CN110674968A - Vehicle path optimization method for dynamic change of customer demands in express delivery process - Google Patents
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Abstract
The invention relates to the technical field of vehicle path planning in logistics industry, and discloses a vehicle path optimization method for dynamically changing customer requirements in the process of express delivery, which comprises the following steps: 1) constructing an express delivery vehicle path optimization model under the dynamic customer demand; 2) designing a processing strategy of various dynamic customer requirements appearing in the distribution process; 3) and optimizing the vehicle distribution path by adopting a hyper-heuristic algorithm. The invention considers the factor of customer demand change when constructing the path optimization model, simultaneously provides a corresponding processing strategy aiming at different types of dynamic customer demands, is beneficial to solving the problems that the original distribution scheme is difficult to execute or even infeasible due to newly increased return and exchange goods pick-up demands and the changed addressee information demand and rejection demand in the express distribution process, reduces the distribution cost and improves the distribution efficiency.
Description
Technical Field
The invention belongs to the technical field of vehicle scheduling, and relates to a new vehicle path optimization method for dynamically changing customer requirements in the express delivery process.
Background
The problems of high distribution cost, untimely delivery of the express delivery pieces and the like exist all the time because the number of clients for delivering services at the tail end of the express delivery is large, the positions of the clients are scattered, and the requirements such as service time are different greatly. Although the application of technologies and management means such as intelligent cabinets and others for collecting instead improves the success rate of one-time delivery, express companies still need to maintain certain transport redundancy so as to respond to dynamic changes of customer demands in time, such as newly-added customer demands, customer demands for modifying received information and the like, and thus the delivery cost is high. Therefore, express companies face the dilemma that the express delivery amount is increasing, but the profit is sliding due to the increase of the distribution cost. With the increase of the number of online shopping people and the number of express delivery, the situation that the information is dynamically changed by the customer is also increased continuously. These dynamic customer demands may cause the originally planned optimal path to be no longer optimal or even make the optimal path become infeasible, resulting in an originally tense distribution system being more congested and customer demands for timely delivery of the express delivery being more difficult to meet.
The existing processing mode of the dynamic customer demands is to accumulate a certain amount of dynamic customer demands or uniformly process the customer demands after a certain time period, or to serve the dynamic customers by virtue of the subjective experience of a courier, but the processing mode can not timely respond to the customer demands, thereby greatly reducing the satisfaction degree of the dynamic customers. If a proper processing strategy can be applied, the distribution scheme can be timely adjusted according to the real-time requirements of the customers, and the requirements of reducing the distribution cost and improving the customer satisfaction can be met.
The invention provides a vehicle path optimization method for dynamic change of customer requirements in express delivery, which considers the factor of customer requirement change when constructing a path optimization model, provides a corresponding processing strategy aiming at different types of dynamic customer requirements, and designs an optimization algorithm to process various dynamic customer requirements in the delivery process in real time so as to rationalize the insertion rate of dynamic customers, improve the performability of a delivery scheme in a dynamic change environment of requirements, help to solve the problems that the demand of returning and exchanging goods and picking up goods is newly added in the express delivery process, and the original delivery scheme is difficult to execute or even infeasible due to the change of the demand of receiving information and the rejection of the goods, reduce the delivery cost and improve the delivery efficiency.
Disclosure of Invention
A new vehicle path optimization method for dynamically changing customer requirements in the express delivery process is provided; firstly, an express delivery vehicle distribution path optimization model under dynamic customer requirements is constructed, various dynamic customer requirement processing strategies aiming at distribution in the process are designed, and then a hyperheuristic algorithm is adopted to optimize the vehicle distribution path.
In order to achieve the purpose, the following technical scheme is provided:
a vehicle path optimization method for dynamically changing customer requirements in express delivery comprises the following steps:
the method comprises the following steps: constructing an express delivery vehicle path optimization model under the dynamic customer demand;
1) parameter definition;
n: a node set N ═ 0, 1.·, N }, which represents a set of static customers to be delivered;
m: a node set M ═ n +1, n + 2.., n + M }, which represents a dynamic customer set occurring in the distribution process;
k: the same type of vehicle set K ═ {1,. K };
q: maximum load of the express delivery vehicle;
v: maximum capacity of express delivery vehicles;
[ai,bi]: a service time window required by the client node i;
ai: the earliest allowed arrival time required by client node i;
bi: the latest allowed arrival time required by the client node i;
[ai,bi]: a soft time window within customer tolerance;
ai: the earliest allowed arrival time that a client node i can tolerate;
bi: the latest allowed arrival time that the client node i can tolerate;
α1: penalty cost per unit time for a vehicle to arrive within a customer-tolerant soft window;
α2: unit time of vehicle arriving outside of customer-tolerant soft time windowPenalty cost of (2);
vi: the express delivery volume of the client node i;
qi: express delivery weight of the customer node i;
c: the cost per unit time of travel of the delivery vehicle;
n: the number of static customers to be distributed;
m: the number of dynamic customers that appear during the distribution process;
tij: the driving time between the client node i and the client node j;
ti: time for express delivery to the customer node i;
dij: the distance traveled between node i and node j;
2) a decision variable;
x for decision variables of vehicle path optimization modelijkIndicates that x is when vehicle k is traveling on the road segment between node i and node j ijk1, otherwise 0, i.e.
