON
MULI-VEHICLE AND MULTI-TASK
FIELD
The present disclosure relates to logistics distribution, and in particularly, to a logistics distribution path optimization method based on multi-vehicle and multi-task.
BACKGROUND
At present, the logistics distribution industry has developed from the previous single vehicle point-to-point distribution mode to a logistics distribution mode of multi-vehicle to multiple collector-distributor points, that is, multiple distribution vehicles complete all distribution tasks together.
However, the existing path optimization methods are generally more helpful to solve the optimization of point-to-point and single vehicle to multi-point, such as route planning from point A to point B, and optimal path for point A to pass through multiple points. For the distribution of multi-vehicle and multi-task, the optimal method should be to automatically distribute all distribution tasks to multiple vehicles to complete in parallel, and change the distribution task for the single vehicle to the distribution task for the single vehicle to the collector-distributor point which is closest to multiple distribution points, and then make a further distribution. Obviously, using the previous distribution route planning method may only be applied to one vehicle to complete all distribution tasks, or manually divide these distribution points, and then perform route planning for multiple vehicles, which obviously cannot effectively improve the efficiency and reduce distribution costs.
Therefore, those skilled in the art provide a logistics distribution path optimization method based on multi-vehicle and multi-task to solve the problems raised in the above background art.
SUMMARY
The object of the present disclosure is to provide a logistics distribution path optimization method based on multi-vehicle and multi-task to solve the problems raised in the above background art.
To achieve the above object, the present disclosure provides the following technical solutions: A logistics distribution path optimization method based on multi-vehicle and multi-task, comprising the following steps: A. importing or inputting multiple distribution points into a system to collect order distribution points; B. entering into the system a number of vehicles supporting this batch of distribution tasks and a maximum number of distribution tasks that can carried by each vehicle; C. T) traversing the distribution points, calculating center coordinates of all distribution points, and (Z. drawing a two-dimensional coordinate system with the calculated center coordinates, and dividing all distribution points into regions; D. traversing a set of distribution points which have been divided into regions to find the point that is farthest from the center coordinate in each region; E. (i) after finding the point farthest from the center in each region, calculating the distance between this point and other points in the region, and recording correspondence between points and distances, CZ sorting the distances in ascending order to get a distance from each point in each region to the center coordinate, and sorting by distance from near to far; F. dividing maximum distribution tasks for each vehicle in turn when combining with the maximum number of distribution tasks that can carried by the vehicle which is entered in step B, according to the distance order in each region, and recording the points that do not meet maximum number of distributions; G. processing the distribution points that have not been assiped in each region, these points are directly collected and distributed in turn until the number of distribution tasks returns to zero when combining with the maximum number of distribution tasks, since they are relatively close to the center coordinate.
As a further solution of the present disclosure: the system in the step A and the step B include a set of devices such as a computer display, a processor, and a memory, and the memory is used to store one or more programs, and the programs are loaded with several sets of instructions, and the instructions are used to implement steps C to C. As a further solution of the present disclosure: in the step C-T), the center coordinate is calculated by using a multi-threaded parallel computing framework fork/join newly added in JUK 1.8.
As a further solution of the present disclosure: the specific program of the multi-threaded parallel computing framework forkjoin is as follows: I, Given the values for the first location in the list: Lati, Ion', years!, months] and daysi Convert Lat.] and Lon] from degrees to radians.
lati = lati * P17180 lom =ton] *PI/180 II Convert Nylon to Cartesian coordinates for first location, Xi = cos(lati)* cos(loni) Yi = cos(lati)* sin(loni) Zi = sin(lati) 1111. Compute weight (by time) for first location.
vvi= (years! * 365.25) f (months] * 30.4375) I days] If locations are to be weighted equally, set wi, w2 etc all equal to 1.
IV, Repeat steps 1-3 for all remaining locations in the list.
V, Compute combined total weight for all locations.
Totweight -wi + + Wu VI. Compute weighted average x, y and z coordinates.
x = ((x * + (x2 * w2) + + * wi,)) totweight y = w1) + (y2 w2) + + (yn wn)) / totweight z = ((ii wi) + (z, * w2) + = (zn * wn)) / totweight VII, Convert average x, y, z coordinate to latitude and longitude. Note that in Excel and possibly some other applications, the parameters need to be reversed in the atan2 function, for example, use atan2(X,Y) instead of atan2(Y,X).
