CN111985790A - Order distribution method and system for delivery vehicles - Google Patents

Order distribution method and system for delivery vehicles Download PDF

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CN111985790A
CN111985790A CN202010750769.0A CN202010750769A CN111985790A CN 111985790 A CN111985790 A CN 111985790A CN 202010750769 A CN202010750769 A CN 202010750769A CN 111985790 A CN111985790 A CN 111985790A
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order
time period
time
user
carrying
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CN111985790B (en
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邓承志
高代轩
孙利强
王珊
韩真真
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China Foreign Transport Co ltd
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Abstract

The order distribution method and the order distribution system for the delivery vehicle provided by the embodiment of the invention comprise the following steps: acquiring all user orders on the current day, wherein each user order contains pickup and discharge time window information; dividing the total working time period of the day into a plurality of continuous time periods according to all the lifting and unloading time window information; based on meta heuristic algorithm, all user orders are clustered according to time periods, and order lines of each time period are output. The order distribution method and the order distribution system for the delivery vehicles provided by the embodiment of the invention utilize the meta-heuristic algorithm, dynamically set and adjust the order quantity in each time period through the order information of the user, obviously improve the wire arranging efficiency and ensure that the line time arrangement is more accurate and reasonable. The requirement of a user on the order delivery accuracy is met, meanwhile, the large idle time on a line is avoided, and the requirements of vehicle group personnel on the working saturation and the balance are considered.

Description

Order distribution method and system for delivery vehicles
Technical Field
The embodiment of the invention relates to the technical field of intelligent logistics, in particular to an order distribution method and system for a delivery vehicle.
Background
With respect to vehicle routing issues (VRPs), in practice, a number of constraints typically need to be considered, including: lifting and unloading time windows, vehicle loading capacity, maximum driving distance, and loaded commodity type limitation. In the shipping industry, such as the home shipping industry, the constraint of the pickup and discharge time window is directly related to the user experience, and the quality of service depends greatly on whether the goods can be delivered on time.
The general VRP algorithm only supports simple time window constraints, but cannot equalize the work intensity between periods. Although the order can meet the reservation time requirement, the balance of work intensity of the crew of the train set cannot be guaranteed, the workload of the crew of the train set is often over-saturated in a certain time period of a day and is not saturated in another time period, and the acceptance of the crew of the train set on the line is low. Meanwhile, due to uncertainty of estimated lifting and unloading time and estimated installation time, subsequent orders cannot be completed on time due to delay of a certain order in a supersaturation period, and user experience is influenced.
Conventional algorithms based on linear, integer or mixed integer programming have difficulty quantifying and adding the workload of "balancing" consist crew to the objective function, resulting in an inability to significantly increase the solution time. However, in the conventional algorithm based on meta-heuristic and large-scale neighborhood search, due to limitations in operator and switching logic design, adding a target of "equalization" to a large-scale actual service scene causes the quality of a solution to be significantly reduced, even to be completely unavailable.
In view of the above, it is desirable to provide a more effective order allocation method, which can satisfy the requirement of line load balance on the premise of satisfying the on-time delivery and completion of the order.
Disclosure of Invention
The embodiment of the invention provides an order distribution method and system for a delivery vehicle, which are used for overcoming or partially solving the defects of low operation efficiency, unreasonable distribution and the like in order distribution of the delivery vehicle in the prior art.
In a first aspect, an embodiment of the present invention provides an order allocation method for a delivery vehicle, which mainly includes: acquiring all user orders on the current day, wherein each user order contains pickup and discharge time window information; dividing the total working time period of the day into a plurality of continuous time periods according to all the lifting and unloading time window information; based on meta heuristic algorithm, all user orders are clustered according to time periods, and order lines of each time period are output.
Optionally, in the process of clustering all user orders according to a time period based on a meta heuristic algorithm, the method may further include: and dynamically setting the constraint conditions of the carrying orders in each time period according to the lifting and unloading time window information and the predicted service duration contained in the user order.
Optionally, the constraint condition of the carrying unit quantity may include: any user order is sent within the corresponding lifting time window and the installation is completed within the expected service duration.