3) Designing a soft time window penalty function;
the client has a requirement on the service time window, the courier can not necessarily ensure that the client arrives within the time window range required by the client when serving the client, and the courier needs to pay certain punishment cost when arriving outside the time window range required by the client for service. But the tolerance of the client to the service time deviation is limited, so a broken-line type soft time window penalty function is designed, namely a soft time window penalty function fpunishThe following were used:
4) establishing a vehicle path optimization model taking the minimum delivery cost and penalty cost as an objective function, wherein the model is as follows:
an objective function:
constraint conditions are as follows:
xijk＝{0,1} (9)
in the formula, the objective function (3) represents minimizing the delivery cost and penalty cost of the delivery vehicle; the constraint condition (4) indicates that each customer point can only be served by one delivery vehicle and cannot be served for multiple times; constraint (5) indicates that each customer point must be placed in the line at the time of initial line planning; the constraint condition (6) indicates that the distribution vehicle finally needs to return to the distribution center after starting from the tail end distribution center; the constraint condition (7) represents that the sum of express delivery qualities of various customers transported by the delivery vehicle cannot exceed the weight limit of the vehicle; the constraint condition (8) indicates that the sum of express delivery volumes of various customers transported by the delivery vehicle cannot exceed the capacity limit of the vehicle; the constraint (9) represents an integer constraint, limiting the decision variable, namely xijkCan only take 0 and 1
Step two: designing a processing strategy of various dynamic customer requirements appearing in the distribution process;
in express delivery, these dynamic customer demands can be divided into three categories: newly adding customer requirements, modifying the customer requirements of the received information, and rejecting the customer requirements. An insertion algorithm is adopted for the dynamic clients to judge whether the newly added pickup requirement can be inserted into the current path, and the specific steps are as follows:
step 1: judging the type of the dynamic customer requirement;
① when the dynamic client requirement is the new client requirement, adding the new client information to the existing client information group;
② when the dynamic client requirement is the requirement of modifying the client for receiving information, modifying the receiving address information or receiving time information of the client;
③ when the dynamic client request is a rejected client request, the relevant information of the rejected client is deleted.
If the dynamic client is a newly added client or a client for modifying the received information, continuing to execute the next step; if the dynamic client is the rejected client, jumping to step 7;
step 2: the method comprises the steps that a distribution center is numbered as 0, and customers are numbered as 1, 2., n, n +1, n + 2.,. n, n + m, wherein 1, 2.,. n represents static customers to be distributed, and n +1, n + 2.,. n + m represents dynamic customers occurring in the distribution process;
and step 3: the fitness function values Δ f (i) added when n + m is inserted between the initial distribution paths path [ i ] and path [ i +1] are calculated, where i is 0, 1. The fitness function f (i) and the added fitness function Δ f (i) may be expressed as:
Δf(i)＝f(i+1)-f(i) (11)
and 4, step 4: selecting a minimum value from the delta f (i) as the current optimal insertion position, and if the added fitness function value is within an acceptable range, continuing to execute the next step; if the added fitness function is not in the acceptable range, jumping to step 6;
and 5: judging whether the dynamic client meets the requirement of the soft time window or not, and the time t of reaching the client jjComprises the following steps:
tj＝tpath[m]+max[(apath[m]-tpath[m]),0]+tpath[m],j(12)
where j ═ n +1, n + 2.., n + m, max [ (a)path[m]-tpath[m]),0]Expressed as the wait time after the courier arrives at customer m; t is tpath[m],jRepresenting the driving time from the inserted dynamic client m to the client j; t is tpath[m]Indicating the time of delivery of the express to the dynamic customer m. If aj≤tj≤bjIf yes, the time window requirement of the dynamic client is met, and an initial optimal solution is obtained; if aj≤tj≤bjIf not, the time window requirement of the dynamic client is not met, and the inserting operation fails for the client node. Wherein, ajRepresents the earliest allowed arrival time required by client node j; bjRepresents the latest allowed arrival time required by the client node j; t is tjIndicating the time of delivery of the express to the client node j;
step 6: making j equal to j +1 to continue to execute the insert operation, and if all the dynamic clients execute the insert operation, continuing to execute the next step; if the dynamic client does not execute the inserting operation, jumping to the step 3;
and 7: and optimizing the vehicle distribution path by adopting a hyper-heuristic algorithm for the dynamic clients capable of being inserted into the existing path and the static clients to be distributed.
