CN108985597B - Dynamic logistics scheduling method - Google Patents
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Abstract
The invention discloses a dynamic logistics scheduling method. The invention aims to respond to a dynamic logistics request in real time, update the real-time dynamic logistics request into the existing logistics distribution route, and reform the optimal vehicle distribution route so as to optimize the preset target. The scheduling method can quickly respond to dynamic requests of newly added orders and requests of canceling orders, and in order to respond to the requests of the newly added orders, a real-time insertion algorithm is introduced to carry out insertion calculation of the newly added orders; in order to respond to the order cancellation request, the invention optimizes the processing scheme of the order cancellation request under different conditions. In order to efficiently and quickly respond to the dynamic logistics request, the fitting degree of the newly added order and the distribution route is introduced, the higher the fitting degree is, the higher the feasibility of adding the newly added order request to the distribution route is, and the calculation cost of selecting the distribution route by the newly added order can be greatly reduced after the fitting degree is introduced.
Description
Technical Field
The invention belongs to the field of logistics scheduling, and particularly relates to a dynamic logistics request scheduling method and a real-time insertion method.
Background
Under the great push of economic development, logistics becomes an important component of enterprise management now, not only because logistics occupies a higher proportion in the total enterprise cost, but also because logistics activity influences the service level of the enterprise, excellent logistics service directly influences the loyalty of customers to the enterprise, and therefore, lowering logistics consumption cost and improving logistics operation efficiency become one of the well-known important ways capable of effectively improving enterprise competitiveness. In the prior logistics distribution activities, enterprises perform static distribution and distribution on batch orders, and along with the characteristics of rapid popularization of communication means of smart phones and convenience of shopping by merchants in communication networks of China, the logistics request sent by people at any time and any place becomes a normal state. In the face of such real-time dynamic logistics requests, the enterprise logistics distribution service is required to have the capability of fast response and to process the dynamic logistics requests as soon as possible. This rapid response capability has become one of the core competencies of the enterprise.
In the logistics distribution in transit, to weight, the great goods of volume, if when unloading, the goods is in the carriage is outmost just, then need not to carry out extra goods moving cost, has reduced the handling time in the logistics distribution, very big improvement logistics distribution experience. At present, few researches on the limitation conditions of the goods in the order in advance and the limitation conditions of the goods in the order in dynamic logistics request scheduling at home and abroad are considered, and a plurality of defects of overlong dynamic scheduling solving time and the like exist.
Disclosure of Invention
In order to respond to a dynamic logistics request more efficiently and ensure that ordered goods follow a first-in last-out limiting condition, the invention provides a dynamic logistics scheduling method.
The purpose of the invention is realized by at least one of the following technical solutions.
A dynamic logistics scheduling method is characterized in that a dynamic logistics request is responded in real time, the real-time dynamic logistics request is updated to an existing logistics distribution path, an optimal vehicle distribution path is formed again, and the difference value between the updated new path cost and the original path cost is the minimum.
Further, the real-time dynamic logistics request comprises a newly added order request and a withdrawn order request.
Further, the order corresponding to each dynamic request comprises the volume, weight and type of goods; each dynamic request comprises a pick request and a delivery request; the goods taking request corresponds to a goods taking address, the goods delivery request corresponds to a goods delivery address, and the goods taking address and the goods delivery address belong to different addresses.
Further, for dynamically requested pick requests and delivery requests, the pick action must occur before the delivery action.
Further, for processing a new order request, a real-time insertion algorithm is introduced to insert the order into an existing delivery path, and the real-time insertion algorithm comprises the following steps:
(1) respectively generating a goods taking task and a goods delivering task according to the new order request and the goods delivering request;
(2) for the two newly generated tasks in the step (1), introducing fitting degree to calculate the fitting degree of a connecting line between addresses of the two tasks and the existing distribution route;
(3) sequentially selecting a distribution route with the highest fitting degree with the newly-added order, introducing a greedy insertion algorithm to insert the two tasks in the step (1) into the distribution plan route until a route with the minimum newly-added cost is found;
(4) and updating the distribution route.
