CN113313331A - Emergency material multi-cycle segmentation distribution method and device based on inventory gap risk - Google Patents

Emergency material multi-cycle segmentation distribution method and device based on inventory gap risk Download PDF

Info

Publication number
CN113313331A
CN113313331A CN202110867289.7A CN202110867289A CN113313331A CN 113313331 A CN113313331 A CN 113313331A CN 202110867289 A CN202110867289 A CN 202110867289A CN 113313331 A CN113313331 A CN 113313331A
Authority
CN
China
Prior art keywords
cycle
scheduling
risk
period
emergency
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202110867289.7A
Other languages
Chinese (zh)
Other versions
CN113313331B (en
Inventor
张晓春
朱远祺
陈振武
邢锦江
吴宗翔
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shenzhen Urban Transport Planning Center Co Ltd
Original Assignee
Shenzhen Urban Transport Planning Center Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shenzhen Urban Transport Planning Center Co Ltd filed Critical Shenzhen Urban Transport Planning Center Co Ltd
Priority to CN202110867289.7A priority Critical patent/CN113313331B/en
Publication of CN113313331A publication Critical patent/CN113313331A/en
Application granted granted Critical
Publication of CN113313331B publication Critical patent/CN113313331B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • 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
    • G06Q10/083—Shipping

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Quality & Reliability (AREA)
  • Development Economics (AREA)
  • Marketing (AREA)
  • Operations Research (AREA)
  • Tourism & Hospitality (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Game Theory and Decision Science (AREA)
  • Educational Administration (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention discloses an emergency material multi-period segmentation distribution method and device based on inventory gap risks, wherein the method comprises the following steps: dividing the total time of the emergency plan into a plurality of scheduling periods at preset period intervals; for any material point, dividing the material point into a plurality of equal-part virtual node sets according to the capacity; solving the multi-cycle vehicle path model to obtain a material scheduling scheme of the current scheduling cycle, executing the material scheduling scheme of the current scheduling cycle, updating the reserve quantity of material points, and updating the service state of the virtual nodes according to the material scheduling scheme; and calculating material gaps of each material point at the end of the current scheduling period according to the preset period interval and the material consumption speed, updating the service state of the virtual node according to the preset period interval and the material consumption speed, updating the objective function of the multi-period vehicle path model, and entering the calculation of the next scheduling period. The invention can generate the sequence of using and visiting the stations of the vehicle at the execution level, so that the optimization result has more actual reference value.

