CN113469610B - Shortest path optimization method based on average dinner time of rider - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 22
- 238000005457 optimization Methods 0.000 title claims abstract description 19
- 239000011159 matrix material Substances 0.000 claims abstract description 70
- 235000012054 meals Nutrition 0.000 claims abstract description 21
- 238000013461 design Methods 0.000 claims description 3
- 238000012216 screening Methods 0.000 claims description 3
- 238000010276 construction Methods 0.000 claims 1
- 230000008901 benefit Effects 0.000 abstract description 5
- 230000009286 beneficial effect Effects 0.000 description 2
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- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
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Abstract
The invention discloses a shortest path optimization method based on average dinner time of a rider, which comprises the following steps: designing a time cost and punishment cost model; reading order information; setting constraint conditions, taking meals and delivering the meals; establishing a path matrix conforming to constraint conditions; reading waiting time historical data of a rider at a certain merchant; establishing an average waiting time matrix of a merchant; inquiring riding time among a rider, a merchant and a client; establishing a riding time matrix; utilizing a depth-first traversal algorithm; and recommending the obtained shortest path plan to a rider. According to the invention, by considering factors of average meal time of the rider, the shortest path is optimized, the punishment cost is reduced, the path recommendation system with the lowest cost when the rider rides is developed, the meal taking and delivering time of the rider is shortened, the overtime risk of orders is reduced, the benefit of the rider is increased, the benefit requirements of clients, the rider and a platform are met, and social resources are saved.
Description
Technical Field
The invention relates to the field of vehicle path optimization, in particular to a shortest path optimization method based on average dining time of a rider.
Background
The take-away delivery path optimization problem is subordinate to the vehicle path problem (Vehicle Routing Problem, VRP). In recent years, the problem of collaborative optimization of dispatch and path decision is classified into the problem of immediate delivery vehicle path with a time window; the problem of TSP is considered, and optimization is carried out by taking the shortest distribution path as a target; constructing a customer satisfaction model to optimize the path; the distribution time and the distribution cost are used as multiple constraints, and a high-end catering takeout distribution network path optimization model is built; and the problem of optimizing the distribution path of the mixed integer programming model is solved, and the ant colony algorithm is improved in design.
In the prior art, most of the researches use heuristic algorithms for path planning, but overtime orders are often encountered during take-out at daily points, and most of the reasons for overtime of the take-out orders are slow meal taking of merchants after investigation. Most of the existing research on takeaway delivery is focused on route optimization, and an important factor of meal delivery time of merchants is ignored.
Disclosure of Invention
The invention aims to: aiming at the problems, the invention provides a shortest path optimization method, which shortens the take-out and delivery time of takeaway riders, reduces the overtime risk of orders and increases the income of the riders.
The technical scheme is as follows: in order to achieve the purpose of the invention, the technical scheme adopted by the invention is as follows:
the shortest path optimization method based on average dinner time of a rider specifically comprises the following steps:
(1) Designing a time cost and punishment cost model;
(2) Reading merchant positions, customer positions and path sequences in order information;
(3) Setting constraint conditions, namely taking and delivering the meal firstly;
(4) Establishing a path matrix conforming to constraint conditions;
(5) Reading waiting time historical data of a rider at a certain merchant;
(6) Establishing an average waiting time matrix of a merchant;
(7) Inquiring riding time among a rider, a merchant and a client through a map API;
(8) Establishing a riding time matrix;
(9) Obtaining a path plan with the shortest time consumption and lowest punishment cost by using a depth-first traversal algorithm;
(10) And recommending the obtained shortest path plan to a rider.
Further, the design time cost and penalty cost model is as follows:
the shortest time cost is
Wherein i is the set of the rider, the merchant point and the customer point, j is the set of the merchant point and the customer point, n is the order number and t ij Time, w, required for points i to j j Wait time for point j; x is x ij As a decision variable, x when the rider travels from point i to point j ij =1, otherwise x ij ＝0；
The minimum penalty cost is
Wherein alpha is a punishment coefficient set by the platform, i is a client point of the order, T i For the current time of the rider at point i, l i For the overtime of the order, l is the maximum overtime of the order, and M is the maximum penalty cost set by the platform.
Further, a path matrix which meets constraint conditions, namely taking and delivering the meal firstly is established, and the method specifically comprises the following steps:
establishing a matrix of m, wherein m=2, n+1, n is the number of orders, and the rows i and the columns j of the matrix are the rider position, the merchant position and the customer position;
matrix element x ij Indicating whether or not the constraint condition is satisfied from the position i to the position j; the matrix element x is in accordance with the constraint condition ij 1, if the constraint is not satisfied, matrix element x ij Is 0.
Further, an average waiting time matrix of the merchant is established, specifically:
establishing a matrix of k, wherein k=2, n+1, n is the number of orders, and the rows i and the columns j of the matrix are the rider position, the merchant position and the customer position;
if the element of the ith row and jth column of the path matrix is 1, waiting for the element y of the time matrix ij The value is the average waiting time of the merchant, otherwise, y ij The value is 0.