Lon -atan2(y, x) Hyp = sqrt(x * x ± y * y) Lat -atan2(z, hyp) VIII Convert lat and Ion to degrees.
lat -lat * 180/P1 Ion -Ion * 180/P1 IX Special case: If abs(x) < 10-9 and abs(y) < 10-9 and abs(z) < 10-9 then the geographic midpoint is the center of the earth.
As a further solution of th.e present disclosure: in the step C-©, the multi-threaded parallel. computing framework is used to calculate the region where the distribution point is located relative to the center coordinate, and each region corresponds to a direction, and at least four directions arc set: southwest, northwest, southeast, northeast, etc., a set of distribution points included in these directions can be obtained.
As a further solution of the present disclosure: after traversing all the points in the step D, using the Pythagorean theorem to calculate the distance of individual coordinate point, and find the farthest point.
As a further solution of the present disclosure: in the step E-a), a map data structure of key-value is used to record the correspondence between points and distances.
As a further solution of the present disclosure: in the step E-©, sorting the map structure data by distance by using the characteristics of JDK1.8 streaming processing set, and the coordinate points as keys are also ordered.
As a further solution of the present disclosure: in the step 1', distributing the distribution points to the vehicle in order of distance when combining with the maximum carrying number of the vehicle by using the calculation method of taking the modulus of the maximum carrying number using the total number of points in individual region.
Compared with the prior art, the present disclosure has the following beneficial effects: 1. using the automatic distribution algorithm of multi-vehicle and multi-task, calculating the distance after dividing the direction, and no need to manually set the distribution point; 2. using the multi-thread parallel computing framework fork/join to process repeated and numerous calculations, and using map data structure to record the correspondence between points and distances, which greatly improves the distribution efficiency.
BRIEF DESCRIPTION OWEHE DRAWINGS
FIG. 1 is a schematic diagram of the main flow of a logistics distribution path optimization method based on multi-vehicle and multi-task; FIG. 2 is a schematic diagram of a sub-flow of a logistics distribution path optimization method based on multi-vehicle and multi-task.
DETAILED DESCRIPTION
Referring to Figs. I to 2, in the embodiment of the present disclosure, a logistics distribution path optimization method based on multi-vehicle and multi-task, comprising the following steps: A. importing or inputting multiple distribution points into a system to collect order dis button points; B. entering into the system a number of vehicles supporting this batch of distribution tasks and a maximum number of distribution tasks that can carried by each vehicle; The system in the step A and the step B include a set of devices such as a computer display, a processor, and a memory, and the memory is used to store one or more programs, and the programs are loaded with several sets of instructions, and the instructions are used to implement steps C to G. C. i.,1) traversing the distribution points, calculating center coordinates of all distribution points, and drawing a two-dimensional coordinate system with the calculated center coordinates, and dividing all distribution points into regions; In the step C-0), the center coordinate is calculated by using a multi-threaded parallel computing framework fork/join newly added in JDK1.8.
The specific program of the multi-threaded parallel computing framework fork/join is as follows: I, Given the values for the first location in the list: Lai], loni, years], months, and daysi Convert Lati and Lon' from degrees to radians.
lati =lat.) * FF180 lom lom * PI/180 Convert latilon to Cartesian coordinates for first location.
XI -cos(lati)* cos(loru) Yi = cos(lati)* sin(Iom) Zi = sin(lati) III, Compute weight (by time) for first location.
wi= (years! * 365.25) + (months] * 30.4375) + days! I r locations are to be weighted equally, set wi, w2 etc all equal to I. IV, Repeat steps 1-3 for all remaining locations in the list.
V, Compute combined total weight for all locations. Tot Weight = wi + W2 + + VI, Compute weighted average x, y and z coordinates.
x = ((xi * WI) + (x2 * w2) + + (xn * w,,)); totweight y = ((yi * WI) ..f. (y2 * w2) * w,,)) / totweight z = ((zi * + (z2 * w2) + + (z" * w,i))/ totweight VII, Convert average x, y, z coordinate to latitude and longitude. Note that in Excel and possibly some other applications, the parameters need to be reversed in the ittan2 function, for example, use atan2(X,Y) instead of atan2(Y,X).
Lon = atan2(y, x) Hyp = sqrt(x * x y * y) Lat = atan2(z, hyp) VIM Convert lat and Ion to degrees.
lat = lat * 180/P1 Ion = Ion * 180/PT IX, Special case: If abs(x) < 10 and abs(y) < 10-9 and abs(z) < 10' then the geographic midpoint is the center of the earth.
In the step C-©, the multi-threaded parallel computing framework is used to calculate the region where the distribution point is located relative to the center coordinate, and each region corresponds to a direction, and at least four directions are set: southwest, northwest, southeast, northeast, etc. a set of distribution points included in these directions can be obtained.
D. traversing a set of distribution points which have been divided into regions to find the point that is farthest from the center coordinate in each region; After traversing all the points in the step D, using the Pythagorean theorem to calculate the distance of individual coordinate point, and find the farthest point.
E. ED after finding the point farthest from the center in each region, calculating the distance between this point and other points in the region, and recording correspondence between points and distances, i".:49 sorting the distances in ascending order to get a distance from each point in each region to the center coordinate, and sorting by distance from near to far; In the step E-ct, a map data structure of key-value is used to record the correspondence between points and distances.
In the step E-C4, sorting the map data structure by distance by using the characteristics of SDK 1.8 streaming processing set, and the coordinate points as keys are also ordered.
F. dividing maximum distribution tasks for each vehicle in turn when combining with the maximum number of distribution tasks that can carried by the vehicle which is entered in step B, according to the distance order in each region, and recording the points that do not meet maximum number of distributions; In the step F, distributing the distribution points to the vehicle in order of distance when combining with the maximum carrying number of the vehicle by using the calculation method of taking the modulus of the maximum carrying number using the total number of points in individual region.
G. processing the distribution points that have not been assigned in each region, these points are directly collected and distributed in turn until the number of distribution tasks returns to zero when combining with the maximum number of distribution tasks, since they are relatively close to the center coordinate.
The above arc only preferred specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto. The equivalent replacement or change made by those skilled familiar with the technical field according to the technical solution and the inventive concept of the present disclosure within the technical scope disclosed by the present disclosure should be included within the protection scope of the present invention.

Claims (9)

  1. What is claimed is: I. A logistics distribution path optimization method based on multi-vehicle and multi-task, comprising the following steps: A. importing or inputting multiple distribution points into a system to collect order distribution points; B. entering into the system a number of vehicles supporting this batch of distribution tasks and a maximum number of distribution tasks that can carried by each vehicle; C. ED traversing the distribution points, calculating center coordinates of all distribution points, and i?) drawing a two-dimensional coordinate system with the calculated center coordinates, and dividing all distribution points into regions; D. traversing a set of distribution points which have been divided into regions to find the point that is farthest from the center coordinate in each region; E. 0) after finding the point farthest from the center in each region, calculating the distance between this point and other points in the region, and recording correspondence between points and distances, sorting the distances in ascending order to get a distance from each point in each region to the center coordinate, and sorting by distance from near to far; F. dividing maximum distribution tasks for each vehicle in turn when combining with the maximum number of distribution tasks that can carried by the vehicle which is entered in step B, according to the distance order in each region, and recording the points that do not meet maximum number of distributions; G. processing the distribution points that have not been assigned in each region, these points are directly collected and distributed in turn until the number of distribution tasks returns to zero when combining with the maximum number of distribution tasks, since they are relatively close to the center coordinate.
  2. 2. The logistics distribution path optimization method based on multi-vehicle and multi-task according to claim 1, wherein the system in the step A and the step B include a set of devices such as a computer display, a processor, and a memory, and the memory is used to store one or more programs, and the programs are loaded with several sets of instructions, and the instructions are used to implement steps C to G.
  3. 3. The logistics distribution path optimization method based on multi-vehicle and multi-task according to claim 1, wherein in the step C-©, the center coordinate is calculated by using a multi-threaded parallel computing.framework fork/join newly added in JDK1.8.