Further, the greedy insertion algorithm includes:
1) r represents a planned driving route of the vehicle, and the directed line segment PD is a straight line segment determined by a goods taking task point and a goods delivery task point of a newly added order demand X; the points closest to the points P and D are P 'and D', the change of the running distance before and after inserting the point P into the point P 'is compared, if the total running distance before inserting the point P' is smaller than the total running distance after inserting the point P ', the point P is inserted before the point P', whether the load and the volume of the vehicle after inserting the point P and the limitation condition of the first-in last-out of the vehicle order are met or not is checked, if the load and the volume of the vehicle after inserting the point P and the limitation condition of the first-in last-out of the vehicle order are not met, the point P is continuously shifted and inserted into the path R until all logistics distribution sequences meeting the conditions are found, and the point P can be inserted into the sequence points; otherwise, inserting the P into the P', and sequentially searching the logistics distribution sequence meeting the vehicle load, volume and first-in and last-out limiting conditions backwards as described above until a certain sequence is found, wherein all limiting conditions can be met, and then completing greedy insertion of the P point;
2) comparing the running distance change before and after the D is inserted into the D ', if the total running distance before the D' is inserted into the route is smaller than the total running distance after the D 'is inserted into the route, inserting the D into the route before the D', checking whether the first-in and last-out limiting conditions of each order on the vehicle after the D is inserted into the route are met and whether the P point is before the D point, and if the first-in and last-out limiting conditions of each order cannot be met but the P point is before the D point, continuously inserting the D point into the route; if the point P is not satisfied before the point D, the point D must be inserted into the path backwards until the limitation condition that each order is input first and then output is satisfied and the point P is before the point D; otherwise, after the D point is inserted into the D ', whether the two limiting conditions are met or not is checked, if both the two limiting conditions are met, the greedy insertion process of the D point is ended, and otherwise, the D' is continuously inserted into the path backwards until the two limiting conditions are met.
Further, for the dynamic logistics request for canceling the original order request, the order is directly deleted from the distribution route to which the order belongs, and then a tabu search algorithm is introduced to perform re-optimization calculation on the distribution route from which the order is deleted.
Further, the tabu search algorithm is executed as follows:
1) calculating a route travel distance cost value f (R) for the route R;
2) initializing a path time distance matrix M, a tabu table T and a historical optimal path cost best _ f;
3) judging whether the termination condition is met or not and the execution is not continued, and stopping if the termination condition is met;
4) constructing a neighborhood of R, which is represented by N (S);
5) calculating the cost values of all paths in the fields N (S);
6) finding the best contraindication or non-contraindication move with the smallest path cost value;
7) if the desire conditions are met, executing the optimal contraindication movement, otherwise executing the optimal non-contraindication movement;
8) updating a current path, a path cost value f, a historical optimal path cost best _ f and a tabu table;
9) and (5) executing the step (3).
Further, the updated delivery plan route must satisfy the limitation of the advance-after-exit of the goods, which means that the goods of the order must be at the outermost layer of the vehicle compartment when the delivery request of the order is serviced.
Further, the updated delivery plan must ensure that the vehicle's weight and volume limitations are guaranteed.
Compared with the prior art, the invention has the following advantages and technical effects: for the newly added order requests, the newly added orders are added into the existing distribution path through a real-time insertion algorithm; for the order cancellation request, a method of directly deleting two task nodes corresponding to the order from the distribution route is adopted, and then a taboo search algorithm is utilized to re-optimize the distribution route after the two task nodes corresponding to the order are deleted; in order to efficiently and quickly respond to dynamic logistics requests, for a real-time insertion algorithm, the fitting degree of newly added orders and a distribution route is introduced, and the fitting degree is used for estimating the probability that the newly added order requests are added to a certain route. The greater the fitting degree is, the higher the feasibility that the newly added order request is added to the distribution route is, and after the fitting degree is introduced, the calculation cost of selecting the distribution route by the newly added order can be greatly reduced.
Drawings
Fig. 1 is a flow chart of dynamic logistics scheduling.
FIG. 2 is a diagram of a new order request versus a vehicle delivery route.
FIG. 3 is a schematic diagram of whether a vehicle delivery route meets a first-in-first-out constraint.
Detailed Description
In order to make the technical solutions and advantages of the present invention more apparent, the following detailed description is made with reference to the accompanying drawings, but the present invention is not limited thereto.
1. Dynamic logistics request handling
Fig. 1 is a flow chart of dynamic logistics scheduling, wherein the detailed processing method for the new order request and the cancel order request is as follows:
1.1 processing method of newly-added order request
During the logistics distribution service of the enterprise, the dispatching center receives a new order request, and the enterprise has two options at the moment. The first option is to accept the order; the second is to choose to reject the order. The invention mainly considers the scheduling method under the first condition, and the enterprise logistics scheduling center has two choices at the moment, wherein the first choice is to select a vehicle without any delivery task to serve a newly-added order; the second option is to consider adding new orders to an existing delivery plan to save costs, and if the second option fails to meet the demand, then consider the first option.