Optionally, the order allocation method for the delivery vehicle according to the embodiment of the present invention further includes: determining the upper limit of the carrying order quantity in each time period according to the total number of the vehicles to be loaded and the information of the user order; the constraint condition for carrying the single quantity further comprises: the amount of the carrying single allocated in each time period does not exceed the upper limit of the carrying single.
Optionally, the constraint condition of the carrying unit quantity may further include: the total effective working time of the user orders distributed in each time period is less than the total duration of the time period; wherein the total effective working time includes an expected service time period and an expected traveling time of the delivery vehicle.
Optionally, the constraint condition of the carrying unit quantity may further include: and under the condition that the carrying order quantity allocated in the previous time period does not reach the preset saturation degree, the user order is not allocated to the next time period.
Optionally, the clustering all the user orders according to a time period based on the meta heuristic algorithm mainly includes: differentiating the amount of the carrying unit allocated to each time segment from the upper limit of the amount of the carrying unit in the time segment; multiplying the square of the difference by a penalty coefficient to obtain a penalty value for carrying the single quantity in the time period; calculating the minimum value of the sum of penalty values corresponding to each time period; setting a function for calculating the minimum value of the sum of the penalty values as a penalty function related to the carrying single quantity in each time period; based on a simulated annealing algorithm, clustering all user orders according to the time period in an iterative clustering mode; in each clustering process, balancing the weight of the carrying single quantity in each time period in the overall target function of the line through a penalty function; and if the clustering result is converged, acquiring the order line of each time period.
In a second aspect, an embodiment of the present invention provides an order distribution system for a delivery vehicle, mainly including: the system comprises an order data collection unit, a time period planning unit and an order distribution unit. Wherein: the order data collection unit is mainly used for acquiring all user orders on the day, and each user order comprises pickup and discharge time window information; the time period planning unit is mainly used for determining a plurality of continuous time periods according to the lifting and unloading time window information; the order distribution unit is mainly used for clustering all user orders according to the time periods based on the meta-heuristic algorithm and outputting order lines of each time period.
In a third aspect, an embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the order allocation method for a delivery vehicle according to any one of the first aspect when executing the program.
In a fourth aspect, embodiments of the present invention provide a non-transitory computer readable storage medium, on which a computer program is stored, the computer program, when executed by a processor, implementing the steps of the order allocation method for a delivery vehicle according to any one of the first aspect.
The order distribution method and the order distribution system for the delivery vehicles provided by the embodiment of the invention utilize the meta-heuristic algorithm, dynamically set and adjust the order quantity in each time period through the order information of the user, obviously improve the wire arranging efficiency and ensure that the line time arrangement is more accurate and reasonable. The requirement of a user on the order delivery accuracy is met, meanwhile, the large idle time on a line is avoided, and the requirements of vehicle group personnel on the working saturation and the balance are considered.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart illustrating an order allocation method for delivery vehicles according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an order distribution system for a delivery vehicle according to an embodiment of the present invention;
fig. 3 is a physical structure diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. 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 protection scope of the present invention.
With respect to vehicle routing issues (VRPs), in practice, a number of constraints typically need to be considered, including: lifting and unloading time windows, vehicle loading capacity, maximum driving distance, and loaded commodity type limitation. In the shipping industry, such as the home shipping industry, the constraint of the pickup and discharge time window is directly related to the user experience, and the quality of service depends greatly on whether the goods can be delivered on time. At the same time, the line saturation determines the line acceptance of crew since driver and assembler income is often linked to the effective operating time of the day (depending on the type and quantity of goods dispensed). The demands of train crew and users can be met in time arrangement of the line, the train crew can work at a higher saturation degree, unnecessary idle time is reduced, the commodity can be delivered on time, the installation is completed, and the influence on user experience is avoided.
In view of the above, an embodiment of the present invention provides an order allocation method for a delivery vehicle, as shown in fig. 1, including, but not limited to, the following steps:
step S1, all user orders on the current day are obtained, and each user order contains pickup and discharge time window information;
step S2, dividing the total working time period of the day into a plurality of continuous time periods according to all the lifting and unloading time window information;
and step S3, clustering all user orders according to the time periods based on the meta-heuristic algorithm, and outputting order lines of each time period.