Further, the establishment of the riding time matrix is specifically:
establishing a matrix with r=2xn+1, n being the number of orders, and rows i and columns j of the matrix being the rider position, the merchant position and the customer position, and matrix element z ij Is the riding time between position i and position j.
Further, a path plan with the shortest time consumption and the lowest punishment cost is obtained by using a depth-first traversal algorithm, which specifically comprises the following steps:
reading a path matrix, a merchant average equal meal time matrix and a riding time matrix, loading a time cost and punishment cost model, and loading constraint conditions;
then performing depth-first traversal, and simultaneously performing path screening according to the path matrix to remove paths with data of 0 in the path matrix;
and finally, calculating the time cost and the punishment cost of the screened path to obtain the path planning with the shortest time consumption and the lowest punishment cost.
The beneficial effects are that: compared with the prior art, the technical scheme of the invention has the following beneficial technical effects:
the method disclosed by the invention has the advantages that the meal taking route and the meal delivery route are comprehensively optimized, the important factor of the meal delivery time of a merchant is also taken into consideration, the meal taking and delivery route of takeaway riders can be better guided, the meal taking and delivery time of the riders is shortened, the overtime risk of orders is reduced, the benefit of the riders is increased, the benefit requirements of clients, the riders and a platform are met, and social resources are saved.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a flow chart of an embodiment.
Detailed Description
The technical scheme of the invention is further described below with reference to the accompanying drawings and examples.
The invention discloses a shortest path optimization method based on average dinner time of a rider, which is shown in fig. 1 and 2, and the specific implementation comprises the following steps:
(1) Designing a time cost and punishment cost model;
the shortest time cost is
Wherein i is the set of the rider, the merchant point and the customer point, j is the set of the merchant point and the customer point, n is the order number and t ij Time, w, required for points i to j j Wait time for point j; x is x ij As a decision variable, x when the rider travels from point i to point j ij =1, otherwise x ij ＝0；
The minimum penalty cost is
Wherein alpha is a punishment coefficient set by the platform, i is a client point of the order, T i For the current time of the rider at point i, l i For the overtime of the order, l is the maximum overtime of the order, and M is the maximum penalty cost set by the platform.
When the rider spends time T i Without exceeding the time-out time of the order/ i When the penalty cost is 0; when it takes time T i Exceeding the timeout period l i When the maximum timeout period l set by the platform is not exceeded, the penalty cost is alpha (T i -l i ) The method comprises the steps of carrying out a first treatment on the surface of the When it takes time T i When the maximum timeout period l is exceeded, the penalty cost is M.
(2) The merchant location, customer location, and path order in the order information are read.
(3) And setting constraint conditions according to the path sequence in order information, namely, the principle of taking and then delivering the meal is required to be followed.
(4) Establishing a path matrix conforming to constraint conditions; the method comprises the following steps:
establishing a matrix of m, wherein m=2, n+1, n is the number of orders, and the rows i and the columns j of the matrix are the rider position, the merchant position and the customer position; matrix element x ij Indicating whether or not the constraint condition is satisfied from the position i to the position j; the matrix element x is in accordance with the constraint condition ij 1, if the constraint is not satisfied, matrix element x ij Is 0.
When table 1 is two orders, the first order corresponds to merchant 1 and customer 101, and the second order corresponds to merchant 2 and customer 102. Because the rider must follow the principle of taking a meal before going, merchant 1 must visit before customer 101, the path for customer 101 to visit merchant 1 is not clear, the form is set to 0, and order 2 is the same. Since the rider does not need to return to the initial point, the first column is set to 0 entirely. The rider cannot access the customer point directly from the initial location without accessing the merchant point, so the last two data of the first row in the table is set to 0.
TABLE 1 whether the road between the points meets the constraint condition
| Rider 0 | Merchant 1 | Merchant 2 | Customer 101 | Customer 102 | |
| Rider 0 | 0 | 1 | 1 | 0 | 0 |
| Merchant 1 | 0 | 0 | 1 | 1 | 1 |
| Merchant 2 | 0 | 1 | 0 | 1 | 1 |
| Customer 101 | 0 | 0 | 1 | 0 | 1 |
| Customer 102 | 0 | 1 | 0 | 1 | 0 |
(5) And reading the waiting time historical data of the rider at a certain merchant.
And acquiring historical data of a time period that all the riders arrive at a store of a certain merchant on a certain platform, namely taking meals, and obtaining average waiting time of the riders at the store.
(6) Establishing an average waiting time matrix of a merchant; the method comprises the following steps:
establishing a matrix of k, wherein k=2, n+1, n is the number of orders, and the rows i and the columns j of the matrix are the rider position, the merchant position and the customer position; if the element of the ith row and jth column of the path matrix is 1, waiting for the element y of the time matrix ij The value is the average waiting time of the merchant, otherwise, y ij The value is 0.
Table 2 is the waiting time of each merchant when two orders are placed in the table. The average waiting time as for merchant 1 is: 5min, average waiting time for merchant 2 is: 2min.