Fig. 1 illustrates the relationship between the newly added order request and an existing delivery planned route, a directed line segment PD is a straight line segment determined by a pick-up task point and a delivery task point of a newly added order request X, a directed curve R represents a planned driving route of a vehicle, and a middle node in the curve R represents a passing point of the vehicle. To calculate the degree of fit of the line segment PD to the curve R, the following steps are carried out: 1) respectively finding out points P ' and D ' which are the closest to the same point P and D from each path point set of the curve R, and defining a straight line determined by the point P ' D ' as R '; assuming that P ' and D ' are coincident, any one point of P and D is selected, and one next closest point is reselected as a new closest point, for example, the next closest point of P is selected as P '; 2) defining an included angle between PD and R' as alpha, wherein alpha belongs to [0, pi ], and then calculating the fitting degree of the newly added order demand X and the planned driving route of the vehicle as follows:
wherein P '-P | and D' -D | are the distance between two points, C>0 is a constant value, and C is usually taken as the radius of the map area space. Obviously, the larger the "average distance" between the line segment PD and P 'D', the smaller the included angle α, and FxThe larger. When F is presentxWhen approaching 1, the more the newly added order is fitted with the planned driving route of the vehicle, and the smaller the newly added order is. Fig. 1 only describes the calculation of the fitting degree of the newly added order request with a certain route, and the method and the calculation formula are the same as those of the calculation of the fitting degree of other distribution routes in the same city of an enterprise with the newly added order request.
And after the fitting degrees of the newly added orders and all the distribution routes are obtained, inserting the two tasks generated by the newly added orders into the distribution routes by utilizing a greedy insertion algorithm according to the fitting degrees from high to low. The implementation of the greedy insertion algorithm is as follows:
3) as shown in fig. 2, the travel distance changes before and after inserting P into P 'are compared, if the total travel distance before inserting P' is less than the total travel distance after inserting P ', P is inserted before P', and whether the load and volume of the vehicle after inserting P and the first-in and last-out limiting conditions of each order on the vehicle are satisfied are checked, if not, the P point is continuously shifted and inserted into the path R until all the logistics distribution sequences with the satisfied conditions are found, and P can be inserted into the sequence points. Otherwise, inserting P into P', and sequentially searching backwards for logistics distribution sequences meeting the vehicle load, volume and first-in and last-out limiting conditions as described above until a certain sequence is found, wherein all limiting conditions can be met, and then completing greedy insertion of the P point.
4) As shown in fig. 2, the travel distance changes before and after D is inserted into D ' are compared, if the total travel distance before D ' is inserted into D ' is less than the total travel distance after D ' is inserted into D ', D is inserted into D ', whether the first-in last-out limiting condition of each order on the vehicle after D is inserted into D ' is satisfied and whether P is before D is checked, and if the first-in last-out limiting condition of each order on the vehicle after D is not satisfied but P is before D is satisfied, D can be inserted into the path ahead; if P is not satisfied before D, then D must be inserted back into the path until both the order-in-first-out constraints are satisfied and P is before D. Otherwise, after the D point is inserted into the D ', whether the two limiting conditions are met or not is checked, if both the two limiting conditions are met, the greedy insertion process of the D point is ended, and otherwise, the D' is continuously inserted into the path backwards until the two limiting conditions are met.
In the process of executing the greedy insertion algorithm, it is necessary to satisfy a certain distribution route in which a distribution route satisfies a point-in-point-out-of-point restriction condition of goods, fig. 3 shows a certain distribution route satisfying a point-in-point-out-of-point restriction condition and a point-in-point-out-of-point-out restriction condition, and the left side of fig. 3 describes that the point-in-point-out restriction condition is satisfied, it can be found that, in this distribution sequence, goods of a task 2 need to be transported first to get off to complete the unloading of goods of the order 1 and then goods of the task 2 need to be transported to get on to serve the delivery task of the order 1, thereby bringing additional transportation cost. Therefore, the limitation of advance and exit of the goods is met, extra carrying cost can be greatly reduced, and logistics service experience is improved.
If the algorithm fails to satisfy the processing of the new order request, the dispatch center dispatches a new vehicle to service the dynamic request.
1.2 order cancellation processing method
When the dispatching center receives the order withdrawal request, there are two cases, one is that the order picking task is completed, and the second is that the order picking task is not completed.
For the first case, the order cannot be cancelled, but it can be converted into a new order request whose pick task is the delivery task of the original cancelled order and whose delivery task is the pick task of the original cancelled order. The distribution task will continue to distribute, but the newly added order request refers to the method of the newly added order request in 1.1, and when it needs to be explained, the service vehicle requested by the newly added order request is not necessarily the service vehicle that has cancelled the original order request, and specifically which vehicle to distribute is determined by the algorithm in 1.1.