In the prior art, a heuristic algorithm, including an annealing algorithm, a genetic algorithm, an ant colony algorithm, a tabu algorithm and the like, is used for solving the VRP problem, because clustering of orders according to time periods cannot be realized, and because of limitations in operator and exchange logic design, under a large-scale actual delivery service scene, an objective function for balancing each time period is added, so that the quality of a solution is remarkably reduced, and even the solution is not converged completely.
The embodiment of the invention aims to provide an algorithm for determining the carrying single quantity of the vehicles carried by the home in different time periods so as to meet the requirements of order on-time delivery and installation completion and give consideration to the requirement of carrying single quantity balance of the lines in different time periods.
Specifically, in step S1, all user orders for the day are first obtained before order distribution is performed each day. The method for collecting all user orders may be derived from an existing order management system, and this embodiment is not particularly limited.
Since each user order includes the pickup time window information, the pickup time window information (including the travel time, the loading and unloading time, the installation time, and the like) can be determined based on the pickup start time of the pickup goods, the loading specific location, the type of the loaded goods, the weight and the volume of the loaded goods, and the like of each user order.
Further, according to the lifting and unloading time window information of each order, the total time consumption of each user order can be determined, and accordingly, the distribution condition of each user order at each moment of the loading day can be counted. When the total working time period of the day is divided, different time periods can be divided according to the distribution condition of each time.
Optionally, the total working time period of the day may also be divided according to business habits and rules, and the embodiment of the present invention does not limit the specific dividing manner and the number of the time periods, for example, the total working time period of the day may be determined as 3 time periods, which are 09:00-13:00 in the morning and 13:00-17 in the afternoon: 00 and 17:00-21:00 at night.
Further, after the division of the middle working time period is completed, based on the meta heuristic algorithm, clustering operation is performed on all user orders in different time periods, so as to obtain an ideal order distribution mode in each time period.
Wherein, Meta-Heuristic algorithm (Meta Heuristic Algorigthm) is an improvement on the basis of Heuristic algorithm, and is a product of combining random algorithm and local search algorithm. Specifically, the order allocation method for the delivery vehicle provided by the embodiment of the invention is based on meta-heuristic line planning, and the requirement of balancing the carrying single quantity in different time periods is added, that is, all the user orders are clustered according to the time periods so as to meet the requirements of on-time delivery and installation of the orders and balanced carrying single quantity in different time periods of the line.
Optionally, the meta-heuristic algorithm used in the embodiment of the present invention may be an annealing clustering algorithm, a genetic algorithm, an ant colony clustering algorithm, a tabu algorithm, or the like, or may also adopt a C-W saving algorithm or a genetic algorithm, which is not specifically limited in the embodiment of the present invention.
The order distribution method for the delivery vehicles provided by the embodiment of the invention utilizes the meta-heuristic algorithm, dynamically sets and adjusts the order quantity in each time period through the order information of the user, obviously improves the wire arranging efficiency, and can ensure that the line time arrangement is more accurate and reasonable. The requirement of a user on the order delivery accuracy is met, meanwhile, the large idle time on a line is avoided, and the requirements of vehicle group personnel on the working saturation and the balance are considered.
Based on the content of the above embodiment, as an option, the embodiment of the present invention provides an optimization method for restricting the carrying quantity of the loaded vehicle in each time period and ensuring the workload balance for the problem of determining the carrying quantity of the loaded vehicle in each time period. The key technology is that the order quantity distributed in each time period is dynamically set and adjusted according to the obtained user order information. It is expressed that, in the above process of clustering all user orders according to the time period based on the meta heuristic algorithm, the method may further include: and dynamically setting the constraint conditions of the carrying orders in each time period according to the lifting and unloading time window information and the predicted service duration contained in the user order.
Specifically, in the embodiment of the present invention, in the process of performing time-interval clustering on all orders by using a meta-heuristic algorithm, first, an upper limit of the number of orders in each time interval may be set according to order information included in each user order; in addition, in the clustering iteration process, the order number in each time period can be adjusted to achieve the purpose of balancing.