Step three: optimizing the vehicle distribution path by adopting a hyper-heuristic algorithm;
the corresponding dynamic customer demands are processed by applying different strategies aiming at different dynamic customers, and then the vehicle distribution path is optimized by adopting a hyper-heuristic algorithm. The method comprises the following specific steps:
step 1: encoding existing static clients and dynamic clients that can be inserted into existing paths;
step 2: calculating a fitness function value of each individual;
and step 3: executing selection operation through the determined selection mode and the set selection probability, and selecting a certain amount of individuals with high fitness values as parents;
and 4, step 4: carrying out crossing and mutation operations on individuals according to the set crossing probability and mutation probability through the crossing mode and mutation mode of the individuals;
and 5: returning to the step 2 to recalculate the fitness function value of the individual, judging whether the terminal condition is met, and if the terminal condition is met, updating the optimal distribution scheme; and if the termination condition is not met, jumping to the step 3.
To determine the effect of dynamic customer demand on the vehicle delivery schedule, the total delivery costs after real-time processing are compared to the original rescheduled total delivery costs. The percentage of delivery costs η that are reduced before and after optimization can be expressed as:
in the formula, F' is the total delivery cost rescheduled before the express delivery scheme is adjusted, and F is the total delivery cost after the delivery scheme is updated in real time.
The invention has the beneficial effects that:
a large amount of dynamic customer demands can appear in an actual distribution environment, and the vehicle path optimization method for dynamic change of the customer demands in the express delivery process is provided.
Drawings
In order to make the object, technical scheme and beneficial effect of the invention more clear, the invention provides the following drawings for explanation:
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a diagram of a single point crossover operation;
FIG. 3 is a variation diagram;
FIG. 4 is a static customer demand;
FIG. 5 is a diagram of three types of dynamic customer requirements;
FIG. 6 is a dynamic client process flow diagram;
FIG. 7 is a delivery scenario;
FIG. 8 is a distribution cost comparison;
Detailed Description
Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of the method of the present invention, and as shown in the figure, the method for optimizing a vehicle route in which customer demands are dynamically changed during express delivery according to the present invention includes the following steps:
the method comprises the following steps: constructing an express delivery vehicle path optimization model under the dynamic customer demand;
establishing a vehicle path optimization model taking the minimum delivery cost and penalty cost as an objective function, wherein the model is as follows:
an objective function:
constraint conditions are as follows:
xijk＝{0,1}
in the formula, tijRepresenting the driving time from the client node i to the client node j; n represents the number of static customers to be distributed; m represents the number of dynamic clients present during the distribution process; k represents the same type of vehicle set; c represents the cost per unit travel time of the delivery vehicle; x is the number ofijkRepresenting decision variables of the vehicle k path optimization model at a client node i and a client node j; f. ofpunishRepresenting a soft time window penalty function; q. q.siExpress delivery weight of the customer node i; q represents the maximum load of the express delivery vehicle; v. ofiExpress delivery volume of the customer node i; v denotes the maximum capacity of the express delivery vehicle.
