CN110826968B - Urban crowdsourcing distribution task optimal scheduling method based on path planning - Google Patents

Abstract

The invention provides an urban crowdsourcing distribution task optimal scheduling method based on path planning, which comprises the following steps: constructing a crowdsourcing distribution network graph; acquiring crowdsourcing riders and crowdsourcing distribution task information; constructing a crowdsourcing distribution task optimization scheduling model based on path planning; solving the initial crowdsourcing task scheduling scheme based on a greedy strategy; and optimally scheduling the crowdsourcing distribution task based on variable neighborhood search. According to the invention, an optimized task scheduling scheme including a task set distributed by each rider and a shortest distribution path sequence can be formulated according to the position information of the rider, a merchant and a customer, the time constraint of each task and the real-time load constraint of each rider.

Description

Urban crowdsourcing distribution task optimal scheduling method based on path planning

Technical Field

The invention belongs to the field of logistics resource scheduling, and particularly relates to an urban crowdsourcing distribution task optimal scheduling method based on path planning and a support tool.

Background

With the rapid development of internet-based electronic commerce and O2O industries, people have higher and higher requirements on timeliness of logistics distribution, traditional logistics distribution is completed by full-time express delivery personnel of logistics companies, and the distribution mode is difficult to respond to the service demands of numerous consumers in time. Aiming at the condition of insufficient logistics distribution resources, an urban crowdsourcing distribution mode appears, wherein the urban crowdsourcing distribution mode transfers the distribution tasks originally born by full-time express delivery personnel of a logistics company to public groups outside the enterprise to be completed in a free and voluntary mode, and the distribution mode can effectively integrate the idle resources of the society, relieve the distribution pressure at the tail end and play a great role in solving the distribution problem of the last kilometer. Currently, many urban crowdsourced distribution service providers have emerged, such as Amazon, uber, kyoto crowdsourcing, point-to-me, and so on. Amazon recruits part-time drivers with private cars to assist in delivering logistics packages through a crowdsourcing delivery platform Amazon Flex, and pays corresponding rewards to the drivers on time. Together with local express enterprises and taxi operation companies, uber builds an urban distribution network platform for realizing the last kilometer of express delivery by taxi drivers.

Urban crowdsourcing distribution services mainly involve four categories of participants: service providers, shippers, customers, and courier-liberties (also known as riders). The service provider is responsible for building a crowdsourcing distribution platform which integrates the resources of parties such as the shipper, customer and rider and is the core of the urban crowdsourcing distribution service. The delivery side issues delivery tasks on the platform, the platform matches the delivery tasks with available riders and distributes the delivery tasks to the most appropriate riders for execution, the riders receive the delivery tasks and then get the goods at the delivery side, then the goods are sent to a specified customer location, and the quality evaluation can be carried out on the services of the riders through the platform after the customers receive the goods. The main functions of the crowdsourcing distribution platform comprise participant registration, qualification authentication, task distribution, task monitoring, quality evaluation and the like.

The crowdsourcing distribution task allocation is divided into two modes: the system comprises a list grabbing mode and a list dispatching mode, wherein the list grabbing mode is that a rider actively selects a distribution task according to own preference, and the list dispatching mode is that a platform distributes the distribution task to the designated rider to execute according to a certain strategy.

At present, many researches on city crowdsourcing distribution task optimization scheduling in a dispatch mode exist, the researches are used for optimizing task distribution to improve task distribution rate so as to optimize task distribution, or optimizing matching relation between tasks and riders, which tasks are more suitable for which riders, or optimizing selection schemes of the riders, however, some researches only carry out task distribution aiming at the starting points of the tasks, and other factors influencing task distribution and rider distribution, such as task end points, task merchant service time and task customer service time, are ignored. For crowdsourcing tasks to be distributed in cities, how to perform more optimal task scheduling by using a crowdsourcing mode to minimize the overall cost or minimize the overall distribution path is a problem to be solved currently.

Disclosure of Invention

The invention makes the overall delivery path shortest by optimizing the scheduling of urban crowdsourcing delivery tasks in the dispatch mode. The invention provides an urban crowdsourcing distribution task optimal scheduling method based on path planning, which comprises the following steps of:

step 1: constructing a crowdsourcing distribution network graph;

step 2: acquiring crowdsourcing riders and crowdsourcing distribution task information;

and 3, step 3: constructing a crowdsourcing distribution task optimization scheduling model based on path planning;

and 4, step 4: solving the initial crowdsourcing task scheduling scheme based on a greedy strategy;

and 5: and performing optimized scheduling on the crowdsourcing distribution task based on variable neighborhood search.

In a further technical solution, in step 1, the crowdsourcing distribution network graph is constructed as follows:

the crowdsourcing distribution network graph may be represented as graph G = (V, E), where V = V _S ∪V _C For a collection of nodes, each node representing a merchant or customer, V _S ＝{1,2,…,n _s Is the set of merchants, V _C ＝{n _s +1,n _s +2,…,n _c Is a set of clients, n _s And n _c The number of merchants and customers respectively; e = { (i, j) | i, j ∈ V } is a set of edges, and each edge (i, j) ∈ E, d _ij Represents the distance between node i and node j, d _ij ＝d _ji ，d _ii ＝0。