As a specific implementation of the above constraint condition for dynamically setting the carrying unit amount of each time period, for example: the algorithm may adjust the upper limit of the number of orders for each time period based on the proportion of orders for each time period. For example, in the initial setting, the upper limit of the order quantity for three time periods is set to 6: 7: 3, but the proportion of actual orders in three time periods after cluster statistics is 6: 7: 5, then on the next iteration, the algorithm will attempt to increase the upper limit ratio of the third time period to 5 to ensure that the user order can be more reasonably enqueued into each time period.
According to the order allocation method for the delivery vehicles, the carrying order quantity constraint in each time period is dynamically set according to the order information of the user and the initial input parameters, the algorithm constraint can be more reasonably configured under the condition that the fluctuation of the total order quantity and the number of available vehicles is large, the algorithm precision is effectively improved, and the order allocation accuracy is improved.
Based on the content of the foregoing embodiment, as an alternative embodiment, the constraint condition of the carrying unit amount may include: any user order is sent within the corresponding lifting time window and the installation is completed within the expected service duration.
As one of the constraint conditions of the algorithm, in the embodiment of the present invention, the time ranges for which the user can get on the bus are respectively set according to the reserved time periods for all the user orders, so as to serve as the basic constraint of the algorithm bus, and all the orders must be guaranteed to arrive and complete installation within the reserved time range during the bus arrangement process. I.e. for each order: the reservation period start time is less than the order start service time and the order start service time is less than the reservation period end time.
The order distribution method for the loaded vehicles avoids the situation that the order of any user is not sent and loaded timely due to the fact that the balance of the carrying amount in each time period is over considered.
Based on the content of the foregoing embodiment, optionally, in the order allocation method for delivery vehicles provided in the embodiment of the present invention, an upper limit of the carrying amount in each time period is determined according to the total number of delivery vehicles and information of user orders; further, the constraint condition of the carrying unit quantity may further include: the amount of the carrying single allocated in each time period does not exceed the upper limit of the carrying single.
For example, the method according to the above embodiment divides the total working period of the day into three consecutive time periods, 09:00-13:00 in the morning and 13:00-17 in the afternoon: 00 and 17:00-21:00 at night. Since the proportion of the 3 time periods carrying the single quantities is relatively fixed every day, it can be understood that: on one hand, scheduling personnel can respectively determine the initial upper limit of the order quantity in three time periods in a parameter mode according to historical data because the carrying order quantity in each time period is relatively fixed; on the other hand, the meta-heuristic algorithm dynamically adjusts the upper limit of the order quantity in each time period according to the total order quantity and the number of vehicles per day, for example, calculates the proportion of the order of the user in each time period, and adjusts the upper limit of the order quantity in each time period according to the proportion. For example, the upper limit of the order quantity of the parameter setting three time periods is 6: 7: 3, but the actual order is in a ratio of 6: 7: and 5, when the next iteration is performed, the algorithm increases the upper limit proportion of the third time period to 5 as much as possible so as to ensure that the order can be normally discharged.
And after the upper limit of the carrying order quantity of each time interval is determined, setting a constraint to ensure that the quantity of the orders distributed in each time interval does not exceed the upper limit when clustering is carried out based on the meta-heuristic algorithm.
According to the order allocation method for the delivery vehicles, provided by the embodiment of the invention, the upper limit of the delivery unit quantity in each time period is set as another clustering constraint, so that the delivery unit quantity is not timely due to over saturation in a certain time period, and the situation that the delivery unit quantity in other time periods is not saturated, and further the line acceptance of vehicle group personnel is not high is avoided.
Based on the content of the foregoing embodiment, as an optional constraint condition of the carrying unit amount, the method may further include: the total effective working time of the user orders distributed in each time period is less than the total duration of the time period; wherein the total effective working time mainly comprises the predicted service time and the predicted running time of the delivery vehicle.
This constraint is used to reasonably plan all orders in each time period, i.e., ensure that the total duration of all orders allocated to that time period (including road travel time, loading and unloading time, and installation time, etc.) is less than the total duration of that time period, to ensure that all orders allocated to the delivery vehicle are completed properly during that time period.
Based on the content of the foregoing embodiment, as an optional constraint condition of the carrying unit amount, the method may further include: and under the condition that the carrying order quantity allocated in the previous time period does not reach the preset saturation degree, the user order is not allocated to the next time period.