Step two: designing a processing strategy of various dynamic customer requirements appearing in the distribution process;
step 1: and judging the type of the dynamic customer requirements, including newly increased customer requirements, modifying the customer requirements of the received information, and rejecting the customer requirements. If the dynamic client is a newly added client or a client for modifying the received information, continuing to execute the next step; if the dynamic client is the rejected client, jumping to step 7;
step 2: the method comprises the steps that a distribution center is numbered as 0, and customers are numbered as 1, 2., n, n +1, n + 2.,. n, n + m, wherein 1, 2.,. n represents static customers to be distributed, and n +1, n + 2.,. n + m represents dynamic customers occurring in the distribution process;
and step 3: the fitness function values Δ f (i) added when n + m is inserted between the initial distribution paths path [ i ] and path [ i +1] are calculated, where i is 0, 1. The fitness function f (i) and the added fitness function Δ f (i) may be expressed as:
Δf(i)＝f(i+1)-f(i)
and 4, step 4: selecting a minimum value from the delta f (i) as the current optimal insertion position, and if the added fitness function value is within an acceptable range, continuing to execute the next step; if the added fitness function is not in the acceptable range, jumping to step 6;
and 5: judging whether the dynamic client meets the requirement of the soft time window or not, and the time t of reaching the client jjComprises the following steps:
tj＝tpath[m]+max[(apath[m]-tpath[m]),0]+tpath[m],j
where j ═ n +1, n + 2.., n + m, max [ (a)path[m]-tpath[m]),0]Expressed as the wait time after the courier arrives at customer m; t is tpath[m],jRepresenting the driving time from the inserted dynamic client m to the client j; t is tpath[m]Indicating the time of delivery of the express to the dynamic customer m. If aj≤tj≤bjIf yes, the time window requirement of the dynamic client is met, and an initial optimal solution is obtained; if aj≤tj≤bjIf not, the time window requirement of the dynamic client is not met, and the inserting operation fails for the client node. Wherein, ajRepresents the earliest allowed arrival time required by client node j; bjRepresents the latest allowed arrival time required by the client node j; t is tjIndicating the time of delivery of the express to the client node j;
step 6: making j equal to j +1 to continue to execute the insert operation, and if all the dynamic clients execute the insert operation, continuing to execute the next step; if the dynamic client does not execute the inserting operation, jumping to the step 3;
and 7: and optimizing the vehicle distribution path by adopting a hyper-heuristic algorithm for the dynamic clients capable of being inserted into the existing path and the static clients to be distributed.
Step three: and optimizing the vehicle distribution path by adopting a hyper-heuristic algorithm.
Step 1: encoding existing static clients and dynamic clients that can be inserted into existing paths;
step 2: calculating a fitness function value of each individual;
and step 3: executing selection operation through the determined selection mode and the set selection probability, and selecting a certain amount of individuals with high fitness values as parents;
the value range of the general probability is 0.4-0.7, and the selection probability is set to be 0.6. After selecting the superior parent individual, two parent chromosomes, parent 1 and parent 2, are randomly selected, and a single point crossover operation is performed to generate two new child chromosomes, child 1 and child 2. The single point crossover operation is illustrated in fig. 2.
And 4, step 4: carrying out crossing and mutation operations on individuals according to the set crossing probability and mutation probability through the crossing mode and mutation mode of the individuals;
the mutation operation is performed by selecting a certain individual from the population according to a set mutation probability, and mainly includes gene splitting, gene merging, and gene deletion, and a specific mutation operation diagram is shown in fig. 3.
And 5: returning to the step 2 to recalculate the fitness function value of the individual, judging whether the terminal condition is met, and if the terminal condition is met, updating the optimal distribution scheme; and if the termination condition is not met, jumping to the step 3.
Example (b):
22 customers of a certain end distribution center are selected for testing, the number of the distribution center is 0, and the number of the customers is 1, 2. The courier has the following dynamic customer requirements in the delivery process: the order number is 18 customer refusal express delivery requirement, the order number is 6 customer modification receiving time requirement, the order number is 12 customer modification receiving address requirement, the order number is 22 customer newly added mailing requirement, specific static customer information is shown in fig. 4, and dynamic customer information is shown in fig. 5. When the courier receives the dynamic customer demand of the customer or the dynamic customer demand sent by the distribution center, the dynamic customer processing flow chart is shown in fig. 6 by applying the processing method provided by the invention. According to the optimized distribution scheme, the courier adjusts the distribution scheme in real time in the distribution process, and the obtained distribution scheme is shown in fig. 7. To analyze the effectiveness of the method, the optimization plan is compared to the delivery costs of re-dispatching vehicles to service the customer, with the results shown in FIG. 8. The total delivery cost after real-time processing is compared to the original rescheduled total delivery cost, the percentage of delivery costs that are reduced before and after optimization
The dynamic customer requirements are changed to be the main reason that most of terminal distribution schemes are not feasible, general couriers cannot process various dynamic customer requirements in real time in the distribution process, a distribution center needs to re-distribute vehicles to serve the dynamic customers, and the total distribution cost is higher than that of the optimized distribution scheme provided by the invention.
The invention provides a vehicle path optimization method for dynamically changing customer demands in express delivery process, which can process various dynamic customer demands in the delivery process in real time so as to rationalize dynamic customer insertion rate and improve timeliness of dynamic customer service.