In the second case, after the two pick-and-send tasks to which the order belongs are deleted from the delivery path, the delivery path from which the order is deleted needs to be re-optimized. A tabu search algorithm is adopted to optimize the path, and the execution process of the tabu search algorithm is as follows:
1) for path R, calculating a path cost value f (R);
2) initializing a path time distance matrix M, a tabu table T and a historical optimal path cost best _ f;
3) judging whether the iteration times of the tabu search algorithm are larger than or equal to the set maximum iteration times, if not, continuing to execute, and if so, stopping executing the algorithm;
4) constructing a neighborhood of R, which is represented by N (S);
5) calculating the cost values of all paths in the fields N (S);
6) finding the best contraindication or non-contraindication move with the smallest path cost value;
7) if the desire conditions are met, executing the optimal contraindication movement, otherwise executing the optimal non-contraindication movement;
8) updating a current path, a path cost value f, a historical optimal path cost best _ f and a tabu table;
9) executing the step (3);
wherein f (R) is the cost of the driving distance of the path, the depth of the tabu table T is 5, the desirability condition means that the optimal solutions in the fields N (S) are all inferior to the optimal solutions in the tabu table, if the condition is met, tabu movement is executed, otherwise, non-tabu movement is executed, and the field construction algorithm of R comprises the following steps:
1) and randomly selecting a pick-up task and a corresponding delivery task of an order from the path R, and then reinserting the pick-up task and the corresponding delivery task into the path R, wherein the inserted new path needs to meet the load and volume of the vehicle and the first-in last-out limiting conditions of each order.
2) If the newly generated path is not in the domain, it is added to the domain and step 1) is repeated 10 times.
Claims (7)
1. A dynamic logistics scheduling method is characterized in that a dynamic logistics request is responded in real time, the real-time dynamic logistics request is updated to an existing logistics distribution path, an optimal vehicle distribution path is formed again, and the difference value between the updated new path cost and the original path cost is minimum; the real-time dynamic logistics request comprises a new order request and an order cancellation request, and the path cost is the vehicle driving distance;
for the processing of a newly added order request, a real-time insertion algorithm is introduced to insert the order into the existing distribution path, and the real-time insertion algorithm comprises the following steps:
(1) respectively generating a goods taking task and a goods delivering task according to the new order request and the goods delivering request;
(2) for the two newly generated tasks in the step (1), introducing fitting degree to calculate the fitting degree of a connecting line between addresses of the two tasks and the existing distribution route;
(3) sequentially selecting a distribution route with the highest fitting degree with the newly-added order, introducing a greedy insertion algorithm to insert the two tasks in the step (1) into the distribution plan route until a route with the minimum newly-added cost is found;
(4) updating a distribution route;
the greedy insertion algorithm includes:
1) r represents a planned driving route of the vehicle, and the directed line segment PD is a straight line segment determined by a goods taking task point and a goods delivery task point of a newly added order demand X; the points closest to the points P and D are P 'and D', the change of the running distance before and after inserting the point P into the point P 'is compared, if the total running distance before inserting the point P' is smaller than the total running distance after inserting the point P ', the point P is inserted before the point P', whether the load and the volume of the vehicle after inserting the point P and the limitation condition of the first-in last-out of the vehicle order are met or not is checked, if the load and the volume of the vehicle after inserting the point P and the limitation condition of the first-in last-out of the vehicle order are not met, the point P is continuously shifted and inserted into the path R until all logistics distribution sequences meeting the conditions are found, and the point P can be inserted into the sequence points; otherwise, inserting the P into the P', and sequentially searching the logistics distribution sequence meeting the vehicle load, volume and first-in and last-out limiting conditions backwards as described above until a certain sequence is found, wherein all limiting conditions can be met, and then completing greedy insertion of the P point;
2) comparing the running distance change before and after the D is inserted into the D ', if the total running distance before the D' is inserted into the route is smaller than the total running distance after the D 'is inserted into the route, inserting the D into the route before the D', checking whether the first-in and last-out limiting conditions of each order on the vehicle after the D is inserted into the route are met and whether the P point is before the D point, and if the first-in and last-out limiting conditions of each order cannot be met but the P point is before the D point, continuously inserting the D point into the route; if the point P is not satisfied before the point D, the point D must be inserted into the path backwards until the limitation condition that each order is input first and then output is satisfied and the point P is before the point D; otherwise, after the D point is inserted into the D ', whether the two limiting conditions are met or not is checked, if both the two limiting conditions are met, the greedy insertion process of the D point is ended, otherwise, the D' is continuously inserted into the path backwards until the two limiting conditions are met;
defining the included angle between PD and R' as alpha, alpha belongs to [0, pi ]]And then calculating the fitting degree F of the newly added order demand X and the planned driving route of the vehiclexComprises the following steps:
wherein P '-P | and D' -D | are the distance between two points, C>0 is a constant value, and C is usually taken as the radius of the map area space; obviously, the larger the "average distance" between the line segment PD and P 'D', the smaller the included angle α, and FxThe larger; when F is presentxWhen approaching 1, the more the newly added order is fitted with the planned driving route of the vehicle, and the smaller the newly added order is.