Description

Emergency material multi-cycle segmentation distribution method and device based on inventory gap risk
Technical Field
The invention relates to the technical field of material distribution and vehicle routing, in particular to an emergency material multi-period segmentation distribution method and device based on inventory gap risks.
Background
Various sudden disaster accidents have serious destructiveness, which causes great damage to the daily life of people. After a disaster accident occurs, emergency materials need to be delivered to a disaster site. In the prior art, the problem of multi-cycle emergency material distribution only concerns the problem of material distribution, namely only concerns the destination and the corresponding quantity of material sources, and an economically feasible vehicle path scheme is not provided.
Disclosure of Invention
The invention solves the problem that the existing material distribution problem does not provide an economically feasible vehicle path scheme.
The invention provides an emergency material multi-period segmentation distribution method based on inventory gap risks, which comprises the following steps:
step 1: dividing the total time length of the emergency plan into a plurality of dispatching cycles at preset cycle intervals, using i to represent the serial numbers of the dispatching cycles,
Figure DEST_PATH_IMAGE001
(ii) a For any material point, dividing the material point into material points according to capacity
Figure 783733DEST_PATH_IMAGE002
A set of virtual nodes that are equal in size,
Figure DEST_PATH_IMAGE003
；
step 2: solving the multi-cycle vehicle path model to obtain the first
Figure 177805DEST_PATH_IMAGE004
Material scheduling scheme of scheduling periodExecution of the first
Figure 385801DEST_PATH_IMAGE004
A material scheduling scheme for each scheduling period;
and step 3: after the execution is finished
Figure 712877DEST_PATH_IMAGE004
After the material scheduling scheme of each scheduling period, updating the reserve quantity of material points, and updating the service state of the virtual nodes according to the material scheduling scheme;
and 4, step 4: calculating material gaps of each material point at the end of the current scheduling period according to the preset period interval and the material consumption speed, updating a virtual node service state according to the preset period interval and the material consumption speed, and updating a target function of the multi-period vehicle path model;
and 5: order to
Figure DEST_PATH_IMAGE005
If, if
Figure 200491DEST_PATH_IMAGE006
Then go back to execute the step 2, if
Figure DEST_PATH_IMAGE007
Then the scheduling is finished.
Optionally, the objective function of the multi-cycle vehicle path model is:
Figure 81859DEST_PATH_IMAGE008
Figure DEST_PATH_IMAGE009
Figure 844279DEST_PATH_IMAGE010
Figure DEST_PATH_IMAGE011
wherein the content of the first and second substances,
Figure 494703DEST_PATH_IMAGE012
for a positive number greater than the preset value,
Figure DEST_PATH_IMAGE013
a fleet set is represented as a set of vehicles,
Figure 169529DEST_PATH_IMAGE014
represents a vehicle number, 0 represents a distribution center,
Figure DEST_PATH_IMAGE015
a set of virtual nodes is represented that is,
Figure 538193DEST_PATH_IMAGE016
、
Figure DEST_PATH_IMAGE017
each of which represents a virtual node,
Figure 573145DEST_PATH_IMAGE018
representative arc
Figure DEST_PATH_IMAGE019
The driving distance of the vehicle is set to be,
Figure 343655DEST_PATH_IMAGE020
is shown as
Figure DEST_PATH_IMAGE021
The decision variables of the cycle are varied in such a way that,
Figure 687918DEST_PATH_IMAGE022
indicating the value of the access reward,
Figure DEST_PATH_IMAGE023
the status of the service is indicated,
Figure 543878DEST_PATH_IMAGE024
representing virtual nodes
Figure DEST_PATH_IMAGE025
The corresponding inventory gap space-time risk,
Figure 382521DEST_PATH_IMAGE026
indicating that the preset period interval is set to be,
Figure DEST_PATH_IMAGE027
representing virtual nodes
Figure 7538DEST_PATH_IMAGE028
Corresponding material point
Figure DEST_PATH_IMAGE029
At said predetermined periodic intervals
Figure 539013DEST_PATH_IMAGE026
The integral of the inner one of the two,
Figure 882270DEST_PATH_IMAGE030
for material point
Figure 275336DEST_PATH_IMAGE029
The size of the (c) is (d),
Figure DEST_PATH_IMAGE031
for material point
Figure 754859DEST_PATH_IMAGE029
The risk factor of inventory gaps of (a),
Figure 457236DEST_PATH_IMAGE032
for material point
Figure 287789DEST_PATH_IMAGE029
The number of the capacity is divided into parts,
Figure DEST_PATH_IMAGE033
first finger
Figure 733814DEST_PATH_IMAGE021
The time of execution of the periodic scheduling scheme.
Optionally, the updating the service state of the virtual node according to the material scheduling scheme includes:
setting the service states of all accessed virtual nodes in the emergency plan of the current scheduling period as
Figure 333422DEST_PATH_IMAGE034
Setting the service state of the nodes with preset number for the virtual nodes which are not served in the emergency plan of the current scheduling period
Figure DEST_PATH_IMAGE035
。
Optionally, the preset number is:
Figure 190389DEST_PATH_IMAGE036
，
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE037
as the ith period material point
Figure 508237DEST_PATH_IMAGE029
The minimum amount of the delivery of the liquid,
Figure 757953DEST_PATH_IMAGE038
for material point
Figure 212068DEST_PATH_IMAGE029
The total capacity of (c).
Alternatively,
Figure DEST_PATH_IMAGE039
wherein the content of the first and second substances,
Figure 990668DEST_PATH_IMAGE040
for material point
Figure 795813DEST_PATH_IMAGE029
The minimum guaranteed amount of reserve of the tank,
Figure DEST_PATH_IMAGE041
the speed of the consumption of the material is shown,
Figure 583641DEST_PATH_IMAGE042
reserve the amount of materials before dispatching.
Optionally, the updating the service state of the virtual node according to the preset cycle interval and the material consumption speed includes:
calculating the number of virtual nodes to be consumed according to the material consumption speed in the preset period interval, and setting the service state of the part of virtual nodes as
Figure DEST_PATH_IMAGE043
。
Optionally, the constraint conditions of the multi-cycle vehicle path model are:
Figure 642995DEST_PATH_IMAGE044
Figure DEST_PATH_IMAGE045
Figure 592496DEST_PATH_IMAGE046
Figure DEST_PATH_IMAGE047
Figure 884937DEST_PATH_IMAGE048
Figure DEST_PATH_IMAGE049
Figure 945297DEST_PATH_IMAGE050
wherein the decision variables
Figure DEST_PATH_IMAGE051
Representative vehicle
Figure 357693DEST_PATH_IMAGE052
Whether or not to pass through arc
Figure 274833DEST_PATH_IMAGE054
And 0 represents a number of the distribution center,
Figure DEST_PATH_IMAGE055
is a set of nodes to be served,
Figure 523412DEST_PATH_IMAGE056
is a collection of a fleet of vehicles,
Figure DEST_PATH_IMAGE057
which represents the number of the vehicle,
Figure 387463DEST_PATH_IMAGE058
is a node to be served by a node,
Figure DEST_PATH_IMAGE059
represents
Figure 936256DEST_PATH_IMAGE058
The amount of demand for the point(s),
Figure 493139DEST_PATH_IMAGE060
is a collection of network arcs and is,
Figure DEST_PATH_IMAGE061
represents a segment of an arc in the network,
Figure 979746DEST_PATH_IMAGE062
represents
Figure 913067DEST_PATH_IMAGE058
The arc-out of the point is realized,
Figure DEST_PATH_IMAGE063
represents
Figure 50788DEST_PATH_IMAGE058
The arc-in of the point is formed,
Figure 44151DEST_PATH_IMAGE064
set of representative material points
Figure DEST_PATH_IMAGE065
All of the arc-out points of (c),
Figure 267322DEST_PATH_IMAGE066
indicating vehicles
Figure 738755DEST_PATH_IMAGE052
Whether or not to pass through arc
Figure DEST_PATH_IMAGE067
。
Optionally, the method further comprises:
presetting a value range of a stock gap risk coefficient and the preset period interval;
respectively executing the steps 1-5 according to parameter combinations composed of different inventory gap risk coefficients and preset period intervals to obtain material scheduling schemes under different parameter combinations and corresponding transportation cost and risk cost;
and normalizing the transportation cost and the risk cost of all the material scheduling schemes, calculating the weighted sum to obtain dimensionless comprehensive cost, determining the material scheduling scheme with the lowest comprehensive cost and the corresponding optimal parameter combination thereof, and taking the material scheduling scheme with the lowest comprehensive cost as the optimal scheme for multi-period emergency material scheduling.
The invention further provides an emergency material multi-period segmentation and distribution device based on the inventory gap risk, which comprises a computer readable storage medium and a processor, wherein the computer readable storage medium stores a computer program, and the computer program is read by the processor and runs to realize the emergency material multi-period segmentation and distribution method based on the inventory gap risk.
The invention further provides a computer-readable storage medium, which stores a computer program, and when the computer program is read and executed by a processor, the method for multi-cycle segmentation and distribution of emergency materials based on the risk of inventory gaps is implemented.
