CN112085271B - Crowdsourcing mode-based traditional industry cluster goods collection path optimization method - Google Patents

Crowdsourcing mode-based traditional industry cluster goods collection path optimization method Download PDF

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CN112085271B
CN112085271B CN202010933700.1A CN202010933700A CN112085271B CN 112085271 B CN112085271 B CN 112085271B CN 202010933700 A CN202010933700 A CN 202010933700A CN 112085271 B CN112085271 B CN 112085271B
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毛海军
王志豪
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Abstract

The invention discloses a traditional industry cluster cargo collection path optimization method based on a crowdsourcing mode. And matching the position of the available truck in the crowdsourcing mode with the position and the demand of a logistics demand party, performing cluster selection on the vehicle and the customer by applying cluster analysis and a greedy algorithm, and determining the vehicle and the route applied by the path optimization. And (3) calculating the solution of the previous step as an initial population by using a genetic algorithm, optimizing the path, and finally verifying and comparing the obtained different schemes to obtain an optimal path scheme. The method takes the genetic algorithm as the core, combines the basic concepts of the cluster analysis model and the greedy algorithm, and improves the operation efficiency, thereby utilizing the logistics resources of the traditional industrial cluster to the maximum extent, improving the logistics efficiency and reducing the logistics cost.

Description

Crowdsourcing mode-based traditional industry cluster goods collection path optimization method
Technical Field
The invention relates to a traditional industry cluster collection path optimization method based on a crowdsourcing mode, and belongs to the technical field of logistics path optimization.
Background
After being reformed and opened in China, a group of traditional industry clusters with large influence is formed, such as China (ever-maturing) shopping mall, Nantong rock-folded bridge home textile market and the like. These traditional industry clusters have become important fulcrums for leveraging domestic economic growth and modernized construction. However, the socialized logistics awareness of these spontaneously formed traditional industry clusters is relatively weak, the ratio of self-operated logistics is high, and the management of the logistics supply chain is delayed. Meanwhile, the logistics enterprises in the cluster are more in number, smaller in scale and respectively administrative, and the logistics lines and logistics resources in the traditional industrial cluster are disordered and wasted. These problems greatly limit the development steps of transformation and upgrade of traditional industry clusters, and the logistics system of the traditional industry clusters needs to be optimized and upgraded urgently.
The crowd-sourced mode logistics distribution refers to a logistics mode that distribution work which is completed by professional institutions is transferred to the public communities outside enterprises, generally, logistics demand parties issue logistics demands to a special public information platform, logistics practitioners with service intentions also input information to the information platform, the platform integrates resources, the logistics practitioners distribute orders in a unified manner and schedule vehicles, and finally the logistics practitioners and the platform settle accounts to obtain remuneration, as shown in fig. 2. The crowdsourcing mode can fully utilize idle logistics resources of the society and improve the logistics distribution efficiency.
At present, the research on capacity resource allocation, terminal distribution mode and scheduling optimization at home and abroad is sufficient, but the research on applying the crowdsourcing mode to the distribution aspect is less. The crowdsourcing idea can effectively integrate existing medium and small logistics enterprises of the traditional industry cluster, vehicle scheduling is optimized based on the crowdsourcing mode, the overall logistics efficiency of the cluster can be optimized, logistics cost is reduced, and the crowdsourcing concept has important guiding significance for terminal logistics integration and optimized scheduling of the traditional industry cluster.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the traditional industrial cluster goods collection path optimization method based on the crowdsourcing mode can reduce calculation steps in the path optimization process, improve the operation efficiency and obtain the optimal goods collection path more quickly. Therefore, logistics resources of the traditional industrial cluster are utilized more fully, logistics efficiency is improved, and logistics cost is reduced.
The invention adopts the following technical scheme for solving the technical problems:
a traditional industry cluster goods collection path optimization method based on a crowdsourcing mode comprises the following steps:
step 1, analyzing the cost in the traditional industrial cluster cargo collection process, and establishing a target function with the lowest total cost as a target;
the objective function is as follows:
Figure BDA0002671150350000021
wherein C represents the total cost; c1、C2、C3、C4Respectively representing the truck fixed cost, the truck running cost, the truck loading cost and the time window transportation punishment cost; cgRepresenting fixed use and maintenance of a single truckMaintenance cost, XrIndicating whether truck r is in use, XrIs a variable from 0 to 1, r is 1, …, m represents the number of available trucks; cdRepresenting the cost per unit distance travelled by a single truck when empty, driIndicating the distance of the truck r from the logistics consumer i,
Figure BDA0002671150350000022
indicating whether the logistics consumer i is the first customer serviced by truck r,
Figure BDA0002671150350000023
is a variable from 0 to 1, i is 1, …, n, n represents the number of logistics demand side; dijIndicating the distance from the logistics demand side i to the logistics demand side j,
Figure BDA0002671150350000024
indicating whether the truck r serves the logistics demand side i and then serves the logistics demand side j,
Figure BDA0002671150350000025
the variable is 0-1, j is 1, …, n +1, j is not equal to i, j is n +1, which indicates that the truck returns the freight to the cluster logistics center; cwRepresenting the cost per unit distance a single truck will transport per unit weight, WriThe total weight of goods on the truck after the truck r arrives at the logistics demand party i for taking goods is represented; ciPenalty cost, X, representing the logistics demander iiIndicating whether the logistics demand party i gets the service in the appointed time, XiIs a variable from 0 to 1;
step 2, setting constraint conditions under the condition of knowing the position of a logistics demander, the freight demand of the logistics demander, the position of a truck and the rated freight volume of the truck;
step 3, taking the positions of the trucks as a clustering center, performing initial clustering on logistics demanders by adopting a clustering analysis method, simultaneously applying greedy thought, determining the lowest truck number and the corresponding truck, and simultaneously determining the logistics demander corresponding to the service of each truck;
step 4, performing secondary clustering by adopting a clustering analysis method according to the lowest truck number and the corresponding truck determined in the step 3, and distributing logistics demand parties to the determined trucks;
step 5, determining the path of the truck by taking the shortest total distance as a target according to the clustering result of the step 4, taking the path of the truck as an initial population of a genetic algorithm, optimizing the path of the truck by adopting the genetic algorithm by taking the lowest total cost as a target based on the clustering result of the step 4, and adjusting the number of the trucks and logistics demand parties corresponding to services of each truck in the optimization process, so as to obtain the number of the trucks corresponding to the lowest total cost and the path of each truck, namely a scheme with the lowest total cost;
step 6, judging the lowest total cost scheme obtained in the step 5 by adopting the constraint conditions set in the step 2, wherein when the lowest total cost scheme obtained in the step 5 meets all the constraint conditions, the scheme is the optimal solution; otherwise, adjusting the logistics demand side of the corresponding service of each truck and the path of the truck until the optimal solution is obtained.
As a preferred scheme of the invention, the punishment cost C of the logistics demand party i in the step 1iExpressed as:
Figure BDA0002671150350000031
wherein, TiIndicates the time, gamma, required for the truck to arrive at the logistics party i1Penalty factor, gamma, representing the advance arrival of a truck to provide service2Penalty factor, LT, indicating the delayed arrival of a truck to provide servicei、ETiRespectively representing the upper limit and the lower limit, lT, of the ideal service time window of the logistics demand party ii、eTiRespectively representing the upper limit and the lower limit of the maximum service time window which can be accepted by the logistics demand party i.
As a preferred embodiment of the present invention, the constraint conditions in step 2 are as follows:
constraint 1: at least one truck is selected to serve all the logistics consumers, i.e.
Figure BDA0002671150350000032
Constraint 2: the final determined truck being able to perform all the collecting services, i.e.
Figure BDA0002671150350000033
QrIndicating the nominal cargo capacity, Q, of the truck riRepresenting the freight demand of the logistics demand party i;
constraint 3: the total weight W of goods on the truck after the truck r arrives at the logistics demand party i for taking goodsriEqual to the total weight of goods on the truck after the truck r takes goods from the previous logistics demand party plus the freight demand Q of the logistics demand party ii；
Constraint 4: the actual cargo-collecting capacity of the truck r not exceeding its nominal capacity, i.e.
Figure BDA0002671150350000041
Figure BDA0002671150350000042
Indicating whether the logistics customers i are serviced by the trucks r,
Figure BDA0002671150350000043
is a variable from 0 to 1;
constraint 5: the final determined total cargo collection amount of all trucks is equal to the total cargo demand amount of all logistics demand parties, namely
Figure BDA0002671150350000044
Constraint 6: determining the punishment cost C of the logistics demander i according to the service providing time of the truck and the upper limit and the lower limit of the maximum service time window which can be accepted by the logistics demander ii。
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
the method divides the path optimization problem into a plurality of sub-problems to be solved step by referring to the solving step of the LRP problem, takes the genetic algorithm as the core, combines the basic idea of a cluster analysis model and a greedy algorithm to obtain a high-quality initial population, shortens the evolution process, reduces the calculation steps in iteration, improves the operation efficiency and obtains the optimal goods collection path more quickly. Therefore, logistics resources of the traditional industrial cluster are utilized more fully, logistics efficiency is improved, and logistics cost is reduced.
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Fig. 1 is a flowchart of a conventional industry cluster cargo gathering path optimization method based on a crowdsourcing mode according to the present invention.
Fig. 2 is a flow diagram of a conventional industry cluster crowd-sourced logistics flow.
Fig. 3 is a flow chart of the conventional industry cluster cargo collection based on crowdsourcing mode according to the present invention.
FIG. 4 is a basic flow chart of the optimal cargo gathering path solving method based on the genetic algorithm as the core and combined with the cluster analysis and the greedy algorithm.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As shown in fig. 1, a flowchart of a conventional industry cluster cargo gathering path optimizing method based on a crowdsourcing mode according to the present invention is shown, and the method includes the following steps:
and S1, analyzing the cost in the conventional industry cluster cargo gathering process based on the crowdsourcing mode, and taking the lowest total cost as an objective function.
The cost in the traditional industry cluster cargo collection process based on the crowdsourcing mode specifically comprises the following steps: cost of truck fixing
Figure BDA0002671150350000051
Cost of freight car travel
Figure BDA0002671150350000052
Freight car loading cost
Figure BDA0002671150350000053
Time window constraint penalty cost
Figure BDA0002671150350000054
Four types of the raw materials are selected,
wherein,
Figure BDA0002671150350000055
the objective function with the lowest total cost is as follows:
Figure BDA0002671150350000056
wherein M isrA set of crowdsourced vans available to the current cluster, r 1, …, m; cgThe fixed use and maintenance cost of the truck is shown; xrA variable of 0-1, indicating whether vehicle r is applied; n is a radical ofiTo generate a set of customers for a shipping requirement, i is 1, …, n; cdRepresenting the cost of the truck in unit distance of travel when empty; driRepresents the distance from truck r to customer i;
Figure BDA0002671150350000057
a variable of 0 to 1 indicating whether customer i is the first customer serviced by truck r; dijRepresents the distance from customer i to customer j;
Figure BDA0002671150350000058
a variable of 0 to 1 indicates whether the truck r serves the customer i and then serves the customer j immediately; cwRepresents the cost per unit distance a truck will transport per unit weight; wriRepresenting the total weight of goods on the truck after the truck r arrives at the customer i for taking goods; ciRepresents the penalty cost of customer i; xiA variable of 0-1, indicating whether the client i gets service within the appointed time; t isiRepresenting the time required for the truck to arrive at customer point i; gamma ray1A penalty coefficient representing the advance arrival of the truck for providing service; gamma ray2A penalty coefficient representing the delay of the truck to reach the service; [ ETi,LTi]An ideal service time window for client i; [ eT ]i,lTi]For client i to be servedA maximum time window. i is a client with current logistics demand, j is a client with current logistics demand or a cluster logistics center E, when j is N +1, it is indicated that the truck finally returns the freight to the cluster logistics center, i and j can not represent the same client, j is not equal to i, i belongs to Ni，j∈Ni∪E。
And S2, taking the customer demand, the vehicle capacity and the time window requirement as constraints under the condition that the customer demand amount, the customer position and the crowd-sourced vehicle position are known.
1) At least one truck is selected to serve all logistics demanding parties,
Figure BDA0002671150350000061
2) the final selected truck has the ability to meet all customer requirements, can complete all collection services,
Figure BDA0002671150350000062
wherein QiRepresenting the freight demand of the client i; qrIndicating the nominal load capacity of the vehicle r.
3) The total weight W of goods on the truck after the truck r arrives at the customer i for taking goodsriEqual to the total weight of the previous point plus the freight requirement Q of the customer ii。
4) The actual cargo collection quantity of the vehicles on each path does not exceed the rated cargo carrying quantity, namely, the reasonability of the distribution of the cargo collection tasks of all trucks is ensured,
Figure BDA0002671150350000063
wherein
Figure BDA0002671150350000064
A variable of 0 to 1 indicates whether customer i is serviced by truck r.
5) The total cargo collection weight of each vehicle is equal to the cargo transportation demand of all customers, namely all the cargos are guaranteed to be collected,
Figure BDA0002671150350000065
6) based on time windowsThe constraint condition of penalty cost determines the penalty cost C of the client i according to the time of providing service by the trucki。
And S3, matching the positions of the available trucks in the crowdsourcing mode with the positions and requirements of logistics demand parties, performing cluster selection on the vehicles and the customers by using cluster analysis and a greedy algorithm, determining the vehicles applied by the path optimization and arranging a route.
Firstly, clustering analysis is used for initial clustering, namely, a client near a truck and the truck are clustered, all vehicles and client nodes are clustered according to the principle of limited distance, and the clients with close distance are generally assigned to the trucks with close distance. And simultaneously, determining the minimum number of trucks and determining a vehicle selection scheme by using a greedy idea. And secondly, performing secondary clustering on the determined trucks and the clients, and clustering and distributing the clients to the determined trucks according to the distance and the truck cargo capacity.
And S4, calculating the solution of the previous step as an initial population by using a genetic algorithm, optimizing a path, and storing the scheme.
The method comprises the steps of selecting a vehicle path, determining a truck path by taking the shortest total distance as a target according to a clustering scheme obtained in the previous step, namely an initial population of a genetic algorithm, then applying the genetic algorithm to optimize the path by taking the lowest cost as the target based on the current clustering scheme, adjusting the number of vehicles and the vehicle selection scheme in the optimization process, calculating the lowest cost, and storing the scheme with the lowest cost.
And S5, carrying out verification and comparison on the obtained different schemes to obtain an optimal path scheme.
And finally, verifying the obtained scheme, namely judging all constraint conditions one by one, wherein the optimal solution is obtained when all constraint conditions are met, otherwise, the truck selection scheme and the driving path need to be changed, and finally obtaining the optimal solution by comparing and selecting the solutions of different schemes in the optimization process, thereby obtaining the finally required truck selection and path optimization scheme.
As shown in fig. 3, to meet the logistics demand of the traditional industry cluster, the goods are collected based on the crowd-sourced mode.
And optimizing the goods collecting path according to the logistics requirement of the family textile industry cluster at a certain moment. With the logistics park D as a central point, the locations of 20 customers are placed in a 100 x 100 coordinate system, the distance between two adjacent coordinates is 1 km, and the customer locations, the logistics demand and the maximum service time window are known, as shown in table 1.
TABLE 1
Figure BDA0002671150350000071
Randomly generating 5 trucks with different positions in a coordinate system, wherein the types of the trucks are consistent, the rated cargo capacity is 5 tons, and the average speed of the trucks is 50 kilometers per hour; setting the fixed use and maintenance cost C of the truck according to the actual situationg90 yuan/vehicle; cost C of freight train unit distance of traveling when no loadd3 yuan/km; cost C of truck to transport unit weight per unit distancew0.9 yuan/ton km; penalty cost C of client iiSet to 20 yuan/min.
Randomly generated vehicle locations are shown in table 2, where the point numbered 0 is the D logistics park. Taking the lowest total cost of collection as an objective function:
Figure BDA0002671150350000081
TABLE 2
Figure BDA0002671150350000082
Constraints are set from customer requirements, vehicle capacity, time window angle.
As shown in fig. 4, the solution is performed using a genetic algorithm that combines cluster analysis with a greedy algorithm followed by a refinement. Initial clustering is performed first, then path optimization is performed on the initial scheme, the scale of the genetic race is set to be 100, the iteration number is set to be 200, and the final path optimization result is shown in table 3.
TABLE 3
Figure BDA0002671150350000083
As can be seen from table 3, the trucks numbered 1, 2, 4, and 5 are finally selected to provide services to 20 customers, the goods loaded by each crowd-sourced truck do not exceed the rated loading capacity, the time window requirement of each customer is met, and the total service cost after path optimization is 2705.52 yuan. Namely, the result is the optimal cargo gathering path of the traditional industry cluster.
The above embodiments are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modifications made on the basis of the technical scheme according to the technical idea of the present invention fall within the protection scope of the present invention.

Claims (3)

1. A traditional industry cluster collection path optimization method based on a crowdsourcing mode is characterized by comprising the following steps:
step 1, analyzing the cost in the traditional industrial cluster cargo collection process, and establishing a target function with the lowest total cost as a target;
the objective function is as follows:
Figure FDA0002671150340000011
wherein C represents the total cost; c1、C2、C3、C4Respectively representing the truck fixed cost, the truck running cost, the truck loading cost and the time window transportation punishment cost; cgRepresenting fixed use and maintenance costs, X, of a single truckrIndicating whether truck r is in use, XrIs a variable from 0 to 1, r is 1, …, m represents the number of available trucks; cdRepresenting the cost per unit distance travelled by a single truck when empty, driIndicating the distance of the truck r from the logistics consumer i,
Figure FDA0002671150340000012
indicating whether the logistics consumer i is the first customer serviced by truck r,
Figure FDA0002671150340000013
is a variable from 0 to 1, i is 1, …, n, n represents the number of logistics demand side; dijIndicating the distance from the logistics demand side i to the logistics demand side j,
Figure FDA0002671150340000014
indicating whether the truck r serves the logistics demand side i and then serves the logistics demand side j,
Figure FDA0002671150340000015
the variable is 0-1, j is 1, …, n +1, j is not equal to i, j is n +1, which indicates that the truck returns the freight to the cluster logistics center; cwRepresenting the cost per unit distance a single truck will transport per unit weight, WriThe total weight of goods on the truck after the truck r arrives at the logistics demand party i for taking goods is represented; ciPenalty cost, X, representing the logistics demander iiIndicating whether the logistics demand party i gets the service in the appointed time, XiIs a variable from 0 to 1;
step 2, setting constraint conditions under the condition of knowing the position of a logistics demander, the freight demand of the logistics demander, the position of a truck and the rated freight volume of the truck;
step 3, taking the positions of the trucks as a clustering center, performing initial clustering on logistics demanders by adopting a clustering analysis method, simultaneously applying greedy thought, determining the lowest truck number and the corresponding truck, and simultaneously determining the logistics demander corresponding to the service of each truck;
step 4, performing secondary clustering by adopting a clustering analysis method according to the lowest truck number and the corresponding truck determined in the step 3, and distributing logistics demand parties to the determined trucks;
step 5, determining the path of the truck by taking the shortest total distance as a target according to the clustering result of the step 4, taking the path of the truck as an initial population of a genetic algorithm, optimizing the path of the truck by adopting the genetic algorithm by taking the lowest total cost as a target based on the clustering result of the step 4, and adjusting the number of the trucks and logistics demand parties corresponding to services of each truck in the optimization process, so as to obtain the number of the trucks corresponding to the lowest total cost and the path of each truck, namely a scheme with the lowest total cost;
step 6, judging the lowest total cost scheme obtained in the step 5 by adopting the constraint conditions set in the step 2, wherein when the lowest total cost scheme obtained in the step 5 meets all the constraint conditions, the scheme is the optimal solution; otherwise, adjusting the logistics demand side of the corresponding service of each truck and the path of the truck until the optimal solution is obtained.
2. The crowd-sourcing mode-based conventional industry cluster cargo gathering path optimization method as claimed in claim 1, wherein the penalty cost C of the logistics demand party i in step 1iExpressed as:
Figure FDA0002671150340000021
wherein, TiIndicates the time, gamma, required for the truck to arrive at the logistics party i1Penalty factor, gamma, representing the advance arrival of a truck to provide service2Penalty factor, LT, indicating the delayed arrival of a truck to provide servicei、ETiRespectively representing the upper limit and the lower limit, lT, of the ideal service time window of the logistics demand party ii、eTiRespectively representing the upper limit and the lower limit of the maximum service time window which can be accepted by the logistics demand party i.
3. The conventional industry cluster cargo gathering path optimizing method based on crowdsourcing mode as claimed in claim 1, wherein the constraint conditions in step 2 are as follows:
constraint 1: at least one truck is selected to serve all the logistics consumers, i.e.
Figure FDA0002671150340000022
Constraint 2: the final determined truck being able to perform all the collecting services, i.e.
Figure FDA0002671150340000023
QrIndicating the nominal cargo capacity, Q, of the truck riRepresenting the freight demand of the logistics demand party i;
constraint 3: the total weight W of goods on the truck after the truck r arrives at the logistics demand party i for taking goodsriEqual to the total weight of goods on the truck after the truck r takes goods from the previous logistics demand party plus the freight demand Q of the logistics demand party ii；
Constraint 4: the actual cargo-collecting capacity of the truck r not exceeding its nominal capacity, i.e.
Figure FDA0002671150340000024
Figure FDA0002671150340000031
Indicating whether the logistics customers i are serviced by the trucks r,
Figure FDA0002671150340000032
is a variable from 0 to 1;
constraint 5: the final determined total cargo collection amount of all trucks is equal to the total cargo demand amount of all logistics demand parties, namely
Figure FDA0002671150340000033
Constraint 6: determining the punishment cost C of the logistics demander i according to the service providing time of the truck and the upper limit and the lower limit of the maximum service time window which can be accepted by the logistics demander ii。
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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106779570A (en) * 2017-03-10 2017-05-31 镇江康飞汽车制造股份有限公司 A kind of intelligent Cold Chain Logistics path multi-objective optimization algorithm
CN109034481A (en) * 2018-07-31 2018-12-18 北京航空航天大学 A kind of vehicle routing problem with time windows modeling and optimization method based on constraint planning
CN110197311A (en) * 2019-06-12 2019-09-03 江苏航运职业技术学院 A kind of logistics distribution paths planning method based on intelligent optimization

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106779570A (en) * 2017-03-10 2017-05-31 镇江康飞汽车制造股份有限公司 A kind of intelligent Cold Chain Logistics path multi-objective optimization algorithm
CN109034481A (en) * 2018-07-31 2018-12-18 北京航空航天大学 A kind of vehicle routing problem with time windows modeling and optimization method based on constraint planning
CN110197311A (en) * 2019-06-12 2019-09-03 江苏航运职业技术学院 A kind of logistics distribution paths planning method based on intelligent optimization

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
基于自动快递机的快递配送车辆路径优化研究;覃运梅等;《公路交通科技》;20151031;第32卷(第10期);第135-140页 *

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