In particular, this constraint can be understood as: in actual service operation, when a dispatcher performs route arrangement, any one route is required not to have the condition of order vacancy in a certain time period, such as a route which is distributed in the morning and evening but not distributed in the afternoon, so that a large period of idle time appears on the route, and the work saturation of a vehicle crew is reduced.
According to the order allocation method for the delivery vehicles provided by the embodiment of the invention, orders are prevented from being allocated to the subsequent time periods under the condition that the order quantity in the preceding time period does not reach the standard by setting the constraint of the relation of the carrying order quantity in each time period by using the meta-heuristic clustering algorithm. That is, by adding the clustering constraint, including making a constraint on the number of orders in two time periods before and after, the embodiments of the present invention cannot allocate an order to a subsequent time period in the case that an order is not allocated in the preceding time period, for example, if an order is not allocated in the afternoon, an order cannot be allocated in the evening.
According to the order distribution method for the delivery vehicle provided by the embodiment of the invention, under the condition that the carrying order quantity distributed in the previous time period does not reach the preset saturation degree, the user order is not distributed to the next time period, so that the balance of the carrying order quantity in each time period can be effectively ensured, the condition that the carrying order quantity in a certain time period is large and the order is not distributed in the next working time period adjacent to the certain time period is avoided, the adjacent working time period is skipped, the order is directly and continuously distributed to the next time period, and the condition that the carrying order quantity is distributed and balanced in each time period is further caused.
Based on the content of the foregoing embodiment, as an optional embodiment, the above clustering all the user orders according to the time period based on the meta heuristic algorithm includes, but is not limited to, the following steps:
differentiating the amount of the carrying unit allocated to each time segment from the upper limit of the amount of the carrying unit in the time segment; multiplying the square of the difference by a penalty coefficient to obtain a penalty value for carrying the single quantity in the time period; calculating the minimum value of the sum of penalty values corresponding to each time period; setting a function for calculating the minimum value of the sum of the penalty values as a penalty function related to the carrying single quantity in each time period; based on a simulated annealing algorithm, clustering all the user orders according to the time period in an iterative clustering mode; in each clustering process, balancing the weight of the carrying single quantity in each time period in the overall target function of the line through the penalty function; and if the clustering result is converged, acquiring the order line of each time period.
Specifically, in the embodiment of the present invention, the method includes setting a penalty function for carrying a single quantity in each time interval: the constraints in the above embodiments are all constraints that need to be strictly observed by the clustering algorithm in the operation process, but the balance of the task amount in each time period between different vehicles on the same day cannot be ensured only by means of the two constraints. Particularly, when the fluctuation of the total order quantity is large, it may occur that the order quantity of a certain delivery vehicle has reached the upper limit in a certain time period. And another vehicle has a lower order number in the same time period. The algorithm is guided to gradually converge towards a direction that the order number of each time interval of each vehicle is more balanced in the searching and exchanging process by setting a penalty function related to the carrying single quantity of each time interval;
finally, on the basis of the above settings and constraints described in the above embodiments, the algorithm generates an initial route and performs iterative optimization: the iteration is divided into multiple stages, each setting different parameters to achieve different line optimization goals. For the target carrying single quantity balance in each time interval, the algorithm gradually increases the weight of the carrying single quantity balance in each time interval in the overall objective function of the line in a simulated annealing mode by setting a dynamically-changed penalty coefficient so as to improve the iteration efficiency. And if the clustering result is converged, acquiring the order line of each time period, namely acquiring the result of clustering operation.