The multi-cycle vehicle path problem model which is constructed under different risk estimations and takes the reward collection, the division and the distribution as well as the capacity constraint into consideration not only pays attention to the material source and the destination, but also generates the vehicle use, the station access sequence and the like of the logistics scheme execution level, so that the optimization result has more actual reference value, and the utilization efficiency of the transportation resource is improved; the invention divides the material points into the virtual node sets according to the capacity so as to split the requirements of the material points, thereby improving the full load rate of vehicles and the utilization efficiency of transportation resources; the invention considers the periodic initial gap risk in the multi-period vehicle path model by utilizing the reward collection mechanism, in the reward collection model, the vehicle does not need to visit all demand points any more, but encourages the vehicle to visit the distribution points with higher priority preferentially through a certain reward mechanism, and under the condition that the transportation resources are limited in the actual emergency material scheduling scene, the material scheduling scheme can preferentially ensure the material supply of the region with higher potential risk so as to reduce the global loss.
Drawings
FIG. 1 is a schematic diagram showing the reserve amount of material points in the first 3 periods as a function of time;
FIG. 2 is a schematic diagram of an embodiment of a multi-cycle division distribution method for emergency materials based on risk of inventory gaps according to the present invention;
FIG. 3 is a schematic plan view of an emergency materials dispatch vehicle path plan at time step 0;
FIG. 4 is a schematic plan view of an emergency materials dispatch vehicle path plan at time step 1;
FIG. 5 is a schematic plan view of an emergency materials dispatch vehicle path plan at time step 2;
FIG. 6 is a schematic plan view of an emergency materials dispatch vehicle path plan at time step 3;
FIG. 7 is a schematic plan view of an emergency materials dispatch vehicle path plan at time step 4;
FIG. 8 is a schematic plan view of an emergency materials dispatch vehicle path plan at time step 5;
FIG. 9 is a schematic plan view of an emergency materials dispatch vehicle path plan at time step 6;
FIG. 10 is a schematic plan view of an emergency materials dispatch vehicle routing scheme at time step 7;
FIG. 11 is a schematic plan view of an emergency materials dispatch vehicle path plan at time step 8;
FIG. 12 is a three-dimensional schematic diagram of an emergency material dispatching vehicle path scenario at time steps 0-60;
fig. 13 is a schematic diagram of the logistics cost and the risk of the inventory gap with an estimated risk coefficient f =1 and a preset period interval T = 1;
fig. 14 is a schematic diagram of the estimated risk coefficient f =9 and the logistics cost and the inventory gap risk at the preset cycle interval T = 1;
fig. 15 is a schematic diagram of the estimated risk coefficient f =22 and the logistics cost and the inventory gap risk at the preset cycle interval T = 1;
FIG. 16 is a multi-dimensional spatial schematic of the composite cost at different estimated risk factors and preset periodic intervals;
fig. 17 shows the variation of the logistics cost with the estimated risk coefficient on the premise of a certain preset period interval;
fig. 18 is a diagram showing the change of the logistics cost with the preset period interval on the premise that the estimated risk coefficient is fixed;
FIG. 19 is a diagram showing the variation of the gap risk of the materials with the estimated risk coefficient under the premise of a certain interval of the preset period;
fig. 20 shows the variation of the material gap risk with the preset period interval on the premise that the estimated risk coefficient is constant.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
To facilitate understanding of the present invention, key words related to the present invention are briefly explained.
Vehicle Routing Problem (VRP) is the set of best routes to travel to provide travel services for a group of customers to solve a group of vehicles. The multi-period material scheduling refers to performing multiple times of material scheduling according to different period intervals in a longer time, and searching for an optimal combined scheduling scheme under different periods. In conventional research, the multi-cycle scheduling problem is modeled as an allocation/assignment problem, generally without concern for logistics-related attributes such as specific vehicle transportation paths and vehicle capacities/quantities. The present invention addresses the multi-cycle Vehicle Routing Problem (VRP) whose solution is a specific set of performable vehicle access site sequences, including the number of vehicles used and the corresponding vehicle capacities.
Periodically needing emergency materials: the emergency supplies can be divided into disposable demand supplies (such as quilts and tents) and periodic demand supplies (such as foods and sanitary supplies) according to consumption types. The type of materials researched by the invention is periodically required materials consumed regularly, one characteristic of the materials is that the materials can be lost along with time and the speed is stable, and the other characteristic is that the stock can not be lower than a certain level at any time so as to guarantee the emergency disaster relief requirement.
Risk of inventory gaps: the shortage of points presents a potential risk, which is affected by the size of the points (serving area/population).
And (3) reward collection: in the reward collection model, the vehicle no longer needs to visit all demand points, but rather is encouraged to visit higher priority (potentially high risk) delivery points preferentially by some reward mechanism. In the case of limited resources, the optimization scheme will prioritize the higher risk of service material points to minimize global losses.
Referring to fig. 2, in an embodiment of the emergency material multi-cycle distribution method based on the risk of inventory gaps, the multi-cycle distribution method includes:
step 1:
step 1.1: and dividing the total time of the emergency plan into a plurality of scheduling periods at preset period intervals. And generating a corresponding material scheduling scheme in each scheduling period, and distributing materials for corresponding material points.
Wherein, i represents the sequence number of the scheduling cycle,
Figure 996561DEST_PATH_IMAGE068
and the initial value of i is 0. The predetermined period interval is a fixed time interval
Figure DEST_PATH_IMAGE069
，
Figure 144514DEST_PATH_IMAGE070
Using a set of exponential integers, using a predetermined periodic interval
Figure 120561DEST_PATH_IMAGE026
Will total time length of
Figure DEST_PATH_IMAGE071
Is divided into
Figure 864526DEST_PATH_IMAGE072
In each scheduling period, the sequence number set of the scheduling period is as follows:
Figure DEST_PATH_IMAGE073
，
period of time
Figure 242418DEST_PATH_IMAGE021
In a scheduling scheme
Figure 312005DEST_PATH_IMAGE033
Executing at any moment:
Figure 775347DEST_PATH_IMAGE074
，
the execution time of the material scheduling scheme is generally far less than the scheduling period
Figure 339315DEST_PATH_IMAGE026
The multicycle vehicle path model assumes that it begins at each cycle
Figure 571713DEST_PATH_IMAGE033
And executing the material scheduling scheme at any moment to finish material scheduling (the process shown by a black solid line in figure 1).
Set the coverage of the emergency plan
Figure DEST_PATH_IMAGE075
Individual material points, each material point
Figure 77781DEST_PATH_IMAGE029
Has a total capacity of
Figure 762840DEST_PATH_IMAGE076
The minimum guaranteed reserve is
Figure DEST_PATH_IMAGE077
. In that
Figure 114187DEST_PATH_IMAGE033
At the moment, the reserve amount of materials before dispatching is
Figure 201091DEST_PATH_IMAGE078
The scheduled material reserve amount is
Figure DEST_PATH_IMAGE079
. The reserve amount of each material point is supplemented after the material scheduling, and the period is
Figure 596170DEST_PATH_IMAGE026
Internal natural losses. The reserve quantity of the material points in any period needs to meet the following constraint:
Figure 768525DEST_PATH_IMAGE080
for any period
Figure DEST_PATH_IMAGE081
Material point
Figure 657984DEST_PATH_IMAGE029
The reserve volume relationship before and after scheduling is:
Figure 130553DEST_PATH_IMAGE082
，
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE083
is as follows
Figure 712844DEST_PATH_IMAGE021
Periodic material scheduling scheme at material points
Figure 372496DEST_PATH_IMAGE029
Total delivery amount of (c).
Step 1.2: for any material point, dividing the material point into material points according to capacity
Figure 93676DEST_PATH_IMAGE002
Equal parts of virtual node sets.
For any material point
Figure 889593DEST_PATH_IMAGE029
Will capacity of
Figure 908365DEST_PATH_IMAGE084
Is divided into
Figure DEST_PATH_IMAGE085
Equal parts of virtual node set
Figure 55312DEST_PATH_IMAGE086
Original network non-distribution center node set
Figure DEST_PATH_IMAGE087
Is divided into virtual node sets
Figure 552153DEST_PATH_IMAGE088
The logistics cost between the virtual nodes is 0. Put the material point
Figure 468156DEST_PATH_IMAGE029
After cutting
Figure DEST_PATH_IMAGE089
A virtual node is represented as
Figure 392250DEST_PATH_IMAGE090
Virtual node
Figure DEST_PATH_IMAGE091
Material demand of
Figure 10182DEST_PATH_IMAGE092
Comprises the following steps:
Figure DEST_PATH_IMAGE093
，
based on the above virtual node
Figure 45134DEST_PATH_IMAGE021
Material point pair by periodic material scheduling scheme
Figure 81223DEST_PATH_IMAGE029
Total material demand for replenishment
Figure 176218DEST_PATH_IMAGE083
Comprises the following steps:
Figure 563337DEST_PATH_IMAGE094
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE095
is as follows
Figure 401980DEST_PATH_IMAGE021
The periodic material scheduling scheme decision variables,
Figure 777729DEST_PATH_IMAGE096
to represent
Figure 309204DEST_PATH_IMAGE016
In the form of a virtual node, the node is,
Figure DEST_PATH_IMAGE097
to represent
Figure 652461DEST_PATH_IMAGE017