Table 2 average waiting schedule for each merchant
| /min | Rider 0 | Merchant 1 | Merchant 2 | Customer 101 | Customer 102 |
| Rider 0 | 0 | 5 | 2 | 0 | 0 |
| Merchant 1 | 0 | 0 | 2 | 0 | 0 |
| Merchant 2 | 0 | 5 | 0 | 0 | 0 |
| Customer 101 | 0 | 0 | 2 | 0 | 0 |
| Customer 102 | 0 | 5 | 0 | 0 | 0 |
(7) Through the map API, the riding time between the rider, the merchant and the customer is queried.
(8) Establishing a riding time matrix; the method comprises the following steps:
establishing a matrix with r=2xn+1, n being the number of orders, and rows i and columns j of the matrix being the rider position, the merchant position and the customer position, and matrix element z ij Is the riding time between position i and position j.
Table 3 shows the riding time between the rider's point and the customer's point in the hundred degree map for two orders.
Table 3 riding time table between points
(9) Obtaining a path plan with the shortest time consumption and lowest punishment cost by using a depth-first traversal algorithm; the method comprises the following steps:
reading a path matrix, a merchant average equal meal time matrix and a riding time matrix, loading a time cost and punishment cost model, and loading constraint conditions;
then performing depth-first traversal, and simultaneously performing path screening according to the path matrix to remove paths with data of 0 in the path matrix;
and finally, calculating the time cost and the punishment cost of the screened path to obtain the path planning with the shortest time consumption and the lowest punishment cost.
(10) And recommending the obtained shortest path plan to a rider.
While the foregoing is directed to the preferred embodiments of the present invention, it should be noted that modifications and variations could be made by those skilled in the art without departing from the technical principles of the present invention, and such modifications and variations should also be regarded as being within the scope of the invention.
Claims (6)
1. A shortest path optimization method based on average dinner time of a rider is characterized in that: the method specifically comprises the following steps:
(1) Designing a time cost and punishment cost model;
(2) Reading merchant positions, customer positions and path sequences in order information;
(3) Setting constraint conditions, namely taking and delivering the meal firstly;
(4) Establishing a path matrix conforming to constraint conditions;
(5) Reading waiting time historical data of a rider at a certain merchant;
(6) Establishing an average waiting time matrix of a merchant;
(7) Inquiring riding time among a rider, a merchant and a client through a map API;
(8) Establishing a riding time matrix;
(9) Obtaining a path plan with the shortest time consumption and lowest punishment cost by using a depth-first traversal algorithm;
(10) And recommending the obtained shortest path plan to a rider.
2. The shortest path optimization method according to claim 1, characterized in that: the design time cost and penalty cost model is as follows:
the shortest time cost is
Wherein i is the set of the rider, the merchant point and the customer point, j is the set of the merchant point and the customer point, n is the order number and t ij Time, w, required for points i to j j Wait time for point j; x is x ij As a decision variable, x when the rider travels from point i to point j ij =1, otherwise x ij ＝0；
The minimum penalty cost is
Wherein alpha is a punishment coefficient set by the platform, i is a client point of the order, T i For the current time of the rider at point i, l i For the overtime of the order, l is the maximum overtime of the order, and M is the maximum penalty cost set by the platform.
3. The shortest path optimization method according to claim 1, characterized in that: establishing a path matrix which accords with constraint conditions, namely taking and delivering the meal firstly, specifically:
establishing a matrix of m, wherein m=2, n+1, n is the number of orders, and the rows i and the columns j of the matrix are the rider position, the merchant position and the customer position;
matrix element x ij Indicating whether or not the constraint condition is satisfied from the position i to the position j; the matrix element x is in accordance with the constraint condition ij 1, if the constraint is not satisfied, matrix element x ij Is 0.
4. A shortest path optimization method according to claim 3, characterized in that: establishing an average waiting time matrix of a merchant, wherein the average waiting time matrix specifically comprises the following steps:
establishing a matrix of k, wherein k=2, n+1, n is the number of orders, and the rows i and the columns j of the matrix are the rider position, the merchant position and the customer position;
if the element of the ith row and jth column of the path matrix is 1, waiting for the element y of the time matrix ij The value is the average waiting time of the merchant, otherwise, y ij The value is 0.
5. The shortest path optimization method according to claim 1, characterized in that: the construction of the riding time matrix is specifically as follows:
establishing a matrix with r=2xn+1, n being the number of orders, and rows i and columns j of the matrix being the rider position, the merchant position and the customer position, and matrix element z ij Is the riding time between position i and position j.
6. The shortest path optimization method according to any one of claims 1 to 5, characterized in that: the depth-first traversal algorithm is utilized to obtain the path planning with the shortest time consumption and the lowest punishment cost, and the method specifically comprises the following steps:
reading a path matrix, a merchant average equal meal time matrix and a riding time matrix, loading a time cost and punishment cost model, and loading constraint conditions;
then performing depth-first traversal, and simultaneously performing path screening according to the path matrix to remove paths with data of 0 in the path matrix;
and finally, calculating the time cost and the punishment cost of the screened path to obtain the path planning with the shortest time consumption and the lowest punishment cost.
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