2. The dynamic logistics scheduling method of claim 1, wherein the order corresponding to each dynamic request comprises the volume, weight and type of goods; each dynamic request comprises a pick request and a delivery request; the goods taking request corresponds to a goods taking address, the goods delivery request corresponds to a goods delivery address, and the goods taking address and the goods delivery address belong to different addresses.
3. The dynamic logistics scheduling method of claim 2, wherein for the dynamically requested pick request and delivery request, the pick action must occur before the delivery action.
4. The dynamic logistics scheduling method of claim 3, wherein for a dynamic logistics request for canceling an original order request, an order is directly deleted from a distribution route to which the order belongs, and then a tabu search algorithm is introduced to perform re-optimization calculation on the distribution route from which the order is deleted.
5. The dynamic logistics scheduling method of claim 1, wherein the tabu search algorithm is executed as follows:
1) calculating a route travel distance cost value f (R) for the route R;
2) initializing a path time distance matrix M, a tabu table T and a historical optimal path cost best _ f;
3) judging whether the termination condition is met or not and the execution is not continued, and stopping if the termination condition is met;
4) constructing a neighborhood of R, which is represented by N (S);
5) calculating the cost values of all paths in the fields N (S);
6) finding the best contraindication or non-contraindication move with the smallest path cost value;
7) if the desire conditions are met, executing the optimal contraindication movement, otherwise executing the optimal non-contraindication movement;
8) updating a current path, a path cost value f, a historical optimal path cost best _ f and a tabu table;
9) and (5) executing the step (3).
6. A dynamic logistics scheduling method as claimed in claim 1 wherein the updated delivery plan route must satisfy a first in last out limitation of the goods, the first in last out limitation of the goods being that the goods of the order must be on the outermost compartment of the vehicle when servicing the delivery request of the order.
7. A dynamic logistics scheduling method as claimed in any one of claims 1 to 6 wherein the updated delivery plan ensures that the load and volume limits of the vehicle are guaranteed.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104616490A (en) * | 2015-02-17 | 2015-05-13 | 北京九五智驾信息技术股份有限公司 | Car sharing method |
| CN105046338A (en) * | 2015-08-24 | 2015-11-11 | 扬州大学 | Intelligent express distribution method on mobile platform |
| CN107145971A (en) * | 2017-04-18 | 2017-09-08 | 苏州工业职业技术学院 | A kind of express delivery dispatching optimization method of dynamic adjustment |
| CN107194575A (en) * | 2017-05-19 | 2017-09-22 | 重庆大学 | It is a kind of to handle the vehicle autonomy dispatching method that express delivery increases pickup demand newly |
| CN107194513A (en) * | 2017-05-26 | 2017-09-22 | 中南大学 | A kind of optimization method for solving full channel logistics distribution |
-
2018
- 2018-06-29 CN CN201810713626.5A patent/CN108985597B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104616490A (en) * | 2015-02-17 | 2015-05-13 | 北京九五智驾信息技术股份有限公司 | Car sharing method |
| CN105046338A (en) * | 2015-08-24 | 2015-11-11 | 扬州大学 | Intelligent express distribution method on mobile platform |
| CN107145971A (en) * | 2017-04-18 | 2017-09-08 | 苏州工业职业技术学院 | A kind of express delivery dispatching optimization method of dynamic adjustment |
| CN107194575A (en) * | 2017-05-19 | 2017-09-22 | 重庆大学 | It is a kind of to handle the vehicle autonomy dispatching method that express delivery increases pickup demand newly |
| CN107194513A (en) * | 2017-05-26 | 2017-09-22 | 中南大学 | A kind of optimization method for solving full channel logistics distribution |
Non-Patent Citations (1)
| Title |
|---|
| 基于互联网的同城配送车货匹配模式及路径优化研究;刘丹霞;《中国优秀硕士学位论文全文数据库》;20170715;全文 * |
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