As a set of virtual nodes
Figure 560374DEST_PATH_IMAGE015
And in distribution center node and
Figure 305476DEST_PATH_IMAGE016
the number of different nodes is such that,
Figure 7853DEST_PATH_IMAGE098
refers to the upper limit of the capacity of the vehicle,
Figure DEST_PATH_IMAGE099
finger-shaped
Figure 572827DEST_PATH_IMAGE016
The amount of demand for the point(s),
Figure 268119DEST_PATH_IMAGE056
is a collection of a fleet of vehicles,
Figure 867728DEST_PATH_IMAGE057
representing the vehicle number.
Since the demand for a single material point may exceed the capacity of the vehicle, a limited capacity of vehicles may be used for co-transportation for demand splits. And the demands of different material points are different, and the full load rate of the vehicle can be improved by adopting segmentation delivery.
Before the step 1-step 5 are executed, initializing material point data, vehicle parameters and a plan total duration L, wherein the material point data comprises data such as material point positions and material point specifications, and the vehicle parameters comprise vehicle capacity; and set a period interval
Figure 741006DEST_PATH_IMAGE026
Risk factor of gap of material
Figure 324434DEST_PATH_IMAGE100
，
Figure 308570DEST_PATH_IMAGE021
The value is assigned to 0.
Step 2: solving the multi-cycle vehicle path model to obtain the first
Figure 28265DEST_PATH_IMAGE021
A material scheduling scheme for each scheduling cycle is performed
Figure 72444DEST_PATH_IMAGE021
And (4) a material scheduling scheme of each scheduling period.
The multi-cycle vehicle path model can be solved by adopting a local search algorithm, and the solving method is the prior art and is not described herein. The directly obtained solution is a set of virtual node access sequences, for example, in a physical site ID (virtual node number) format, there are physical sites 1-9, and the solution may be 1(0) -1(1) -3(0) -6 (0) -9 (0), that is, the access sequence is: virtual node No. 0 of physical site 1-virtual node No. 1 of physical site 1-virtual node No. 0 of physical site 3-virtual node No. 0 of physical site 9. Based on the obtained solution, an executable vehicle access station sequence may be further generated, and if a plurality of virtual nodes of the same physical station are consecutively accessed in one solution, only one physical station is reserved in the vehicle access station sequence generated based on the solution, for example, assuming that the solution is: 1(0) -1(1) -3(0) -6 (0) -9 (0), the obtained vehicle access station sequence is: 1-3-6-9, assuming the solution is: 1(2) -3(1) -6 (0) -9 (0), the obtained vehicle visiting station sequence is also: 1-3-6-9.
The optimization goals of the multi-cycle vehicle path model are as follows:
Figure DEST_PATH_IMAGE101
Figure 877589DEST_PATH_IMAGE102
Figure 665416DEST_PATH_IMAGE010
Figure 990350DEST_PATH_IMAGE011
wherein the content of the first and second substances,
Figure 205430DEST_PATH_IMAGE012
for a positive number greater than the preset value,
Figure 232292DEST_PATH_IMAGE013
a fleet set is represented as a set of vehicles,
Figure 823811DEST_PATH_IMAGE014
represents a vehicle number, 0 represents a distribution center,
Figure 252518DEST_PATH_IMAGE015
a set of virtual nodes is represented that is,
Figure 638500DEST_PATH_IMAGE016
、
Figure 683816DEST_PATH_IMAGE017
all represent virtual sectionsThe point(s) is (are) such that,
Figure 813446DEST_PATH_IMAGE018
representative arc
Figure 96660DEST_PATH_IMAGE019
The driving distance of the vehicle is set to be,
Figure 168390DEST_PATH_IMAGE020
is shown as
Figure 169844DEST_PATH_IMAGE021
The decision variables of the cycle are varied in such a way that,
Figure 368744DEST_PATH_IMAGE022
indicating the value of the access reward,
Figure 506465DEST_PATH_IMAGE023
the status of the service is indicated,
Figure 499828DEST_PATH_IMAGE024
representing virtual nodes
Figure 988579DEST_PATH_IMAGE025
The corresponding inventory gap space-time risk,
Figure 460011DEST_PATH_IMAGE026
indicating that the preset period interval is set to be,
Figure 717817DEST_PATH_IMAGE027
representing virtual nodes
Figure 147662DEST_PATH_IMAGE028
Corresponding material point
Figure 858129DEST_PATH_IMAGE029
At said predetermined periodic intervals
Figure 883985DEST_PATH_IMAGE026
The integral of the inner one of the two,
Figure 261876DEST_PATH_IMAGE030
for material point
Figure 331463DEST_PATH_IMAGE029
The size of the (c) is (d),
Figure 794806DEST_PATH_IMAGE031
for material point
Figure 873620DEST_PATH_IMAGE029
The risk factor of inventory gaps of (a),
Figure 106018DEST_PATH_IMAGE032
for material point
Figure 346507DEST_PATH_IMAGE029
The number of the capacity is divided into parts,
Figure 297145DEST_PATH_IMAGE033
first finger
Figure 914072DEST_PATH_IMAGE021
The time of execution of the periodic scheduling scheme.
Figure 250244DEST_PATH_IMAGE012
Is a sufficiently large positive number when
Figure DEST_PATH_IMAGE103
Value of the prize
Figure 927213DEST_PATH_IMAGE104
At this time, the risk caused by inventory gaps in the next period interval can be reduced by supplementing materials to the corresponding virtual nodes, the risk reduction is converted into the reward for the objective function, and the target value can be obviously reduced by accessing the virtual nodes. When in use
Figure DEST_PATH_IMAGE105
Value of the prize
Figure 568410DEST_PATH_IMAGE106
Accessing the corresponding virtual node must result in a significant increase in the target value. When in use
Figure DEST_PATH_IMAGE107
Value of the prize
Figure 457868DEST_PATH_IMAGE108
In this case, whether the access is carried out is determined by balancing the logistics cost of accessing the node and the size of the risk gap of the virtual node.
The constraints of the multi-cycle vehicle path model include:
Figure 664859DEST_PATH_IMAGE044
which means that each asset point is serviced by one vehicle once or 0 times.
Figure 997882DEST_PATH_IMAGE045
It means that the arc in of each asset point equals the number of arc outs.
Figure DEST_PATH_IMAGE109
，
Figure 391954DEST_PATH_IMAGE110
Which means that the vehicle must depart from and return to the distribution center.
Figure 350683DEST_PATH_IMAGE048
Which represents the capacity constraint, is,
Figure 412180DEST_PATH_IMAGE066
indicating vehicles
Figure 696531DEST_PATH_IMAGE052
Whether or not to pass through arc
Figure 843479DEST_PATH_IMAGE067
If passing through, then
Figure DEST_PATH_IMAGE111
If not, it is
Figure 74740DEST_PATH_IMAGE112
。
Figure 240011DEST_PATH_IMAGE049
It means that the sub-loop removes the constraint.
Figure 429684DEST_PATH_IMAGE050
Which represents an integer variable constraint.
Wherein the decision variables
Figure 329506DEST_PATH_IMAGE051
Representative vehicle
Figure 630038DEST_PATH_IMAGE052
Whether or not to pass through arc
Figure 134968DEST_PATH_IMAGE054
And 0 represents a number of the distribution center,
Figure 761122DEST_PATH_IMAGE055
is a set of nodes to be served,
Figure 882662DEST_PATH_IMAGE056
is a collection of a fleet of vehicles,
Figure 721305DEST_PATH_IMAGE057
which represents the number of the vehicle,
Figure 611900DEST_PATH_IMAGE058
is a node to be served by a node,
Figure 894108DEST_PATH_IMAGE059
represents
Figure 237365DEST_PATH_IMAGE058
The amount of demand for the point(s),
Figure 410857DEST_PATH_IMAGE060
is a collection of network arcs and is,
Figure 155959DEST_PATH_IMAGE061
represents a segment of an arc in the network,
Figure 858336DEST_PATH_IMAGE062
represents
Figure 688889DEST_PATH_IMAGE058
The arc-out of the point is realized,
Figure 869334DEST_PATH_IMAGE063
represents
Figure 734522DEST_PATH_IMAGE058
The arc-in of the point is formed,
Figure 607800DEST_PATH_IMAGE064
set of representative material points
Figure 191228DEST_PATH_IMAGE065
All of the arcs.
Further, in the optimization target of the multi-cycle vehicle path model, inventory gap risks are considered, the type of materials researched by the invention is periodically required materials consumed regularly, one characteristic of the materials is that the materials are lost along with time and the speed is stable, and the other characteristic is that the inventory is not lower than a certain level at any time to guarantee emergency disaster relief requirements. Fig. 1 shows the reserve amount of the material points in the first 3 periods as a function of time, in which the horizontal axis of fig. 1 is a time step, the vertical axis represents the reserve amount of the material points, and the gray part represents the current remaining reserve amount of the material. It can be observed that the material accumulation gap of each period is composed of two parts, one part is the accumulation gap of the period initial inventory gap represented by the white rectangular area in the period interval, and the other part is the accumulation gap of the additional inventory gap in the period interval caused by the material loss represented by the horizontal line triangular area. The risk value caused by the gap is positively correlated with the cumulative gap size. At the time of 2T, if the material scheduling and supplementing are not carried out on the material point, the material residual reserve is expected to not meet the guarantee requirement in the period 2. The thick dotted line represents the process that the material storage reaches the maximum capacity of the material point after the material scheduling and supplementing are carried out on the material point. The optimization goal of the multi-cycle materials scheduling model is to minimize the sum of the emergency inventory breach risk and the logistics cost of vehicle delivery for all cycles.
Since the risk value of the material points is influenced by the stock gap proportion and the gap duration, the risk value is determined for any period
Figure 424633DEST_PATH_IMAGE081
To the material point
Figure 144327DEST_PATH_IMAGE029
At periodic intervals
Figure 188506DEST_PATH_IMAGE026
Inner integral (trapezoidal area) as spatio-temporal risk value:
Figure DEST_PATH_IMAGE113
when considering the scale of the material points and the risk coefficient, the material points
Figure 993651DEST_PATH_IMAGE029
In the first place
Figure 781479DEST_PATH_IMAGE021
The risk values due to gaps in the cycle are:
Figure 355679DEST_PATH_IMAGE114
，
wherein the content of the first and second substances,
Figure 570760DEST_PATH_IMAGE030
for material point
Figure 597622DEST_PATH_IMAGE029
The size (area/population) parameter of (c),
Figure 939873DEST_PATH_IMAGE031
for material point
Figure 368580DEST_PATH_IMAGE029
For representing an estimate of risk of a breach.
Because the optimization target of the invention uses virtual node calculation, the material points are calculated
Figure 20141DEST_PATH_IMAGE029
In the first place
Figure 799878DEST_PATH_IMAGE021
The risk value caused by the gap in the period is converted into the virtual node at the first
Figure 929508DEST_PATH_IMAGE021
Risk values caused by gaps within a cycle, in particular, virtual nodes
Figure 212722DEST_PATH_IMAGE028
When served by the material scheduling scheme, the material point
Figure 35185DEST_PATH_IMAGE029
Reduction of stock gap ratio
Figure DEST_PATH_IMAGE115
Then virtual node
Figure 36639DEST_PATH_IMAGE028
Corresponding material point
Figure 704381DEST_PATH_IMAGE029
At periodic intervals
Figure 356948DEST_PATH_IMAGE026
The integral in (d) is:
Figure 350311DEST_PATH_IMAGE116
，
virtual node
Figure 573482DEST_PATH_IMAGE028
The corresponding inventory gap spatiotemporal risk is:
Figure DEST_PATH_IMAGE117
。
and step 3: after the execution is finished
Figure 44915DEST_PATH_IMAGE021
And after the material scheduling scheme of each scheduling period, updating the reserve quantity of the material points, and updating the service state of the virtual nodes according to the material scheduling scheme.
After the execution is finished
Figure 568300DEST_PATH_IMAGE021
After the material scheduling scheme of each scheduling period, the material reserve of all material points or some material points is supplemented, the material point reserve is updated, and the latest reserve data is obtained.
The invention assumes that at the beginning of each cycle
Figure 466986DEST_PATH_IMAGE033
Executing the material scheduling scheme at any time to complete material scheduling, so that the first time is finished
Figure 443032DEST_PATH_IMAGE021
The reserve quantity of the material points updated after the material scheduling scheme of each scheduling period is
Figure 718156DEST_PATH_IMAGE021
The amount of material reserve at the beginning of each dispatch cycle.
As shown in fig. 2, at the beginning of each cycle
Figure 581201DEST_PATH_IMAGE033
And executing the material scheduling scheme at any moment, and after the material scheduling scheme is executed, updating the virtual point service state at the beginning of the period, namely updating the virtual node service state according to the material scheduling scheme. The updating the service state of the virtual node according to the material scheduling scheme comprises:
all accessed virtual nodes in the emergency plan of the current scheduling period
Figure 181946DEST_PATH_IMAGE118
Service state of (2) is set to
Figure DEST_PATH_IMAGE119
Wherein, in the step (A),
Figure 114130DEST_PATH_IMAGE119
representing the service, setting the service state of a preset number of nodes in the virtual nodes which are not served in the emergency plan of the current scheduling period
Figure 192945DEST_PATH_IMAGE120
，
Figure 425343DEST_PATH_IMAGE120
Indicating that service is necessary.
The preset number is as follows:
Figure 931411DEST_PATH_IMAGE036
，
wherein the content of the first and second substances,
Figure 616470DEST_PATH_IMAGE037
is as follows
Figure 498975DEST_PATH_IMAGE021
Periodic material point
Figure 585880DEST_PATH_IMAGE029
The minimum amount of the delivery of the liquid,
Figure 246537DEST_PATH_IMAGE038
for material point
Figure 418893DEST_PATH_IMAGE029
The total capacity of (c).
In order to prevent the remaining material at the end of the cycle from falling below the guaranteed capacity at the material point,
Figure 839510DEST_PATH_IMAGE037
the following constraints need to be satisfied:
Figure DEST_PATH_IMAGE121
wherein the content of the first and second substances,
Figure 780921DEST_PATH_IMAGE077
for material point
Figure 628791DEST_PATH_IMAGE029
The minimum guaranteed amount of reserve of the tank,
Figure 288443DEST_PATH_IMAGE122
the speed of the consumption of the material is shown,
Figure DEST_PATH_IMAGE123
reserve the amount of materials before dispatching.
Demand splitting of material points into
Figure 981592DEST_PATH_IMAGE124
Virtual node for not served
Figure DEST_PATH_IMAGE125
A virtual node provided therein
Figure 782103DEST_PATH_IMAGE126
Service state of individual node
Figure DEST_PATH_IMAGE127
Wherein, in the step (A),
Figure 535295DEST_PATH_IMAGE126
is shown as
Figure 416663DEST_PATH_IMAGE021
Periodic material point
Figure 179083DEST_PATH_IMAGE029
Minimum delivery volume of
Figure 360666DEST_PATH_IMAGE037
Divided by the capacity of a single virtual node
Figure 550339DEST_PATH_IMAGE128
And then rounding up to obtain the number of virtual nodes which need to be served.
And 4, step 4: and calculating material gaps of each material point at the end of the current scheduling period according to the preset period interval and the material consumption speed, updating the service state of the virtual node according to the preset period interval and the material consumption speed, and updating the target function of the multi-period vehicle path model.
In obtaining the first
Figure 184582DEST_PATH_IMAGE021
After the material reserve amount at the beginning of each scheduling period, the period interval length (namely the preset period interval) is used
Figure 485114DEST_PATH_IMAGE026
) And the material consumption rate calculates the current scheduling period (i.e. the first
Figure 504891DEST_PATH_IMAGE021
Each scheduling period) ends based on the material point's material loss at the beginning of the periodAnd (4) obtaining the residual materials at the end of the period and further obtaining a material gap by the material quantity and the material loss in the whole period. For any period
Figure 131045DEST_PATH_IMAGE081
Material point
Figure 987005DEST_PATH_IMAGE029
At periodic intervals
Figure 91227DEST_PATH_IMAGE026
Internal uniform velocity loss
Figure DEST_PATH_IMAGE129
And the residual materials at the end of the period are as follows:
Figure 716244DEST_PATH_IMAGE130
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE131
for material point
Figure 247719DEST_PATH_IMAGE029
The amount of material lost per unit time,
Figure 590976DEST_PATH_IMAGE132
first finger
Figure DEST_PATH_IMAGE133
And (4) reserving the materials before scheduling in each scheduling period.
The material gap of each material point when the current scheduling period is finished can be calculated by a formula:
Figure 718463DEST_PATH_IMAGE134
either the first or the second substrate is, alternatively,
Figure DEST_PATH_IMAGE135
。
as shown in fig. 2, when a cycle is finished, calculating the consumption of periodic materials, and updating the service state of the virtual node according to the preset cycle interval and the material consumption speed, where updating the service state of the virtual node according to the preset cycle interval and the material consumption speed includes:
calculating the number of virtual nodes to be consumed according to the material consumption speed in the preset period interval, and setting the service state of the part of virtual nodes as
Figure 463565DEST_PATH_IMAGE136
。
Calculating the number of virtual nodes to be consumed according to the material consumption speed in the preset period interval, and setting the service state of the part of virtual nodes as
Figure 165942DEST_PATH_IMAGE136
Wherein, in the step (A),
Figure 996495DEST_PATH_IMAGE136
indicating an unserviceable condition. The calculation formula for calculating the number of the virtual nodes of the loss according to the material consumption speed is as follows:
Figure DEST_PATH_IMAGE137
i.e. to point the materials
Figure 176940DEST_PATH_IMAGE029
Amount of material lost per unit time
Figure 42128DEST_PATH_IMAGE131
At a predetermined periodic interval
Figure 915406DEST_PATH_IMAGE026
Divided by the capacity of a single virtual node
Figure 216943DEST_PATH_IMAGE138
And rounding up to obtain the number of the lost virtual nodes. Resetting the service state of the partially lost virtual node, i.e. setting the service state of the partially lost virtual node to
Figure 732238DEST_PATH_IMAGE136
。
And 5: order to
Figure 451933DEST_PATH_IMAGE005
If, if
Figure 496112DEST_PATH_IMAGE006
Then go back to execute the step 2, if
Figure 35678DEST_PATH_IMAGE007
If so, the dispatching is finished and a complete emergency plan can be output.
The invention couples a vehicle path problem model and a periodic material loss model: the method comprises the steps of considering accumulated space-time risks caused by inventory gap proportion and period intervals, converting the accumulated space-time risks into a reward collection problem, considering vehicle capacity constraints in demand splitting and actual logistics transportation in a scheduling scheme, and further constructing a multi-period vehicle path problem model considering reward collection, division delivery and capacity constraints under different risk estimation so as to provide a multi-target multi-period material scheduling vehicle path combination scheme for minimizing gap risks and logistics costs. Specifically, the method not only focuses on material sources and destinations, but also generates vehicle use, station access sequence and the like on the logistics scheme execution level, so that the optimization result has more actual reference value, and the utilization efficiency of transportation resources is improved; the invention divides the material points into the virtual node sets according to the capacity so as to split the requirements of the material points, thereby improving the full load rate of vehicles and the utilization efficiency of transportation resources; the invention considers the periodic initial gap risk in the multi-period vehicle path model by utilizing the reward collection mechanism, in the reward collection model, the vehicle does not need to visit all demand points any more, but encourages the vehicle to visit the distribution points with higher priority preferentially through a certain reward mechanism, and under the condition that the transportation resources are limited in the actual emergency material scheduling scene, the material scheduling scheme can preferentially ensure the material supply of the region with higher potential risk so as to reduce the global loss.