In a further technical scheme, in the step 2, the information of crowdsourcing riders and crowdsourcing distribution tasks is obtained as follows:

the crowdsourced rider is represented as the set K = {1,2, \8230;, n _k }，n _k For crowdsourced rider numbers, K ∈ K, Q for each rider _k Represents the maximum load capacity, v, of rider k _k Represents the average running speed of rider k, d' _ki Representing the distance from the position of the rider k to the node i epsilon V;

the crowdsourced delivery tasks are represented as the set T = {1,2, \8230;, n _t }，n _t For the number of delivery tasks, each delivery task te ∈ T is represented as a six-tuple: (s) _t ,c _t ,b _t ,f _t ,e _t ,w _t ) Wherein s is _t ∈V _S Pick-up merchant, c, representing task t _t ∈V _C Delivery client representing task t, b _t Indicating the earliest starting time of task t, i.e. from merchant s _t The earliest time of starting to take goods, f _t Indicating the latest start time of task t, i.e. from merchant s _t The latest starting time of picking up goods, e _t Indicating the latest end time of task t, i.e. delivery of goods to client c _t Latest end time of, w _t Representing the weight of the load at task t, the weight of the load at each delivery task not exceeding the maximum load of any rider, i.e. w _t ≤min{Q _k |k∈K}；

For each node i ∈ V, let s _i Represents the average service time per task at node i if i ∈ V _S ，s _i Represents the average picking time of the rider at the merchant i if i belongs to V _C ，s _i The average delivery time of the rider at the client i is shown, and the average service time of the task is calculated by a mathematical statistic method according to historical data.

In a further technical scheme, in the step 3, the building of the optimal scheduling model of the crowdsourcing distribution task based on the path planning is specifically as follows:

establishing a task distribution scheme: a task allocation scheme is expressed as a mapping function a from a crowd-sourced distribution task set T to a crowd-sourced rider set K, wherein for a task T epsilon T, if a (T) = K epsilon K, the task T is distributed to the rider K for distribution, and if a (T) =0, the task T is not distributed; let T _a (k) = T ∈ T | a (T) = K, K ∈ K } represents the set of tasks assigned to rider K, if

Indicates that there is noAssigning a delivery task to rider k; let T _a Represents all assigned task sets, then T _a ＝U _k∈K T _a (k) (ii) a There are many allocation schemes from set T to set K, let Ω (T, K) denote the set of all task allocation schemes from T to K;

defining a task sequence and a distribution path: a is expressed as a task allocation scheme, a is equal to omega (T, K), and the task set allocated to the rider K is expressed as

T _a (k) Is a multiple set of task sequences

Is expressed as: p is a radical of _ak ＝p _ak (1),p _ak (2),…,p _ak (2|T _a (k) In which p) is _ak (l) For a task sequence p _ak Task corresponding to the first position, l =1,2, \ 8230;, 2Y ray T _a (k)|；

a ∈ Ω (T, K) is expressed as a task allocation scheme, T _a (k) Represented as a set of tasks, p, assigned to rider k _ak ∈P _a (k) Expressed as a sequence of tasks, then p _ak The distribution path is a path which starts from the position of the rider k and passes through p in sequence _ak For each task in (1), denoted v _ak ＝v _ak (0),v _ak (1),…,v _ak (2|T _a (k) L) wherein v _ak (0) K, representing the position of the rider k, v _ak (l) Represents P _a (k) The node corresponding to the first task in (1), l =1,2, \ 8230;, 2 purple T _a (k) If the node corresponding to the task is a customer node, the node corresponding to the task is a merchant node;

the distribution distance function is expressed as

Wherein p is _ak ∈P _a (k) Is a sequence of tasks, v _ak For the corresponding distribution route, the distribution route is selected,

representing the rider k to the first node v _ak (1) K = v, k = v _ak (0)，

Denotes v _ak The sum of the distances between two adjacent nodes, if the two adjacent nodes are the same node, the distance is 0;

setting time constraints and load constraints: the time constraint function is expressed as H (p) _ak T) is { true, false }, where T is T _a (k) Denoted as a task, p _ak ∈P _a (k) Expressed as a sequence of tasks, if a task t follows p _ak The corresponding distribution route v in _ak The delivered goods meets the merchant's latest pick time and customer's latest delivery time constraints, then H (p) _ak T) = true, otherwise, H (p) _ak ,t)＝false；

The load constraint function is denoted as Q (p) _ak ) E { true, false }, where p _ak ∈P _a (k) Is a task sequence if the rider k follows the task sequence p _ak Corresponding distribution route v _ak The delivered cargo can satisfy its maximum load constraint, then Q (p) _ak ) = true, otherwise Q (p) _ak )＝false；

Defining a crowdsourcing distribution task optimization scheduling model based on path planning as follows:

max∑ _k∈K |T _a (k)| (1)

min∑ _k∈K d(p _ak ) (2)

s.t.

Q(p _ak )＝true p _ak ∈P _a (k) (3)

H(p _ak )＝true p _ak ∈P _a (k) (4)

p _ak ∈P _a (k)a∈Ω(T,K),k∈K (5)

a∈Ω(T,K) (6)。

in a further technical scheme, in the step 4, solving the initial crowdsourcing task scheduling scheme based on the greedy strategy specifically includes:

definition of T ⁰ _a (k) For the set of tasks currently assigned to rider k, p ⁰ _ak For a task sequence of a rider k currently satisfying the constraint, T belongs to T-U _k∈K T ⁰ _a (k) For an unallocated task, if t is inserted into p ⁰ _ak The obtained new task sequence still satisfies the load constraint and the time constraint, and t is called as p ⁰ _ak An extensible task of, T ¹ _a (k)＝T ⁰ _a (k) U { t } is a task set of the rider k after the insertion of the task t;

inserting a task t into a task sequence p ⁰ _ak There are two ways: adjacent and spaced; adjacent insertion refers to the insertion of a task t into p ⁰ _ak Then, two positions of the task t in the new task sequence are adjacent; spaced insertion refers to insertion of a task t into p ⁰ _ak Then, two positions of the task t in the new task sequence are not adjacent;

the method comprises the following specific steps:

step 1: according to the rider k _i Position of rider k is obtained _i Obtaining a shortest path length set D completed by all tasks which are not distributed with a distance set S of all business points which are not distributed with the tasks, adding the subscript values corresponding to the two sets, sequencing the two sets from small to large according to the addition and storing the two sets to a task distribution sequence T ⁰ _a (k _i )；