Finally, it is noted that the above-mentioned preferred embodiments illustrate rather than limit the invention, and that, although the invention has been described in detail with reference to the above-mentioned preferred embodiments, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the scope of the invention as defined by the appended claims.
Claims (4)
1. A vehicle path optimization method for dynamically changing customer requirements in express delivery is characterized in that:
the method comprises the following steps:
the method comprises the following steps: constructing an express delivery vehicle path optimization model under the dynamic customer demand;
step two: designing a processing strategy of various dynamic customer requirements appearing in the distribution process;
step three: and optimizing the vehicle distribution path by adopting a hyper-heuristic algorithm.
2. The method for optimizing the vehicle path for the dynamic change of the customer demand in the express delivery process according to claim 1, wherein: the express delivery vehicle path optimization model under the dynamic customer requirement in the step one is as follows:
an objective function:
constraint conditions are as follows:
xijk＝{0,1}
in the formula, tijRepresenting the driving time from the client node i to the client node j; n represents the number of static customers to be distributed; m represents the number of dynamic clients present during the distribution process; k represents the same type of vehicle set; c represents the cost per unit travel time of the delivery vehicle; x is the number ofijkRepresenting decision variables of the vehicle k path optimization model at a client node i and a client node j; f. ofpunishRepresenting a soft time window penalty function; q. q.siExpress delivery weight of the customer node i; q represents the maximum load of the express delivery vehicle; v. ofiExpress delivery volume of the customer node i; v denotes the maximum capacity of the express delivery vehicle.
3. The method for optimizing the vehicle path according to claim 1, wherein the dynamic customer demands in the second step include: newly adding customer requirements, modifying the customer requirements of the received information, and rejecting the customer requirements.
4. The method for optimizing the vehicle path for the dynamic change of the customer demand in the express delivery process according to claim 1, wherein: the processing strategy of various dynamic customer requirements appearing in the design distribution process in the step two is specifically as follows:
4-1: judging the type of the dynamic client requirement, if the type of the dynamic client is a newly added client or a client for modifying the received information, continuing to execute the next step; if the dynamic client is the rejected client, skipping to 4-7;
4-2: the method comprises the steps that a distribution center is numbered as 0, and customers are numbered as 1, 2., n, n +1, n + 2.,. n, n + m, wherein 1, 2.,. n represents static customers to be distributed, and n +1, n + 2.,. n + m represents dynamic customers occurring in the distribution process;
4-3: calculating an added fitness function value delta f (i) of a customer n +1, n +2, wherein n + m is inserted between an initial distribution path [ i ] and a path [ i +1], wherein i is 0,1,. The fitness function f (i) and the added fitness function Δ f (i) may be expressed as:
Δf(i)＝f(i+1)-f(i)
4-4: selecting a minimum value from the delta f (i) as the current optimal insertion position, and if the added fitness function value is within an acceptable range, continuing to execute the next step; if the added fitness function is not in the acceptable range, skipping to 4-6;
4-5: judging whether the dynamic client meets the requirement of the soft time window or not, and the time t of reaching the client jjComprises the following steps:
tj＝tpath[m]+max[(apath[m]-tpath[m]),0]+tpath[m],j
where j ═ n +1, n + 2.., n + m, max [ (a)path[m]-tpath[m]),0]Expressed as the wait time after the courier arrives at customer m; t is tpath[m],jRepresenting the driving time from the inserted dynamic client m to the client j; t is tpath[m]Indicating the time of delivery to dynamic client m. If aj≤tj≤bjIf yes, the time window requirement of the dynamic client is met, and an initial optimal solution is obtained; if aj≤tj≤bjIf not, the time window requirement of the dynamic client is not met, and the inserting operation fails for the client node. Wherein, ajRepresents the earliest allowed arrival time required by client node j; bjRepresents the latest allowed arrival time required by the client node j; t is tjIndicating the time of delivery of the express to the client node j;
4-6: making j equal to j +1 to continue to execute the insert operation, and if all the dynamic clients execute the insert operation, continuing to execute the next step; if the dynamic client does not execute the insert operation, jumping to 4-3;
4-7: and optimizing the vehicle distribution path by adopting a hyper-heuristic algorithm for the dynamic clients capable of being inserted into the existing path and the static clients to be distributed.
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| CN114819819A (en) * | 2022-04-15 | 2022-07-29 | 电子科技大学 | Path planning implementation method under instant logistics scene |
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