Optionally, a gap material risk coefficient is preset
Figure 823505DEST_PATH_IMAGE100
And periodic interval
Figure 397706DEST_PATH_IMAGE026
The value ranges of (1) are respectively combined by different parameters (gap material risk coefficient)
Figure 878366DEST_PATH_IMAGE100
And periodic interval
Figure 170807DEST_PATH_IMAGE026
The combination of the vehicle route schemes and the corresponding transportation cost and risk cost under different parameter combinations are obtained by executing the steps 1-5, the transportation cost and the risk cost of all the emergency material scheduling schemes are normalized and then summed by equal weight (or multi-target weight is defined according to requirements) to obtain dimensionless comprehensive cost, the multi-cycle vehicle route scheme with the lowest comprehensive cost and the corresponding optimal parameter combination are determined, and the multi-cycle vehicle route scheme with the lowest comprehensive cost is used as the optimal scheme for the multi-cycle emergency material scheduling. Referring to fig. 16, a multidimensional space diagram of the aggregate costs at different estimated risk factors and preset period intervals is shown, based on which the parameter combination with the lowest aggregate cost can be determined. FIG. 17 shows the variation of the logistics cost with the estimated risk factor under the premise of a certain preset period interval, as can be seen from FIG. 17, when the period interval is large, the estimated risk factor
Figure 496746DEST_PATH_IMAGE100
The influence on the logistics cost is not obvious, and when the period interval is small, an excessively high risk coefficient can bring higher logistics cost, such as
Figure 941765DEST_PATH_IMAGE139
In time, the logistics cost is not basically influenced by the estimated risk coefficient,
Figure DEST_PATH_IMAGE140
in time, the logistics cost is greatly influenced by the estimated risk coefficient, and the estimated risk coefficient is too high
Figure 62168DEST_PATH_IMAGE100
Which can result in excessive logistical costs. FIG. 18 shows the variation of the logistics cost with the predetermined period interval under the premise that the estimated risk factor is constant, as can be seen from FIG. 18, the estimated risk factor
Figure 576326DEST_PATH_IMAGE100
When the value is larger, the larger period interval brings lower logistics cost, and the estimated risk coefficient
Figure 705956DEST_PATH_IMAGE100
When smaller, the effect of the periodic intervals on logistics costs is insignificant, e.g. when
Figure 254749DEST_PATH_IMAGE141
The logistics cost is very little affected by the preset period interval. FIG. 19 is a diagram showing the variation of the gap risk of the material with the estimated risk factor under the premise of a certain preset period interval, as shown in FIG. 19, when the period interval is low, the estimated risk factor
Figure 811632DEST_PATH_IMAGE100
Has great influence on the risk of the material gap and estimates the risk coefficient
Figure 813086DEST_PATH_IMAGE100
The elevation can reduce the risk of material gaps (e.g.
Figure DEST_PATH_IMAGE142
Time, estimated risk factor
Figure 995675DEST_PATH_IMAGE100
The risk of material gap is greatly reduced due to rising), and when the period interval is larger, the estimated risk coefficient
Figure 398974DEST_PATH_IMAGE100
The risk of material gaps is not significantly affected, for example, when
Figure 126759DEST_PATH_IMAGE143
When is at
Figure 615509DEST_PATH_IMAGE100
When the risk of the gap of the material is less than a certain value, the influence of the estimated risk coefficient is great
Figure 352521DEST_PATH_IMAGE100
When the risk of the material gap is larger than a certain value, the risk of the material gap is not influenced by the estimated risk coefficient any more; when in use
Figure DEST_PATH_IMAGE144
And meanwhile, the risk of the material gap is not influenced by the estimated risk coefficient. Fig. 20 shows the variation of the gap risk of the material with the predetermined period interval under the premise that the estimated risk factor is constant, the lower the estimated risk factor is, the stronger the monotonicity of the gap risk of the material with respect to the period interval is, the lower the estimated risk factor is, the larger the corresponding minimum gap risk of the material is, for example,
Figure 344747DEST_PATH_IMAGE145
and
Figure DEST_PATH_IMAGE146
compared with the prior art, the gap risk of the materials is more monotonous relative to the periodic interval, and the corresponding minimum gap risk of the materials is less.
The invention provides a combined optimization solving mechanism of multi-period multi-risk estimation coefficients, all optimal schemes in a designated value range interval are solved through parallel computing, and the scheme with the lowest comprehensive cost is selected for reference.
To facilitate understanding, a practical case is given, and fig. 3 to 11 show the risk coefficients in sequence
Figure 243433DEST_PATH_IMAGE145
Periodic interval of
Figure 970212DEST_PATH_IMAGE147
Under the condition of the reaction, the reaction kettle is used for heating,
Figure DEST_PATH_IMAGE148
the total of 9 periods of the material dispatching vehicle path scheme. The numerical value pairs of the nodes in the graph respectively represent the material point number and the residual material proportion of the material point after the current logistics scheme is executed, and different line types represent different vehicle paths. It can be observed that in this case, the material scheduling is performed only in the periods 0, 1 and 8, while the material scheduling is not performed in the periods 2 to 7, and the remaining material proportion of each material point is continuously decreased. Since the remaining material reserve at the material points will be consumed with the cycle iteration, as shown in fig. 10, the 8 th cycle estimates that the material remaining at the material point 1 (14%) is not enough to be supported to the end of the cycle, and the 9 th cycle immediately supplements each material point (as shown in fig. 11).
Step of time
Figure 979756DEST_PATH_IMAGE149
As Z axis, for complete emergency plans
Figure DEST_PATH_IMAGE150
The material scheduling scheme is visualized, and the effect is shown in fig. 12. It can be observed that after a certain number of iterations, a period is formed every 7 unit time steps, which indicates that the multi-period emergency plan generated by the invention forms a stable scheduling mode under the current parameter setting.
The logistics cost and the inventory gap risk of each period of the scheme are counted to obtain figures 13 to 15, wherein the figure 13 shows the estimated risk coefficient
Figure 826490DEST_PATH_IMAGE145
Preset periodic interval
Figure 427235DEST_PATH_IMAGE147
Logistics cost and inventory gap risk, fig. 14 shows the estimated risk factors
Figure 359419DEST_PATH_IMAGE151
Preset periodic interval
Figure 703813DEST_PATH_IMAGE147
Logistics cost and inventory gap risk, fig. 15 shows the estimated risk factor
Figure DEST_PATH_IMAGE152
Preset periodic interval
Figure 654320DEST_PATH_IMAGE147
Logistics costs and inventory gap risks. The white bar represents logistics cost at different time steps and the grey represents inventory gap risk at different time steps.
In one embodiment, the device for multi-cycle split distribution of emergency materials based on inventory gap risk comprises a computer readable storage medium storing a computer program and a processor, wherein the computer program is read by the processor and runs to implement the method for multi-cycle split distribution of emergency materials based on inventory gap risk. Compared with the prior art, the beneficial effects of the emergency material multi-period segmentation and distribution device based on the inventory gap risk are consistent with those of the emergency material multi-period segmentation and distribution method based on the inventory gap risk, and the description is omitted here.
In one embodiment, the present invention provides a computer-readable storage medium, which stores a computer program, and when the computer program is read and executed by a processor, the method for multi-cycle segmentation and distribution of emergency materials based on risk of inventory gaps as described above is implemented.
Compared with the prior art, the beneficial effects of the computer-readable storage medium of the invention are consistent with the above-mentioned emergency material multi-period segmentation distribution method based on the risk of the inventory gap, and are not repeated herein.
Although the present disclosure has been described above, the scope of the present disclosure is not limited thereto. Various changes and modifications may be effected therein by one of ordinary skill in the pertinent art without departing from the spirit and scope of the present disclosure, and these changes and modifications are intended to be within the scope of the present disclosure.