The simulated annealing algorithm is an optimization algorithm which can effectively avoid trapping in a serial structure which is locally minimum and finally tends to global optimum by endowing a search process with time-varying probability jump property and finally tends to zero. The method is a random optimization algorithm based on a Monte-Carlo iterative solution strategy, and the starting point is based on the similarity between the annealing process of solid matters in physics and a general combinatorial optimization problem. The simulated annealing algorithm starts from a certain high initial temperature, and randomly searches a global optimal solution of the objective function in a solution space by combining with the probability jump characteristic along with the continuous decrease of the temperature parameter, namely, the global optimal solution can jump out probabilistically in a local optimal solution and finally tends to be global optimal. The simulated annealing algorithm is a general optimization algorithm, theoretically, the algorithm has probabilistic global optimization performance, and is widely applied to engineering at present
The order distribution system provided by the embodiment of the invention utilizes the simulated annealing algorithm to cluster the user orders according to time periods, dynamically sets the carrying order quantity constraint of each time period according to order data and input parameters, and reasonably configures the algorithm constraint under the condition that the total order quantity and the number of available vehicles fluctuate greatly; penalty functions related to carrying single quantity of each vehicle in each time interval are introduced, penalty coefficients are gradually adjusted in a simulated annealing mode to help the algorithm to iterate more efficiently, the wire arranging efficiency is obviously improved, meanwhile, the line time arrangement is more accurate and reasonable, and the requirement of a user on the order punctuality is met; and each time interval carries the single volume more balanced, avoids appearing the idle time of big section on the circuit, satisfies the requirement of crew of a motor train group to circuit saturation and work equilibrium. Compared with the traditional wire arranging algorithm based on integer programming or heuristic algorithm, the embodiment of the invention designs a more targeted searching method and an iteration mode according to the problem characteristics and the service requirements, and can quickly iterate in reasonable calculation time and output a line meeting the service requirements including vehicle time-interval carrying single quantity constraint.
An embodiment of the present invention provides an order distribution system for a delivery vehicle, as shown in fig. 2, including but not limited to an order data collection unit 1, a time period planning unit 2, and an order distribution unit 3, where:
the order data collection unit 1 is mainly used for acquiring all user orders of the day, wherein each user order comprises pickup and discharge time window information; the time period planning unit 2 is mainly used for determining a plurality of continuous time periods according to the lifting and unloading time window information; the order distribution unit 3 is mainly used for clustering all user orders according to the time periods based on the meta-heuristic algorithm and outputting order lines of each time period.
Specifically, the order distribution system provided in the embodiment of the present invention first uses the order data collection unit 1 to obtain all user orders of the current day before performing order distribution every day, and reads and records the pickup time window information included in each user order.
Further, the time interval planning unit 2 is used for dividing the total working time interval of the day according to the time interval division rule combined with the business habit according to the information of the pickup and discharge time window contained in each user order collected and uploaded by the order data collecting unit 1.
And finally, clustering all user orders in different time periods by using the order distribution unit 3 based on a meta heuristic algorithm to obtain an ideal order distribution mode in each time period.
The order distribution system of the delivery vehicle provided by the embodiment of the invention utilizes the meta-heuristic algorithm, dynamically sets and adjusts the order quantity in each time period through the order information of the user, obviously improves the wire arranging efficiency, and can ensure that the line time arrangement is more accurate and reasonable. The requirement of a user on the order delivery accuracy is met, meanwhile, the large idle time on a line is avoided, and the requirements of vehicle group personnel on the working saturation and the balance are considered.
It should be noted that, during specific operation, the order allocation system for a delivery vehicle according to the embodiment of the present invention may be used to execute the order allocation method for a delivery vehicle according to any of the above embodiments, which is not described in detail herein.
Fig. 3 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 3: a processor (processor)310, a communication Interface (communication Interface)320, a memory (memory)330 and a communication bus 340, wherein the processor 310, the communication Interface 320 and the memory 330 communicate with each other via the communication bus 340. The processor 310 may call logic instructions in the memory 330 to perform the following method: acquiring all user orders on the current day, wherein each user order contains pickup and discharge time window information; dividing the total working time period of the day into a plurality of continuous time periods according to all the lifting and unloading time window information; based on meta heuristic algorithm, all user orders are clustered according to time periods, and order lines of each time period are output.
In addition, the logic instructions in the memory 330 may be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, an embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented to, when executed by a processor, perform the order allocation method for a delivery vehicle provided in the foregoing embodiments, for example, the method includes: acquiring all user orders on the current day, wherein each user order contains pickup and discharge time window information; dividing the total working time period of the day into a plurality of continuous time periods according to all the lifting and unloading time window information; based on meta heuristic algorithm, all user orders are clustered according to time periods, and order lines of each time period are output.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. A method of order distribution for a delivery vehicle, comprising:
acquiring all user orders on the current day, wherein each user order comprises pickup and discharge time window information;
dividing the total working time period of the day into a plurality of continuous time periods according to all the lifting and unloading time window information;
and based on a meta heuristic algorithm, clustering all the user orders according to the time periods, and outputting order lines of each time period.
2. The method of allocating an order for a delivery vehicle of claim 1, wherein in clustering all of said user orders according to said time period based on meta-heuristic algorithm, further comprises:
and dynamically setting the constraint conditions of the carrying orders in each time period according to the lifting and unloading time window information and the predicted service duration contained in the user order.
3. The order allocation method for delivery vehicles according to claim 2, wherein the constraints on the amount of delivery vehicles include: and any user order is sent in the corresponding lifting and unloading time window, and the installation is completed in the predicted service duration.
4. The order allocation method for delivery vehicles according to claim 3,
determining the upper limit of the carrying order quantity in each time period according to the total number of the vehicles to be loaded and the information of the user order;
the constraint condition for carrying the single quantity further comprises: the amount of the shipping unit allocated in each time period does not exceed the upper limit of the shipping unit amount.
5. The order allocation method for delivery vehicles according to claim 3, wherein the constraints on the amount of delivery vehicles further comprise:
the total effective working time of the user orders distributed in each time period is less than the total duration of the time period; wherein the total effective working time includes an expected service time period and an expected travel time of the delivery vehicle.
6. The order allocation method for delivery vehicles according to claim 3, wherein the constraints on the amount of delivery vehicles further comprise:
and under the condition that the carrying order quantity allocated in the previous time period does not reach the preset saturation degree, the user order is not allocated to the next time period.
7. The method of allocating an order for a delivery vehicle of claim 1 wherein said clustering all of said user orders by said time period based on meta-heuristic algorithms comprises:
differentiating the amount of the carrying unit allocated to each time segment from the upper limit of the amount of the carrying unit in the time segment;
multiplying the square of the difference by a penalty factor as a penalty value for carrying a single quantity within the time period;
calculating the minimum value of the sum of penalty values corresponding to each time period;
setting a function for calculating the minimum value of the sum of the penalty values as a penalty function related to the carrying unit quantity in each time period;
based on a simulated annealing algorithm, clustering all the user orders according to the time period in an iterative clustering mode; in each clustering process, balancing the weight of the carrying single quantity in each time period in the overall target function of the line through the penalty function;
and if the clustering result is converged, acquiring the order line of each time period.
8. An order distribution system for a delivery vehicle, comprising:
the order data collection unit is used for acquiring all user orders on the day, and each user order comprises pickup and discharge time window information;
a time period planning unit for determining a plurality of continuous time periods according to the lifting and unloading time window information;
and the order distribution unit is used for clustering all the user orders according to the time periods based on a meta-heuristic algorithm and outputting order lines of each time period.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the method of order allocation for a delivery vehicle according to any of claims 1 to 7 are carried out when the program is executed by the processor.
10. A non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program when executed by a processor implements the steps of the order distribution method for a delivery vehicle according to any of claims 1 to 7.
CN202010750769.0A 2020-07-30 2020-07-30 Order distribution method and system for package-feeding vehicles Active CN111985790B (en)

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CN108647810A (en) * 2018-04-19 2018-10-12 安吉汽车物流股份有限公司 The distribution method and device of order shipment, computer-readable medium
US20180341918A1 (en) * 2017-05-24 2018-11-29 Tata Consultancy Services Limited System and method for dynamic fleet management
CN109978447A (en) * 2019-03-06 2019-07-05 北京三快在线科技有限公司 A kind of logistics distribution layout of roads method and apparatus
CN111044062A (en) * 2018-10-15 2020-04-21 北京京东尚科信息技术有限公司 Path planning and recommending method and device

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US20180341918A1 (en) * 2017-05-24 2018-11-29 Tata Consultancy Services Limited System and method for dynamic fleet management
CN108647810A (en) * 2018-04-19 2018-10-12 安吉汽车物流股份有限公司 The distribution method and device of order shipment, computer-readable medium
CN111044062A (en) * 2018-10-15 2020-04-21 北京京东尚科信息技术有限公司 Path planning and recommending method and device
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