  4. 4. The logistics distribution path optimization method based on multi-vehicle and multi-task according to claim. 3, wherein the specific program of the multi-threaded parallel, computing framework fork/join is as follows: I, Given the values for the first location in the list: I,ati, loni, yearsi, months, and daysi Convert Lan and Lon' from degrees to radians.Ian = lan * PH 80 Ion = Ion * PI/180 Convert latilon to Cartesian coordinates for first location, = cos(lan)* cos(lom) Y, = cos(lan) * sin(tom) = sin(lati) III, Compute weight (by time) for first location.wi= (yearsi * 365.25) -F (months, * 30.4375) + days! If locations are to be weighted equally, set wi, w2 etc all equal to 1.IV. Repeat steps 1-3 for all remaining locations in the list.V, Compute combined total weight for all locations.Totweight = w2+ + w,, VI. Compute weighted average x, y and z coordinates.x = ((x, * wi) + (x2 * w2) + + (xn * wn)) totweight y -gyi vi) (y2 *W2).1-*** + (Yn * WO) totweight z = ((zi * + (z2 * 4112) (1Zn * IWO) totweight VII, Convert average x, y, z coordinate to latitude and longitude. Note that in Excel and possibly some other applications, the parameters need to be reversed in the atan2 function, for example, use atan2(X,Y) instead of atan2(Y,X).Lon = atan2(y, x) Hyp = sq11.(x * x + y * y) Lat = atan2(z, hyp) VIII, Convert at and ion to degrees. lat = lat * 180/PI Ion = ion * 180/P1 IX, Special case: If abs(x) < iO and abs(y) < 10-9 and abs(z) 10-9 then the geographic midpoint is the center of the earth.
  5. 5. The logistics distribution path optimization method based on multi-vehicle and multi-task according to claim 1, wherein in the step C4, the multi-threaded parallel computing framework is used to calculate the region where the distribution point is located relative to the center coordinate, and each region corresponds to a direction, and at least four directions are set: southwest, northwest, southeast, northeast, etc., a set of distribution points included in these directions can be obtained.
  6. 6. The logistics distribution path optimization method based on multi-vehicle and multi-task according to claim 1, wherein after traversing all the points in the step D, using the Pythagorean theorem to calculate the distance of individual coordinate point, and find the farthest point.
  7. 7. The logistics distribution path optimization method based on multi-vehicle and multi-task according to claim 1, wherein in the step E-01), a map data structure of key-value is used to record the correspondence between points and distances.
  8. 8. The logistics distribution path optimization method based on multi-vehicle and multi-task according to claim 7, wherein in the step E-(?), sorting the map structure data by distance by using the characteristics of JDK1.8 streaming processing set, and the coordinate points as keys arc also ordered.
  9. 9. The logistics distribution path optimization method based on multi-vehicle and multi-task according to claim 1, wherein in the step F, distributing the distribution points to the vehicle in order of distance when combining with the maximum carrying number of the vehicle by using the calculation method of taking the modulus of the maximum carrying number using the total number of points in individual region.
GB2202270.1A 2021-04-23 2021-12-27 Logistics distribution path optimization method based on multiple vehicles and multiple tasks Pending GB2615143A (en)

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CN202110463380.2A CN115239221A (en) 2021-04-23 2021-04-23 Multi-vehicle multi-task-based logistics distribution path optimization method
PCT/CN2021/141491 WO2022222531A1 (en) 2021-04-23 2021-12-27 Logistics distribution path optimization method based on multiple vehicles and multiple tasks

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105184412A (en) * 2015-09-21 2015-12-23 北京农业信息技术研究中心 Logistics delivery route planning method and system based on geographic positions
CN108960728A (en) * 2018-07-04 2018-12-07 浪潮软件股份有限公司 A kind of allocator and device
CN110189073A (en) * 2019-04-17 2019-08-30 北京百度网讯科技有限公司 Route planning method, device, equipment and computer readable storage medium
US20200005240A1 (en) * 2018-06-29 2020-01-02 Hitachi, Ltd. Delivery planning device, delivery planning system, and delivery planning method
CN112001557A (en) * 2020-08-31 2020-11-27 物联云仓(成都)科技有限公司 TMS system-based logistics distribution path optimization method, storage medium and computer equipment

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105184412A (en) * 2015-09-21 2015-12-23 北京农业信息技术研究中心 Logistics delivery route planning method and system based on geographic positions
US20200005240A1 (en) * 2018-06-29 2020-01-02 Hitachi, Ltd. Delivery planning device, delivery planning system, and delivery planning method
CN108960728A (en) * 2018-07-04 2018-12-07 浪潮软件股份有限公司 A kind of allocator and device
CN110189073A (en) * 2019-04-17 2019-08-30 北京百度网讯科技有限公司 Route planning method, device, equipment and computer readable storage medium
CN112001557A (en) * 2020-08-31 2020-11-27 物联云仓(成都)科技有限公司 TMS system-based logistics distribution path optimization method, storage medium and computer equipment

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