Claims (10)

1. An emergency material multi-cycle segmentation distribution method based on inventory gap risks is characterized by comprising the following steps:
step 1: dividing the total time length of the emergency plan into a plurality of dispatching cycles at preset cycle intervals, using i to represent the serial numbers of the dispatching cycles,
Figure 159078DEST_PATH_IMAGE001
(ii) a For any material point, dividing the material point into material points according to capacity
Figure 463283DEST_PATH_IMAGE002
A set of virtual nodes that are equal in size,
Figure 756861DEST_PATH_IMAGE003
；
step 2: solving the multi-cycle vehicle path model to obtain the first
Figure 204023DEST_PATH_IMAGE004
A material scheduling scheme for each scheduling cycle is performed
Figure 924854DEST_PATH_IMAGE004
A material scheduling scheme for each scheduling period;
and step 3: after the execution is finished
Figure 559098DEST_PATH_IMAGE004
After the material scheduling scheme of each scheduling period, updating the reserve quantity of material points, and updating the service state of the virtual nodes according to the material scheduling scheme;
and 4, step 4: calculating material gaps of each material point at the end of the current scheduling period according to the preset period interval and the material consumption speed, updating a virtual node service state according to the preset period interval and the material consumption speed, and updating a target function of the multi-period vehicle path model;
and 5: order to
Figure 266154DEST_PATH_IMAGE005
If, if
Figure 98981DEST_PATH_IMAGE006
Then go back to execute the step 2, if
Figure 725134DEST_PATH_IMAGE007
Then the scheduling is finished.
2. The stock gap risk based emergency material multi-cycle segmentation distribution method of claim 1, wherein the objective function of the multi-cycle vehicle path model is as follows:
Figure 112253DEST_PATH_IMAGE008
Figure 872267DEST_PATH_IMAGE009
Figure 294022DEST_PATH_IMAGE010
Figure 356655DEST_PATH_IMAGE011
wherein the content of the first and second substances,
Figure 231071DEST_PATH_IMAGE012
for a positive number greater than the preset value,
Figure 279929DEST_PATH_IMAGE013
a fleet set is represented as a set of vehicles,
Figure 556190DEST_PATH_IMAGE014
represents a vehicle number, 0 represents a distribution center,
Figure 789725DEST_PATH_IMAGE015
a set of virtual nodes is represented that is,
Figure 151436DEST_PATH_IMAGE016
、
Figure 754718DEST_PATH_IMAGE017
each of which represents a virtual node,
Figure 885485DEST_PATH_IMAGE018
representative arc
Figure 289922DEST_PATH_IMAGE019
The driving distance of the vehicle is set to be,
Figure 873350DEST_PATH_IMAGE020
is shown as
Figure 654224DEST_PATH_IMAGE021
The decision variables of the cycle are varied in such a way that,
Figure 514864DEST_PATH_IMAGE022
indicating the value of the access reward,
Figure 90201DEST_PATH_IMAGE023
the status of the service is indicated,
Figure 426505DEST_PATH_IMAGE024
representing virtual nodes
Figure 745491DEST_PATH_IMAGE025
The corresponding inventory gap space-time risk,
Figure 975484DEST_PATH_IMAGE026
indicating that the preset period interval is set to be,
Figure 456144DEST_PATH_IMAGE027
representing virtual nodes
Figure 810902DEST_PATH_IMAGE028
Corresponding material point
Figure 543365DEST_PATH_IMAGE029
At said predetermined periodic intervals
Figure 503231DEST_PATH_IMAGE026
The integral of the inner one of the two,
Figure 685951DEST_PATH_IMAGE030
for material point
Figure 731267DEST_PATH_IMAGE029
The size of the (c) is (d),
Figure 18154DEST_PATH_IMAGE031
for material point
Figure 832527DEST_PATH_IMAGE029
The risk factor of inventory gaps of (a),
Figure 920568DEST_PATH_IMAGE032
for material point
Figure 453181DEST_PATH_IMAGE029
The number of the capacity is divided into parts,
Figure 917660DEST_PATH_IMAGE033
first finger
Figure 727484DEST_PATH_IMAGE021
Execution of a periodic scheduling schemeThe line time.
3. The stock gap risk based emergency material multi-cycle segmentation distribution method according to claim 2, wherein the updating of the virtual node service state according to the material scheduling scheme comprises:
setting the service states of all accessed virtual nodes in the emergency plan of the current scheduling period as
Figure 986427DEST_PATH_IMAGE034
Setting the service state of the nodes with preset number for the virtual nodes which are not served in the emergency plan of the current scheduling period
Figure 6336DEST_PATH_IMAGE035
。
4. The stock gap risk based emergency material multi-cycle segmentation distribution method as claimed in claim 3, wherein the preset number is:
Figure 8927DEST_PATH_IMAGE036
wherein the content of the first and second substances,
Figure 922525DEST_PATH_IMAGE037
is as follows
Figure 617949DEST_PATH_IMAGE021
Periodic material point
Figure 859574DEST_PATH_IMAGE029
The minimum amount of the delivery of the liquid,
Figure 400277DEST_PATH_IMAGE038
for material point
Figure 309327DEST_PATH_IMAGE029
The total capacity of (c).
5. The multi-cycle split distribution method for emergency materials based on inventory gap risk as claimed in claim 4,
Figure 785439DEST_PATH_IMAGE039
wherein the content of the first and second substances,
Figure 779940DEST_PATH_IMAGE040
for material point
Figure 124334DEST_PATH_IMAGE029
The minimum guaranteed amount of reserve of the tank,
Figure 887890DEST_PATH_IMAGE041
the speed of the consumption of the material is shown,
Figure 562934DEST_PATH_IMAGE042
reserve the amount of materials before dispatching.
6. The stock gap risk based emergency material multi-cycle segmentation distribution method as claimed in any one of claims 2 to 5, wherein the updating of the virtual node service state according to the preset cycle interval and the material consumption speed comprises:
calculating the number of virtual nodes to be consumed according to the material consumption speed in the preset period interval, and setting the service state of the part of virtual nodes as
Figure 779151DEST_PATH_IMAGE043
。
7. The stock gap risk based emergency material multi-cycle segmentation distribution method as claimed in any one of claims 2 to 5, wherein the constraint conditions of the multi-cycle vehicle path model are as follows:
Figure 927236DEST_PATH_IMAGE044
Figure 545299DEST_PATH_IMAGE045
Figure 628793DEST_PATH_IMAGE046
Figure 332307DEST_PATH_IMAGE047
Figure 752924DEST_PATH_IMAGE048
Figure 491073DEST_PATH_IMAGE049
Figure 870101DEST_PATH_IMAGE050
wherein the decision variables
Figure 185545DEST_PATH_IMAGE051
Representative vehicle
Figure 675432DEST_PATH_IMAGE052
Whether or not to pass through arc
Figure 2508DEST_PATH_IMAGE054
0 represents in distributionThe heart is provided with a plurality of heart-shaped grooves,
Figure 552438DEST_PATH_IMAGE055
is a set of nodes to be served,
Figure 105911DEST_PATH_IMAGE056
is a collection of a fleet of vehicles,
Figure 399489DEST_PATH_IMAGE057
which represents the number of the vehicle,
Figure 846651DEST_PATH_IMAGE058
is a node to be served by a node,
Figure 36323DEST_PATH_IMAGE059
represents
Figure 201726DEST_PATH_IMAGE058
The amount of demand for the point(s),
Figure 659514DEST_PATH_IMAGE060
is a collection of network arcs and is,
Figure 226762DEST_PATH_IMAGE061
represents a segment of an arc in the network,
Figure 852915DEST_PATH_IMAGE062
represents
Figure 505613DEST_PATH_IMAGE058
The arc-out of the point is realized,
Figure 16360DEST_PATH_IMAGE063
represents
Figure 906956DEST_PATH_IMAGE058
The arc-in of the point is formed,
Figure 969590DEST_PATH_IMAGE064
set of representative material points
Figure 844005DEST_PATH_IMAGE065
All of the arc-out points of (c),
Figure 283076DEST_PATH_IMAGE066
indicating vehicles
Figure 683971DEST_PATH_IMAGE052
Whether or not to pass through arc
Figure 917506DEST_PATH_IMAGE067
。
8. The stock gap risk based multi-cycle split distribution method for emergency materials according to claim 1, further comprising:
presetting a value range of a stock gap risk coefficient and the preset period interval;
respectively executing the steps 1-5 according to parameter combinations composed of different inventory gap risk coefficients and preset period intervals to obtain material scheduling schemes under different parameter combinations and corresponding transportation cost and risk cost;
and normalizing the transportation cost and the risk cost of all the material scheduling schemes, calculating the weighted sum to obtain dimensionless comprehensive cost, determining the material scheduling scheme with the lowest comprehensive cost and the corresponding optimal parameter combination thereof, and taking the material scheduling scheme with the lowest comprehensive cost as the optimal scheme for multi-period emergency material scheduling.
9. An emergency material multi-cycle segmentation distribution device based on inventory gap risk, which comprises a computer readable storage medium storing a computer program and a processor, wherein the computer program is read and executed by the processor to realize the emergency material multi-cycle segmentation distribution method based on inventory gap risk according to any one of claims 1 to 8.
10. A computer-readable storage medium, wherein a computer program is stored, and when the computer program is read and executed by a processor, the method for multi-cycle split distribution of emergency materials based on risk of inventory gaps according to any one of claims 1 to 8 is implemented.
CN202110867289.7A 2021-07-30 2021-07-30 Emergency material multi-cycle segmentation distribution method and device based on inventory gap risk Active CN113313331B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110867289.7A CN113313331B (en) 2021-07-30 2021-07-30 Emergency material multi-cycle segmentation distribution method and device based on inventory gap risk