Step 2: at T ⁰ _a (k _i ) Selecting a first task t ₁ Add the task to rider k _i Task delivery sequence p of _a (k _i ) As a rider k _i The first task is delivered, and meanwhile, the task state is set as the delivered state;

step 3: adding a second task, adding T ⁰ _a (k _i ) Each task t in ₂ ,t ₃ ,…,t _j By adjacent and spaced insertionThe strategies all add to p _a (k _i ) In the first pass, p obtained if some kind of insertion strategy _a (k _i ) Satisfy t ₁ And t _j Time constraint of (1), rider k _i Is limited in capacity, then the task t is saved _j Task delivery sequence p added to the rider according to this insertion strategy _a (k _i ) After all tasks are inserted into the sequence R, the sequence R is arranged in an ascending order, and the task t with the minimum path length _j As a second delivery task, add to p according to insertion position _a (k _i ) In the meantime, a new task delivery sequence p is delivered _a (k _i ) As a task delivery sequence to add the next task, t _j The state becomes delivered; if all insertion strategies yield p _a (k _i ) All do not satisfy the constraint, then p _a (k _i ) To the final rider k _i The task distribution sequence of (1);

step 4: when the sequence R is null, according to p _a (k _i ) Task relation graph acquisition rider k _i V of _aki While obtaining rider k _i Delivery of p _a (k _i ) Path length d (p) of _aki ) (ii) a Let t = t +1, return to Step 2;

step 5: when t = n _t And +1 or when the states of all the tasks are distributed, finishing the algorithm to obtain an initial solution set.

In a further technical solution, in the step 5, the optimized scheduling of the crowdsourcing distribution task based on the variable neighborhood search includes four neighborhood structures, which are respectively:

(1) Two random riders randomly exchange tasks in two task sequences;

(2) Randomly exchanging merchant points of two tasks in a task sequence of a rider;

(3) Randomly exchanging client points of two tasks in a task sequence of a rider;

(4) Two riders are randomized, tasks in a task sequence of one rider are randomized and added in a task sequence of the other rider;

the method comprises the following specific steps:

step 1: obtaining an initial solution p for rider k _ak Let the optimal solution be p _{ak_best} ；

Step 2: defining a set of neighborhood structures N _s ，s＝1,2…,s _max As perturbation operation, where N _s The method comprises the steps of exchanging task operations of a rider, exchanging task sequence merchant point operations, exchanging task sequence customer point operations and transferring task operations of the rider;

step 3: defining a neighborhood structure set N _q ，q＝1,2…,q _max As a neighborhood search, where N _q The method comprises the steps of exchanging task operations of a rider, exchanging task sequence merchant point operations, exchanging task sequence customer point operations and transferring task operations of the rider;

step 4: let s =1,q =1;

step 5: using N _s To p is p _ak Disturbing to generate solution p' _ak ；

Step 6: using N _q To p' _ak Search is carried out to generate solution p' _ak ；

Step 7: if d (p' _ak )<d(p” _ak ) Then p' _ak ＝p” _ak Q =1; otherwise, q = q +1;

step 8: if q is<q _max Jumping to Step 6; otherwise, executing Step 9;

step 9: if d (p) " _ak )<d(p _ak ) Then p is _ak ＝p” _ak S =1; otherwise, s = s +1;

step 10: if s<s _max Jumping to Step 5; otherwise, p _{ak_best} ＝p” _ak Step 11 is executed;

step 11: inputting an optimal solution p _{ak_best} And the algorithm ends.

The invention also provides a support tool for realizing the city crowdsourcing distribution task optimal scheduling method based on the path planning.

The function module of the support tool is mainly divided into a Web end and an App end. The system is divided into two modules: basic information management and crowdsourcing task scheduling management.

(1) The basic information management functions include: user information management, order management, and rider information management, etc.

(2) The crowdsourcing task scheduling management is to distribute task orders, and the functions comprise: task scheduling algorithm management, task path planning, task real-time distribution, distribution result management and the like. The client can place an order on the Web end or the App end, and the support tool distributes tasks to the rider through a scheduling algorithm. And after the rider successfully logs in the App end, receiving the distributed tasks and distributing according to the path planned by the algorithm.

According to the technical scheme, the invention has the following advantages:

the crowdsourcing distribution network graph can formally map the actual position points to position points in a coordinate system, the main information of crowdsourcing riders and crowdsourcing distribution tasks is analyzed to ensure the effectiveness of the information, and the mapping relation between the tasks and the riders is vividly depicted; modeling the task sequence, the distribution path and the distribution path length, determining the relationship between the task sequence and the task set, analyzing the relationship between the task sequence and the distribution path, and setting time constraint and load constraint for constraining each task sequence; meanwhile, a crowdsourcing distribution task optimal scheduling model based on path planning is defined, an optimal target and constraint conditions are determined, an initial crowdsourcing task scheduling scheme is solved based on a greedy strategy, and crowdsourcing distribution tasks are optimally scheduled based on variable neighborhood search, so that tasks can be reasonably distributed, the length of a total distribution path is reduced, and distribution cost is reduced.

Drawings

In order to more clearly illustrate the technical solution of the present invention, the drawings used in the description will be briefly introduced, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.

FIG. 1 is a flow chart of a city crowdsourcing distribution task optimization scheduling method based on path planning;

FIG. 2 is a schematic diagram of a crowdsourcing distribution network of the present invention;

FIG. 3 is a schematic diagram of the task of obtaining crowd-sourced riders and crowd-sourced distribution of the present invention;

FIG. 4 is a flowchart of an algorithm for solving the initial crowdsourced task scheduling scheme based on the greedy policy of the present invention.

FIG. 5 is a flowchart of the crowd-sourced distribution task optimized scheduling algorithm based on variable neighborhood search according to the present invention.

Detailed Description

The invention provides a city crowdsourcing distribution task optimal scheduling method based on path planning, which comprises the following steps of:

step 1: constructing a crowdsourcing distribution network graph;

step 2: acquiring crowdsourcing riders and crowdsourcing distribution task information;

and step 3: constructing a crowdsourcing distribution task optimization scheduling model based on path planning;

and 4, step 4: solving the initial crowdsourcing task scheduling scheme based on a greedy strategy;

and 5: and performing optimized scheduling on the crowdsourcing distribution task based on variable neighborhood search.

In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions of the present invention will be clearly and completely described below with reference to specific embodiments and drawings. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the scope of protection of this patent.

Step 1: the crowdsourcing distribution network graph may be represented as graph G = (V, E), where V = V _S ∪V _C For a collection of nodes, each node representing a merchant or customer, V _S ＝{1,2,…,n _s Is the set of merchants, V _C ＝{n _s +1,n _s +2,…,n _c Is the set of clients, n _s And n _c The number of merchants and customers, respectively; e = { (i, j) | i, j ∈ V } is a set of edges, for each edge (i, j) ∈ E,d _ij represents the distance between node i and node j, d _ij ＝d _ji ，d _ii ＝0。

The invention maps actual position points including merchant points, customer points and the like into nodes of a graph, the position of each node is represented by two-dimensional coordinates (x, y), edges exist between the nodes, and the distance between the nodes is calculated by using the coordinates of the nodes.

FIG. 2 depicts the mapping of merchant actual locations and customer actual locations into nodes of a graph, for a total of 10 location points, including 3 merchants and 7 customers, i.e., V _S ＝{1,2,3}，V _C = 4,5, \8230;, 10, for example node 1 represents merchant 1. A crowd-sourced distribution network graph is obtained.

Step 2: the crowdsourced rider is represented as the set K = {1,2, \8230;, n _k }，n _k For the number of crowdsourced riders, K ∈ K, Q for each rider _k Represents the maximum load capacity, v, of rider k _k Represents the average running speed of rider k, d' _ki Representing the distance from the position of the rider k to the node i ∈ V;

the crowdsourced delivery task is represented as the set T = {1,2, \8230;, n _t }，n _t For the number of the delivery tasks, each delivery task te ∈ T is expressed as a six-tuple: (s) _t ,c _t ,b _t ,f _t ,e _t ,w _t ) Wherein s is _t ∈V _S Pick-up merchant, c, representing task t _t ∈V _C Delivery client representing task t, b _t Indicating the earliest starting time of task t, i.e. from merchant s _t The earliest time of starting to take goods, f _t Indicating the latest start time of task t, i.e. from merchant s _t At the latest at the time of the start of the pickup, e _t Indicating the latest end time of task t, i.e. delivery of goods to client c _t Latest end time of (w) _t Representing the weight of the load at task t, the weight of the load at each delivery task not exceeding the maximum load of any rider, i.e. w _t ≤min{Q _k |k∈K}；

For each node i ∈ V, let s _i Representing average service per task at node iTime, if i ∈ V _S ，s _i Represents the average picking time of the rider at the merchant i if i belongs to V _C ，s _i The average delivery time of the rider at the client i and the average service time of the task are calculated by a mathematical statistic method according to historical data.

According to the invention, crowdsourcing riders and crowdsourcing tasks are configured in a crowdsourcing distribution network diagram, and the position of each rider, the basic information of each rider and the basic information of each task are obtained through the Internet technology and the Internet of things technology. And numbering the rider and the task, and storing rider information and task information into a database. And determining the merchant node and the customer node of the task according to the task information.

Fig. 3 depicts a diagram of a crowdsourced distribution network after the deployment of crowdsourced riders and crowdsourced tasks, for a total of 3 riders, i.e., K = {1,2,3}, with the rider's primary information including the rider number, rider maximum payload, and average rider speed as shown in table 1. There are 7 delivery tasks, i.e., T = {1,2, \8230;, 7}, and the main information of the tasks includes a task number, a merchant, a customer, a weight of goods, a merchant service time, a customer service time, an earliest pickup time, a latest pickup time, and a latest delivery time as shown in table 2.

TABLE 1 rider Main information Table

Number of rider	Maximum load (kg)	Average velocity (m/s)
			1	3	3
2	4	3
			3	3	3

TABLE 2 task Main information Table

And 3, step 3: establishing a task distribution scheme: a task allocation scheme is expressed as a mapping function a from a crowd-sourced distribution task set T to a crowd-sourced rider set K, wherein for a task T epsilon T, if a (T) = K epsilon K, the task T is distributed to the rider K for distribution, and if a (T) =0, the task T is not distributed; let T _a (k) = T ∈ T | a (T) = K, K ∈ K } represents the set of tasks assigned to rider K, if

Indicating that no delivery task has been assigned to rider k; let T _a Represents the set of all assigned tasks, then T _a ＝U _k∈K T _a (k) (ii) a There are many allocations from set T to set K, let Ω (T, K) denote the set of all task allocations from T to K;

defining a task sequence and a distribution path: a is expressed as a task allocation scheme, a is equal to omega (T, K), and the task set allocated to the rider K is expressed as

T _a (k) Is a multi-set

Is expressed as: p is a radical of _ak ＝p _ak (1),p _ak (2),…,p _ak (2|T _a (k) In which p) is _ak (l) For a task sequence p _ak Task corresponding to the first position, l =1,2, \ 8230;, 2Y ray T _a (k)|；

a ∈ Ω (T, K) is expressed as a task allocation scheme, T _a (k) Represented as a set of tasks, p, assigned to rider k _ak ∈P _a (k) Expressed as a sequence of tasks, then p _ak The distribution path is a path which starts from the position of the rider k and passes through p in sequence _ak Is represented by v, the sequence of merchant or customer nodes to which each task in (a) is directed _ak ＝v _ak (0),v _ak (1),…,v _ak (2|T _a (k) L) wherein v _ak (0) K, representing the position of the rider k, v _ak (l) Represents P _a (k) The node corresponding to the first task in (1), l =1,2, \ 8230;, 2 purple T _a (k) If the node corresponding to the task is a customer node, the node corresponding to the task is a merchant node;

the distribution distance function is expressed as

Wherein p is _ak ∈P _a (k) Is a sequence of tasks, v _ak For the corresponding distribution route,

representing the rider k to the first node v _ak (1) K = v, k = v _ak (0)，

Denotes v _ak The sum of the distances between two adjacent nodes is 0 if the two adjacent nodes are the same node;

setting time constraints and load constraints: the time constraint function is expressed as H (p) _ak T) is { true, false }, where T is T _a (k) Denoted as a task, p _ak ∈P _a (k) Expressed as a sequence of tasks, if a taskt is according to p _ak The corresponding distribution route v in _ak The delivered goods meets the merchant's latest pick time and customer's latest delivery time constraints, then H (p) _ak T) = true, otherwise, H (p) _ak ,t)＝false；

The load constraint function is denoted as Q (p) _ak ) E { true, false }, where p _ak ∈P _a (k) Is a task sequence if the rider k follows the task sequence p _ak Corresponding distribution route v _ak The delivered cargo can meet its maximum load constraint, Q (p) _ak ) = true, otherwise Q (p) _ak )＝false；

Defining a crowdsourcing distribution task optimization scheduling model based on path planning as follows:

max∑ _k∈K |T _a (k)| (1)

min∑ _k∈K d(p _ak ) (2)

s.t.

Q(p _ak )＝true p _ak ∈P _a (k) (3)

H(p _ak )＝true p _ak ∈P _a (k) (4)

p _ak ∈P _a (k)a∈Ω(T,K),k∈K (5)

a∈Ω(T,K) (6)。

the invention defines a crowdsourcing distribution task optimization scheduling model based on path planning, and aims to ensure that the total distribution path is shortest under the condition of the maximum number of distributed tasks. The task allocation scheme and the task sequence construct a distribution distance function, a time constraint function and a load constraint function. Each rider may correspond to multiple task assignments, each of which in turn corresponds to multiple task sequences. The distribution path length of the task sequence is determined through a distribution distance function, and then the task sequence with the smaller distribution path is selected through comparison of the distribution path length and the distribution path length. And judging whether each task sequence meets the condition or not through a time constraint function and a load constraint function, and discarding if the task sequence does not meet the condition.

And 4, step 4: definition of T ⁰ _a (k) For the task set currently assigned to rider k, p ⁰ _ak Is k when the rider isTask sequence with front satisfying constraint, T belongs to T-U _k∈K T ⁰ _a (k) For an unallocated task, if t is inserted into p ⁰ _ak The obtained new task sequence still satisfies the load constraint and the time constraint, and t is called p ⁰ _ak An extensible task of, T ¹ _a (k)＝T ⁰ _a (k) U { t } is a task set of the rider k after the insertion of the task t;

inserting task t into task sequence p ⁰ _ak There are two ways: adjacent and spaced; adjacent insertion refers to inserting task t into p ⁰ _ak Then, two positions of the task t in the new task sequence are adjacent; with interval insertion is meant that the task t inserts into p ⁰ _ak Then, two positions of the task t in the new task sequence are not adjacent;

the method comprises the following specific steps:

step 1: according to the rider k _i Position of rider k is obtained _i And obtaining a shortest path length set D of all the un-distributed tasks by combining with distance sets S of all the un-distributed task merchant points, adding corresponding subscript values of the two sets, sequencing the two sets from small to large according to the sum, and storing the sum to a task distribution sequence T ⁰ _a (k _i )；

Step 2: at T ⁰ _a (k _i ) Selecting a first task t ₁ Add the task to rider k _i Task distribution sequence p of _a (k _i ) As a rider k _i The task state is set as the distributed state;

step 3: adding a second task, adding T ⁰ _a (k _i ) Each task t in ₂ ,t ₃ ,…,t _j Both add to p with adjacent and spaced insertion strategies _a (k _i ) In one pass, p obtained if some of the insertion strategies _a (k _i ) Satisfy t ₁ And t _j Time constraint of (1), rider k _i Is limited in capacity, then the task t is saved _j Task delivery sequence p added to the rider according to this insertion strategy _a (k _i ) After all the tasks are inserted into the sequence R, the sequence R is arranged in an ascending order, and the task t with the minimum path length _j As a second delivery task, add to p according to insertion position _a (k _i ) In the meantime, a new task delivery sequence p is delivered _a (k _i ) As a task delivery sequence to add the next task, t _j The status changes to delivered; if p is obtained by all insertion strategies _a (k _i ) All do not satisfy the constraint, then p _a (k _i ) To the final rider k _i The task distribution sequence of (1);

step 4: when the sequence R is null, according to p _a (k _i ) Task relation graph acquisition rider k _i V of _aki While obtaining rider k _i Delivery of p _a (k _i ) Path length d (p) of _aki ) (ii) a Let t = t +1, return to Step 2;

step 5: when t = n _t And when the +1 or all the tasks are distributed, finishing the algorithm to obtain an initial solution set. The invention defines an initial crowdsourcing task scheduling scheme solving algorithm based on a greedy strategy, and the core idea is to find out the current superior task scheduling scheme of each rider by using the greedy strategy.

Fig. 4 depicts a flow of an initial crowd-sourced task scheduling scheme solving algorithm based on a greedy policy.

And 5: including four neighborhood structures, do respectively:

(1) Two random riders randomly exchange tasks in two task sequences;

(2) Randomly exchanging merchant points of two tasks in a task sequence of a rider;

(3) Randomly exchanging client points of two tasks in a task sequence of a rider;

(4) Two riders are randomized, tasks in a task sequence of one rider are randomized and added in a task sequence of the other rider;

the method comprises the following specific steps:

step 1: obtaining an initial solution p for rider k _ak Let the optimal solution be p _{ak_best} ；

Step 2: defining a set of neighborhood structures N _s ，s＝1,2…,s _max As perturbation operation, where N _s The method comprises the steps of exchanging task operations of a rider, exchanging task sequence merchant point operations, exchanging task sequence customer point operations and transferring task operations of the rider;

step 3: defining a set of neighborhood structures N _q ，q＝1,2…,q _max As a neighborhood search, where N _q The method comprises the steps of exchanging task operations of a rider, exchanging task sequence merchant point operations, exchanging task sequence customer point operations and transferring task operations of the rider;

step 4: let s =1,q =1;

step 5: using N _s To p _ak Disturbing to generate solution p' _ak ；

Step 6: using N _q To p' _ak Search is carried out to generate a solution p' _ak ；

Step 7: if d (p' _ak )<d(p” _ak ) Then p' _ak ＝p” _ak Q =1; otherwise, q = q +1;

step 8: if q is<q _max Jumping to Step 6; otherwise, executing Step 9;

step 9: if d (p) " _ak )<d(p _ak ) Then p is _ak ＝p” _ak S =1; otherwise, s = s +1;

step 10: if s<s _max Jumping to Step 5; otherwise, p _{ak_best} ＝p” _ak Executing Step 11;

step 11: inputting an optimal solution p _{ak_best} And the algorithm ends.

The invention provides a variable neighborhood search-based crowdsourcing distribution task optimization scheduling algorithm, and the core idea is to distribute tasks and calculate the total distribution path length through three processes of local search, disturbance and neighborhood structure search. The task allocation results are shown in table 3, the rider delivered task results are shown in table 4, and the rider delivered route results are shown in table 5.

FIG. 5 depicts a flow of a variable neighborhood search based crowd-sourced delivery task optimization scheduling algorithm.

TABLE 3 task Allocation Table

Rider number	Task allocation collections
		1	2，4，6
2	3
		3	1，5

TABLE 4 rider-delivered task List

Number of rider	Task sequence
			1	6→6→2→4→2→4
2	3→3
		3	1→5→1→5

TABLE 5 rider distribution Path Table

Rider number	Path sequence	Total path length (m)
			1	1→9→3→3→5→7	3543
2	1→6	1921
			3	2→2→4→8	2523

The invention also provides a support tool for realizing the city crowdsourcing distribution task optimal scheduling method based on the path planning.

The function module of the support tool is mainly divided into a Web end and an App end. The system is divided into two modules: basic information management and crowdsourcing task scheduling management.

(1) The basic information management functions include: user information management, order management, and rider information management, etc.

(2) The crowdsourcing task scheduling management is to distribute task orders, and the functions comprise: task scheduling algorithm management, task path planning, task real-time distribution, distribution result management and the like. The client can place an order on a Web end or an App end, and the supporting tool distributes tasks to the rider through a scheduling algorithm. And after the rider successfully logs in the App end, receiving the distributed tasks and distributing according to the path planned by the algorithm.

The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (3)

1. A city crowdsourcing distribution task optimal scheduling method based on path planning is characterized by comprising the following steps:

step 1: constructing a crowdsourcing distribution network graph;

and 2, step: acquiring crowdsourcing riders and crowdsourcing distribution task information;

and 3, step 3: constructing a crowdsourcing distribution task optimization scheduling model based on path planning;

and 4, step 4: solving the initial crowdsourcing task scheduling scheme based on a greedy strategy;

and 5: performing optimized scheduling on the crowdsourcing distribution task based on variable neighborhood search;

in the step 1, the crowdsourcing distribution network diagram is constructed as follows:

the crowdsourcing distribution network graph may be represented as graph G = (V, E), where V = V _S ∪V _C For a collection of nodes, each node representing a merchant or customer, V _S ＝{1,2,…,n _s Is the set of merchants, V _C ＝{n _s +1,n _s +2,…,n _c Is a set of clients, n _s And n _c The number of merchants and customers, respectively; e = { (i, j) | i, j ∈ V } is a set of edges, and each edge (i, j) ∈ E, d _ij Represents the distance between node i and node j, d _ij ＝d _ji ，d _ii ＝0；

Step 2, the information of crowdsourcing riders and crowdsourcing distribution tasks is obtained as follows:

the crowdsourced rider is represented as the set K = {1,2, \8230;, n _k }，n _k For crowdsourced rider numbers, K ∈ K, Q for each rider _k Represents the maximum load capacity, v, of rider k _k Represents the average running speed of rider k, d' _ki Representing the distance from the position of the rider k to the node i ∈ V;

the crowdsourced delivery task is represented as the set T = {1,2, \8230;, n _t }，n _t For the number of delivery tasks, each delivery task te ∈ T is represented as a six-tuple: (s) _t ,c _t ,b _t ,f _t ,e _t ,w _t ) Wherein s is _t ∈V _S Pick-up merchant, c, representing task t _t ∈V _C Delivery client representing task t, b _t Indicating the earliest starting time of task t, i.e. from merchant s _t The earliest time of starting to take goods, f _t Indicating the latest start time of task t, i.e. from merchant s _t The latest starting time of picking up goods, e _t Indicating the latest end time of task t, i.e. delivery of goods to client c _t Latest end time of, w _t Representing the weight of the load at task t, without exceeding the maximum load of any rider, i.e. w, for each delivery task _t ≤min{Q _k |k∈K}；

For each node i ∈ V, let s _i Represents the average service time per task at node i if i ∈ V _S ，s _i Represents the average pick-up time of the rider at the merchant i if i ∈ V _C ，s _i The average delivery time of the rider at the client i is represented, and the average service time of the task is calculated by adopting a mathematical statistic method according to historical data;

in the step 3, the optimal scheduling model of the crowdsourcing distribution task based on the path planning is constructed as follows:

establishing a task distribution scheme: a task allocation scheme is represented as a mapping function a: T → KU {0} from a crowd-sourcing distribution task set T to a crowd-sourcing rider set K, for a taskT ∈ T, if a (T) = K ∈ K, indicating that the task T is allocated to the rider K for distribution, and if a (T) =0 indicating that the task T is not allocated; let T _a (k) = T ∈ T | a (T) = K, K ∈ K } represents the set of tasks assigned to rider K, if

Indicating that no delivery task has been assigned to rider k; let T _a Represents the set of all assigned tasks, then T _a ＝∪ _k∈K T _a (k) (ii) a There are many allocation schemes from set T to set K, let Ω (T, K) denote the set of all task allocation schemes from T to K;

defining task sequence and distribution path: a is expressed as a task allocation scheme, a is equal to omega (T, K), and the task set allocated to the rider K is expressed as

T _a (k) Is a multiple set of task sequences

Is expressed as: p is a radical of _ak ＝p _ak (1),p _ak (2),…,p _ak (2|T _a (k) In which p) is _ak (l) For a task sequence p _ak Task corresponding to the first position, l =1,2, \ 8230;, 2Y ray T _a (k)|；

a ∈ Ω (T, K) is expressed as a task allocation scheme, T _a (k) Represented as a set of tasks, p, assigned to rider k _ak ∈P _a (k) Expressed as a sequence of tasks, then p _ak The distribution path is a path which starts from the position of the rider k and passes through p in sequence _ak Is represented by v, the sequence of merchant or customer nodes to which each task in (a) is directed _ak ＝v _ak (0),v _ak (1),…,v _ak (2|T _a (k) L) where v _ak (0) K, representing the position of the rider k, v _ak (l) Is represented by P _a (k) The node corresponding to the first task in (1) is l =1,2, \8230, 2 is the dominant strain T _a (k) Due to each task in the task sequenceTwo times of business appear-picking and delivering goods, the node corresponding to the task arranged in the front is specified as a merchant node, and the node corresponding to the task arranged in the back is specified as a customer node;

the distribution distance function is expressed as

Wherein p is _ak ∈P _a (k) Is a sequence of tasks, v _ak For the corresponding distribution route,

representing the rider k to the first node v _ak (1) K = v, k = v _ak (0)，

Denotes v _ak The sum of the distances between two adjacent nodes is 0 if the two adjacent nodes are the same node;

setting time constraints and load constraints: the time constraint function is expressed as H (p) _ak T) is ∈ { true, false }, where T is T _a (k) Denoted as a task, p _ak ∈P _a (k) Expressed as a sequence of tasks, if a task t follows p _ak The corresponding distribution route v in _ak The delivered goods meets the merchant's latest pick time and customer's latest delivery time constraints, then H (p) _ak T) = true, otherwise, H (p) _ak ,t)＝false；

The load constraint function is denoted as Q (p) _ak ) E { true, false }, where p _ak ∈P _a (k) Is a task sequence if the rider k follows the task sequence p _ak Corresponding distribution route v _ak The delivered cargo can meet its maximum load constraint, Q (p) _ak ) = true, otherwise Q (p) _ak )＝false；

Defining a crowdsourcing distribution task optimization scheduling model based on path planning as follows:

max∑ _k∈K |T _a (k)| (1)

min∑ _k∈K d(p _ak ) (2)

s.t.

Q(p _ak )＝true p _ak ∈P _a (k) (3)

H(p _ak )＝true p _ak ∈P _a (k) (4)

p _ak ∈P _a (k) a∈Ω(T,K),k∈K (5)

a∈Ω(T,K) (6)。

2. the optimal scheduling method for urban crowdsourcing distribution tasks based on path planning according to claim 1, wherein in the step 4, the specific method for solving the initial crowdsourcing task scheduling scheme based on the greedy strategy is as follows:

definition of T ⁰ _a (k) For the task set currently assigned to rider k, p ⁰ _ak For a task sequence of a rider k currently satisfying the constraint, T ∈ T — (U) _k∈K T ⁰ _a (k) For an unallocated task, if t is inserted into p ⁰ _ak The obtained new task sequence still satisfies the load constraint and the time constraint, and t is called p ⁰ _ak An extensible task of, T ¹ _a (k)＝T ⁰ _a (k) U { t } is a task set of the rider k after the insertion of the task t;

inserting a task t into a task sequence p ⁰ _ak There are two ways: adjacent and spaced; adjacent insertion refers to the insertion of a task t into p ⁰ _ak Then, two positions of the task t in the new task sequence are adjacent; spaced insertion refers to insertion of a task t into p ⁰ _ak Then, two positions of the task t in the new task sequence are not adjacent;

the method comprises the following specific steps:

step 1: according to the rider k _i Position of rider k is obtained _i Obtaining a shortest path length set D completed by all tasks which are not distributed with a distance set S of all business points which are not distributed with the tasks, adding the subscript values corresponding to the two sets, sequencing the two sets from small to large according to the addition and storing the two setsTo task assignment sequence T ⁰ _a (k _i )；

Step 2: at T ⁰ _a (k _i ) Selecting a first task t ₁ Add the task to rider k _i Task delivery sequence p of _a (k _i ) As a rider k _i The task state is set as the distributed state;

step 3: adding a second task, adding T ⁰ _a (k _i ) Each task t in ₂ ,t ₃ ,…,t _j Both add to p with adjacent and spaced insertion strategies _a (k _i ) In the first pass, p obtained if some kind of insertion strategy _a (k _i ) Satisfy t ₁ And t _j Time constraints of (1), rider k _i Is restricted, the task t is saved _j Task delivery sequence p added to the rider according to this insertion strategy _a (k _i ) After all the tasks are inserted into the sequence R, the sequence R is arranged in an ascending order, and the task t with the minimum path length _j As a second delivery task, add to p according to insertion position _a (k _i ) In the meantime, a new task is dispatched in the sequence p _a (k _i ) As a task delivery sequence to add the next task, t _j The state becomes delivered; if p is obtained by all insertion strategies _a (k _i ) All do not satisfy the constraint, then p _a (k _i ) To the final rider k _i The task distribution sequence of (1);

step 4: when the sequence R is empty, according to p _a (k _i ) Task relation graph acquisition rider k _i V of _aki While obtaining rider k _i Delivery of p _a (k _i ) Path length d (p) of _aki ) (ii) a Let t = t +1, return to Step 2;

step 5: when t = n _t And +1 or when the states of all the tasks are distributed, finishing the algorithm to obtain an initial solution set.

3. The method for optimally scheduling urban crowdsourcing distribution tasks based on path planning as claimed in claim 2, wherein in the step 5, the optimally scheduling the crowdsourcing distribution tasks based on variable neighborhood search comprises four neighborhood structures, which are respectively:

(1) Two riders are randomized, and tasks in the task sequences of the two riders are randomized and exchanged;

(2) Randomly exchanging merchant points of two tasks in a task sequence of a rider;

(3) Randomly exchanging client points of two tasks in a task sequence of a rider;

(4) Two riders are randomized, tasks in a task sequence of one rider are randomized and added in a task sequence of the other rider;

the method comprises the following specific steps:

step 1: obtaining an initial solution p for rider k _ak Let the optimal solution be p _{ak_best} ；

Step 2: defining a set of neighborhood structures N _s ，s＝1,2…,s _max As perturbation operation, where N _s The method comprises the steps of exchanging task operations of a rider, exchanging task sequence merchant point operations, exchanging task sequence customer point operations and transferring task operations of the rider;

step 3: defining a set of neighborhood structures N _q ，q＝1,2…,q _max As a neighborhood search, where N _q The method comprises the steps of exchanging rider task operation, exchanging task sequence merchant point operation, exchanging task sequence customer point operation and transferring rider task operation;

step 4: let s =1,q =1;

step 5: using N _s To p _ak Disturbing to generate solution p' _ak ；

Step 6: using N _q To p' _ak Search is carried out to generate a solution p' _ak ；

Step 7: if d (p' _ak )<d(p” _ak ) Then p' _ak ＝p” _ak Q =1; otherwise, q = q +1;

step 8: if q is<q _max Jumping to Step 6; otherwise, executing Step 9;

step 9: if d (p) " _ak )<d(p _ak ) Then p is _ak ＝p” _ak S =1; otherwise, s = s +1;

step 10: if s<s _max Jumping to Step 5; otherwise, p _{ak_best} ＝p” _ak Step 11 is executed;

step 11: inputting an optimal solution p _{ak_best} And the algorithm ends.

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