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110867289.7A CN113313331B (en) 2021-07-30 2021-07-30 Emergency material multi-cycle segmentation distribution method and device based on inventory gap risk

Publications (2)

Publication Number Publication Date
CN113313331A true CN113313331A (en) 2021-08-27
CN113313331B CN113313331B (en) 2022-01-07

Family

ID=77382492

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110867289.7A Active CN113313331B (en) 2021-07-30 2021-07-30 Emergency material multi-cycle segmentation distribution method and device based on inventory gap risk

Country Status (1)

Country Link
CN (1) CN113313331B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114005519A (en) * 2021-09-23 2022-02-01 平安国际智慧城市科技股份有限公司 Material scheduling method, device and equipment and readable storage medium

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102542395A (en) * 2010-12-31 2012-07-04 北京师范大学 Emergency material dispatching system and calculating method
CN107808213A (en) * 2017-10-19 2018-03-16 中国人民解放军国防科技大学 Dynamically-adjusted emergency transportation scheduling plan generation method
CN111199309A (en) * 2019-12-19 2020-05-26 深圳供电局有限公司 Early warning management and control system of electric power material supply chain operation
CN111242454A (en) * 2020-01-07 2020-06-05 杭州电子科技大学 Chemical accident multi-target two-stage emergency rescue material scheduling method
CN111366160A (en) * 2020-05-25 2020-07-03 深圳市城市交通规划设计研究中心股份有限公司 Path planning method, path planning device and terminal equipment
CN111445093A (en) * 2020-04-23 2020-07-24 长春工程学院 Power transmission line emergency repair path optimization system and method
CN111932029A (en) * 2020-09-09 2020-11-13 耀方信息技术(上海)有限公司 Warehouse stock allocation system and method based on follow-up prediction
CN112193116A (en) * 2020-09-30 2021-01-08 山西大学 Electric vehicle charging optimization guiding strategy considering reward mechanism
CN113191619A (en) * 2021-04-25 2021-07-30 大连海事大学 Emergency rescue material distribution and vehicle dispatching dynamic optimization method

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102542395A (en) * 2010-12-31 2012-07-04 北京师范大学 Emergency material dispatching system and calculating method
CN107808213A (en) * 2017-10-19 2018-03-16 中国人民解放军国防科技大学 Dynamically-adjusted emergency transportation scheduling plan generation method
CN111199309A (en) * 2019-12-19 2020-05-26 深圳供电局有限公司 Early warning management and control system of electric power material supply chain operation
CN111242454A (en) * 2020-01-07 2020-06-05 杭州电子科技大学 Chemical accident multi-target two-stage emergency rescue material scheduling method
CN111445093A (en) * 2020-04-23 2020-07-24 长春工程学院 Power transmission line emergency repair path optimization system and method
CN111366160A (en) * 2020-05-25 2020-07-03 深圳市城市交通规划设计研究中心股份有限公司 Path planning method, path planning device and terminal equipment
CN111932029A (en) * 2020-09-09 2020-11-13 耀方信息技术(上海)有限公司 Warehouse stock allocation system and method based on follow-up prediction
CN112193116A (en) * 2020-09-30 2021-01-08 山西大学 Electric vehicle charging optimization guiding strategy considering reward mechanism
CN113191619A (en) * 2021-04-25 2021-07-30 大连海事大学 Emergency rescue material distribution and vehicle dispatching dynamic optimization method

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
刘维怡 等: "面向出行服务的车辆路径问题求解器应用研究", 《第十五届中国智能交通年会科技论文集》 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114005519A (en) * 2021-09-23 2022-02-01 平安国际智慧城市科技股份有限公司 Material scheduling method, device and equipment and readable storage medium

Also Published As

Publication number Publication date
CN113313331B (en) 2022-01-07

Similar Documents

Publication Publication Date Title
Campbell et al. The inventory routing problem
De Chardon et al. Bike-share rebalancing strategies, patterns, and purpose
US10200457B2 (en) Selective distribution of machine-learned models
Aghajani et al. Operational scheduling of electric vehicles parking lot integrated with renewable generation based on bilevel programming approach
Rigas et al. Algorithms for electric vehicle scheduling in large-scale mobility-on-demand schemes
Mei et al. Better lucky than rich? Comparative analysis of parking reservation and parking charge
CN109214756B (en) Vehicle logistics scheduling method and device, storage medium and terminal
CN109345091B (en) Ant colony algorithm-based whole vehicle logistics scheduling method and device, storage medium and terminal
CN113191619A (en) Emergency rescue material distribution and vehicle dispatching dynamic optimization method
CN111695842B (en) Distribution scheme determining method, distribution scheme determining device, electronic equipment and computer storage medium
CN113313331B (en) Emergency material multi-cycle segmentation distribution method and device based on inventory gap risk
Prescott-Gagnon et al. Heuristics for an oil delivery vehicle routing problem
Benjamin Metaheuristics for the waste collection vehicle routing problem with time windows
Zheng et al. Supply-demand-aware deep reinforcement learning for dynamic fleet management
Mellou et al. Dynamic resource redistribution and demand estimation: An application to bike sharing systems
JP5632412B2 (en) Electric power reduction request plan planning apparatus and method
CN111275229B (en) Resource model training method, resource gap prediction method, device and electronic equipment
Huo et al. A mixed-integer program (MIP) for one-way multiple-type shared electric vehicles allocation with uncertain demand
EP3983967A1 (en) System and method for vehicle relocation
Akturk et al. Managing resources for shared micromobility: Approximate optimality in large-scale systems
Ramalhinho Dias Lourenço et al. Inventory-routing model, for a multi-period problem with stochastic and deterministic demand
JP7243564B2 (en) Information processing device and information processing method
Bosman et al. Computationally intelligent online dynamic vehicle routing by explicit load prediction in an evolutionary algorithm
Alizadeh et al. On modeling and marketing the demand flexibility of deferrable loads at the wholesale level
CN114037110A (en) Vehicle scheduling method, device, equipment and computer program product

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant