CN108537491A - A kind of fresh agricultural products Distribution path optimization method, storage medium - Google Patents
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Abstract
A kind of fresh agricultural products Distribution path optimization method of present invention offer and storage medium, the information such as the position coordinates of oneself, the car loading of required dispatching and required distribution time range are submitted to system by each demand point, to minimize distribution cost and maximize customer satisfaction as optimization aim, fresh agricultural products Distribution path Model for Multi-Objective Optimization is established.Simultaneously using based on adaptive genetic algorithm solving model, Distribution path prioritization scheme is obtained.The present invention establishes the speed characteristic model under different weather situation, different periods；Establish the time window punishment cost function of fresh agricultural products；Construct the Model for Multi-Objective Optimization of fresh agricultural products Distribution path；And condition of road surface when can be according to dispatching formulates distribution route, to reach distribution cost minimum, the maximum target of customer satisfaction.
Description
Technical Field
The invention belongs to the field of logistics distribution, and particularly relates to a fresh agricultural product distribution path optimization method and storage equipment.
Background
With the rapid development of social economy and the continuous improvement of the living standard of residents, the quality requirements of people on fresh agricultural products are higher and higher. The fresh agricultural products mainly comprise fruits, vegetables, aquatic products, meat, flowers and the like, and have the characteristics of easy and easy decay, short quality guarantee period and the like, so that higher control requirements are provided for the circulation link of the fresh agricultural products. Therefore, how to scientifically and reasonably arrange the distribution route to ensure the freshness of the fresh agricultural products, improve the distribution efficiency and reduce the distribution cost is one of the important problems in the logistics distribution link of the fresh agricultural products.
At present, scholars at home and abroad carry out a great deal of research work around the optimization problem of logistics distribution paths of fresh agricultural products. Some methods establish the fresh logistics distribution vehicle path problem with a time window on the basis of comprehensively considering distribution distance, vehicle fixed cost, fresh loss and other factors; some methods use a fuzzy membership function to represent the customer satisfaction of distribution points, and establish a multi-objective distribution path optimization model with minimized distribution cost and maximized customer satisfaction; the above studies assume that the travel time and the transportation cost of the delivery vehicle are related only to the delivery distance, and neglect the influence of different road conditions on the vehicle travel speed and the delivery cost. Aiming at the optimization model of the cold-chain logistics distribution path under the time-varying condition, some methods consider the dynamic driving speed and improve the tabu search algorithm to find the balance point between the distribution service quality and the distribution cost. Some methods construct a cold-chain logistics distribution optimization model according to the passing condition of each distribution road section in different time periods, and design a hybrid genetic algorithm to solve the model. Some methods combine real-time traffic information to study the path optimization of the same-city cold-chain logistics distribution. The above studies, while taking into account the time-varying nature of the delivery vehicle travel speed in the optimization process, do not establish a link between different road conditions and the delivery optimization model.
Disclosure of Invention
The invention provides a fresh agricultural product distribution path optimization method considering a road state, which aims to solve the problems in the prior art.
The invention adopts the following technical scheme:
a fresh agricultural product delivery path optimization method comprises the following steps:
(1) establishing a path optimization target model of fresh agricultural product delivery by taking the goals of minimizing the transportation cost and maximally obtaining the satisfaction degree of customers on logistics service as targets:
s.t.
wherein Z is1Indicating delivery cost, Z2 indicating customer satisfaction; n denotes the number of delivery points, k denotes the kth vehicle, i denotes the ith delivery point, j denotes the jth delivery point, tijIndicating the time from the ith delivery point to the jth delivery point for the kth vehicle,indicating the amount of demand at the delivery point i,the price of the unit fresh agricultural product is expressed,it is shown that,representing the sensitivity of fresh agricultural products to time, C3Represents a time window penalty cost, tiRepresents the time at which the vehicle reaches delivery point i;
represents the operating cost of the delivery vehicle;
vkis a variable of 0-1, v is when the k-th vehicle is usedkIs 1, otherwise vkIs 0;
xijkx is a variable from 0 to 1 when the k-th vehicle travels from delivery point i to delivery point jijkIs 1, otherwise,
xijkis 0;
(2) and solving the model by using a genetic algorithm to obtain a distribution scheme of the fresh agricultural products.
The genetic algorithm for solving the model comprises the following steps:
s1: assuming a population size of L, i.e., L feasible solutions, the ith individual is represented asWherein r isn(0＜rn< 1) is the genetic information of the individual, generated by a random function;
whereinIs a groupThe number, equal to the sum of the number of delivery points and the maximum number of delivery vehicles required:
s2: constructing a fitness function:
W(g)＝w0·aw gg＝1,2,…,gmax
wherein, FlDenotes the fitness of the ith individual, L ═ 1,2, … L; z1And Z2The delivery cost and customer satisfaction corresponding to the first individual, respectively; w (g) represents an annealing temperature function and is related to a population generation number g; w is a0＝gmaxDenotes initial annealing temperature, gmaxRepresenting the maximum evolutionary algebra of the population; a iswIs the annealing temperature coefficient;
s3: individual selection: and (3) selecting excellent individuals from the population by adopting a roulette rule to perform cross operation and mutation operation:
cross probability pcIs shown as
Wherein, FmaxRepresenting the maximum fitness in the current population;representing the average fitness of the population; f' represents a higher fitness value between the paired individuals; k'1And k'2Represents a crossover parameter;
mutation probability p of the l-th individuall,mIs defined as
Wherein, FlRepresenting the fitness of the ith individual; k'3And k'4Representing a variation parameter. The algorithm adopts Gaussian variation to realize variation operation;
s4: and (3) decoding: in the chromosome decoding process, comparing each gene in the chromosome by an algorithm, and acquiring the position values of the genes in the array from small to large, wherein when the value is larger than the number of distribution points, the value is changed into 0; and when 0 values are adjacent, carrying out duplicate removal operation to obtain a distribution path scheme.
The vehicle transportation cost C1 is expressed as:
wherein N is the number of distribution points;represents a vehicle running cost per unit time; dijIs the distance from delivery point i to delivery point j;is the average travel speed of the delivery vehicle; zetaconThe influence rate of the vehicle speed under different weather conditions and different time periods is, sun, rain, snow and fog respectively represent sunny days, rainy days, snowy days and foggy days:
the raw fresh loss cost is expressed as follows:
wherein,representing the demand of the delivery point i;expressing the price of unit fresh agricultural product;the sensitivity of the quality of the fresh agricultural products to the time is shown,the larger the value is, the lower the sensitivity of the product quality to time is; t is tikIndicating the time at which the kth vehicle reaches delivery point i.
The time window penalty cost is expressed as:
wherein M is a positive number, αc，βc(αc，βc< 0) is a weight parameter;
andis the delivery time range rejected by the customer;
is the ideal delivery time range for the customer;
andis the range of delivery times acceptable to the customer.
The customer satisfaction cost for distribution point i is expressed as:
wherein the parametersIs a weight parameter.
A storage medium having stored therein a computer program which, when read by a processor, performs the above-described method.
The invention has the beneficial effects that:
(1) vehicle speed characteristic models under different weather conditions and different time periods are established;
(2) establishing a time window punishment cost function of the fresh agricultural products;
(3) constructing a multi-objective optimization model of a fresh agricultural product delivery path;
(4) the system can make a distribution route according to the road condition during distribution, so that the aims of minimum distribution cost and maximum customer satisfaction are achieved.
Drawings
FIG. 1 is a flow chart of the present invention.
Fig. 2 is a schematic diagram of a decoding process.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
In the actual distribution process, factors such as weather conditions, morning and evening peak hours, special festivals and the like directly influence the running speed of the distribution vehicle, so that the distribution cost and the customer satisfaction degree are changed. Therefore, the fresh agricultural product distribution scheme is provided according to the vehicle speed characteristics under different weather conditions and different time periods and under the condition of comprehensively considering factors such as road conditions, time windows, fresh loss and the like, the distribution scheme enables the distribution cost to be minimum, and the customer satisfaction degree to be maximum.
The method comprises the following specific steps:
1) first, each demand point submits information such as its own position coordinates, the amount of goods to be delivered, and the required delivery time range to the system.
2) And establishing a multi-objective optimization model of the fresh agricultural product delivery path by taking the minimized delivery cost and the maximized customer satisfaction as optimization objectives.
The detailed description is as follows:
(ii) vehicle transportation cost
Vehicle transportation cost C1Including operating costs and driving costs during vehicle use, expressed as
Wherein k represents the kth vehicle, and N is the number of distribution points;representing the operating cost of the delivery vehicle, mainly comprising the fixed loss cost of the vehicle and the wage cost of the driver; v. ofkIs a variable of 0-1, v is when the k-th vehicle is usedkIs 1, otherwise, vkIs 0;represents a vehicle running cost per unit time; x is the number ofijkIs a variable of 0-1, x when the k-th vehicle travels from delivery point i to delivery point jijkIs 1, otherwise, xijkIs 0; dijIs the distance from delivery point i to delivery point j;is the average travel speed of the delivery vehicle; zetacon(con, rain, fog) is the influence rate of the vehicle speed in different weather conditions (sunny days, rainy days, snowy days, and foggy days) and different periods, and is represented by expressions (2) to (5),
where t denotes a certain time in 24 hours of vehicle travel.
fresh goods loss cost
Fresh agricultural products are easily affected by factors such as temperature and time, and the cost of goods loss can be generated in the distribution process. Considering that fresh agricultural products are transported by cold-chain logistics and are relatively stable in temperature, the cargo loss cost C2 can be expressed as
Wherein,representing the demand of the delivery point i;expressing the price of unit fresh agricultural product;the sensitivity of the quality of the fresh agricultural products to the time is shown,the larger the value is, the lower the sensitivity of the product quality to time is; t is tikIndicating the time at which the kth vehicle reaches delivery point i.
penalty cost for time window
The time window is established by requiring the delivery vehicle to arrive within a customer specified time frame. The time windows can be divided into hard time windows and soft time windows depending on whether the customer's requirements for service time are strict or not. Unlike the hard time window, the soft time window allows the delivery vehicle to arrive outside the customer specified time frame, but requires a certain penalty fee to be paid, and the farther the time specified for the vehicle to arrive at the customer is from the specified time frame, the higher the penalty fee is paid. In order to fit the actual distribution situation, the method adopts a soft time window, and constructs a time window punishment cost function of fresh agricultural product distribution, as shown in a formula (7).
wherein M is a large positive number, αc，βc(αc，βc< 0) is a weight parameter, and the value depends on the requirement of a customer on the delivery time. The compound represented by the formula (7),andthe delivery time range rejected by the customer, and when the delivery vehicle arrives in the time period, a large penalty fee needs to be paid;the ideal distribution time range of the customer, and when the distribution vehicle arrives in the time period, no penalty fee is paid;andis the delivery time range acceptable to the customer, and a certain penalty fee is paid when the delivery vehicle arrives in the time range. The delivery vehicles are based on the perishable vulnerability characteristics of the fresh produceWhen the range reaches (earlier than the ideal distribution time), the product quality and the sales are not greatly influenced, and the punishment cost linearly changes along with the time; however, the delivery vehicle is inWhen the range is reached (later than the ideal delivery time), the remaining shelf life of the product is shortened, thereby having a great influence on the quality and sales of the product, and the penalty cost exponentially changes with time.
satisfaction degree of customer
The customer satisfaction refers to the satisfaction degree of the customer on the logistics service, and reflects the logistics service level of the fresh agricultural products. In a logistics distribution segment, a major factor affecting customer satisfaction is the time for a distribution vehicle to reach a distribution point. Thus, the customer satisfaction function for delivery point i is expressed as,
wherein, the weight parameterThe value of (a) depends on the customer's demand for delivery time.
model establishment
The invention establishes a multi-target optimization model of fresh agricultural product delivery paths by taking the minimization of delivery cost and the maximization of customer satisfaction as optimization targets:
in the optimization problem, the constraint conditional expressions (11) to (12) indicate that the delivery vehicle starts from the delivery center, serves the delivery point, and returns to the delivery center; constraint equations (13) to (14) indicate that each distribution point can be serviced by a distribution vehicle only once; a constraint conditional expression (15) constrains a loading capacity of a delivery vehicle,is the maximum loading capacity of the delivery vehicle, yikIs a variable of 0 to 1, and y is the delivery point i delivered by the k-th vehicleikIs 1, otherwise, yikIs 0. t is tiIndicating the time at which the vehicle reaches delivery point i.
When the model is solved, the simulated annealing thought is combined, and the distribution path optimization scheme is obtained by using the self-adaptive genetic algorithm for solving.
The detailed method comprises the following steps:
coding and initializing populations
And (3) encoding the feasible solution by adopting a random number group: assuming a population size of L, i.e., L feasible solutions, the ith individual is represented asWherein r isn(0＜rn< 1) is the genetic information of the individual, generated by a random function;is the number of genes, which is equal to the sum of the number of delivery points and the theoretically required maximum number of delivery vehicles.
For example, there are 4 distribution points in the distribution network, the theoretical maximum number of required distribution vehicles is 4, and the coded individuals are represented as: [0.25,0.32,0.41,0.40,0.36,0.68,0.16,0.88].
fitness calculation
The invention combines simulated annealing to construct a fitness function Fl
W(g)＝w0·aw gg＝1,2,…,gmax(17)
Wherein, FlDenotes the fitness of the ith individual, L ═ 1,2, … L; z1And Z2The delivery cost and customer satisfaction corresponding to the first individual, respectively; w (g) represents an annealing temperature function and is related to a population generation number g; in formula (17), w0＝gmaxDenotes initial annealing temperature, gmaxRepresenting the maximum evolutionary algebra of the population; a iswIs the annealing temperature coefficient.
(iii) Individual selection
The invention adopts roulette principle to select excellent individuals from the population for crossover and mutation.
fourthly, cross operation
The invention defines the cross probability pcIs shown as
Wherein, FmaxRepresenting the maximum fitness in the current population;representing the average fitness of the population; f'Representing a higher fitness value between the paired individuals; k'1And k'2Indicating the crossing parameter. The method uses arithmetic interleaving to implement the interleaving operation.
variant operation
The invention defines the mutation probability p of the I individuall,mIs defined as
Wherein, FlRepresenting the fitness of the ith individual; k'3And k'4Representing a variation parameter. The algorithm adopts Gaussian variation to realize variation operation.
decoding
In the chromosome decoding process, comparing each gene in the chromosome by an algorithm, and acquiring the position values of the genes in the array from small to large, wherein when the value is larger than the number of distribution points, the value is changed into 0; when 0 values are adjacent, the duplicate removal operation is carried out to obtain a distribution path scheme (the optimal distribution path refers to the ordering of the demand points with the lowest distribution cost and the highest customer satisfaction degree), wherein 0 represents a distribution center, and other numbers represent different distribution points. For example, an individual [0.25,0.32,0.41,0.40,0.36,0.68,0.16,0.88] is decoded and represented as [2,3,0,4,0,1,0], from which it can be derived: the distribution paths are respectively 0-2-3-0; 0-4-0; 0-1-0. The decoding process is shown below in fig. 2.
The present invention also provides a storage medium storing a computer program therein, which when read by a processor, executes the path optimization method of the present invention. The storage medium is an existing storage medium that may be connected to the processor or belong to a memory in the processor.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various changes and modifications can be made without departing from the overall concept of the present invention, and these should also be considered as the protection scope of the present invention.
Claims (7)
1. A fresh agricultural product delivery path optimization method is characterized by comprising the following steps:
(1) establishing a path optimization target model of fresh agricultural product delivery by taking the goals of minimizing the transportation cost and maximizing the satisfaction degree of customers to logistics service as targets:
s.t.
wherein Z is1Indicating delivery cost, Z2 indicating customer satisfaction; n denotes the number of delivery points, k denotes the kth vehicle, i denotes the ith delivery point, j denotes the jth delivery point, tijIndicating the time from the ith delivery point to the jth delivery point for the kth vehicle,indicating the amount of demand at the delivery point i,the price of the unit fresh agricultural product is expressed,it is shown that,representing the sensitivity of fresh agricultural products to time, C3Represents a time window penalty cost, tiIndicating the time at which the vehicle reaches delivery point i；
Represents the operating cost of the delivery vehicle;
vkis a variable of 0-1, v is when the k-th vehicle is usedkIs 1, otherwise vkIs 0;
xijkx is a variable from 0 to 1 when the k-th vehicle travels from delivery point i to delivery point jijkIs 1, otherwise, xijkIs 0;
(2) and solving the model by using a genetic algorithm to obtain a distribution scheme of the fresh agricultural products.
2. The fresh agricultural product delivery path optimization method of claim 1, wherein:
the genetic algorithm for solving the model comprises the following steps:
s1: assuming a population size of L, i.e., L feasible solutions, the ith individual is represented asWherein r isn(0＜rn< 1) is the genetic information of the individual, generated by a random function;
whereinIs the number of genes, equal to the sum of the number of delivery points and the maximum number of vehicles required to be delivered:
s2: constructing a fitness function:
W(g)＝w0·aw gg＝1,2,…,gmax
wherein, FlDenotes the fitness of the ith individual, L ═ 1,2, L; z1And Z2Distribution cost and customer fullness corresponding to the first individualDegree of intention; w (g) represents an annealing temperature function and is related to a population generation number g; w is a0＝gmaxDenotes initial annealing temperature, gmaxRepresenting the maximum evolutionary algebra of the population; a iswIs the annealing temperature coefficient;
s3: individual selection: and (3) selecting excellent individuals from the population by adopting a roulette rule to perform cross operation and mutation operation:
cross probability pcIs shown as
Wherein, FmaxRepresenting the maximum fitness in the current population;representing the average fitness of the population; f' represents a higher fitness value between the paired individuals; k'1And k'2Represents a crossover parameter;
mutation probability p of the l-th individuall,mIs defined as
Wherein, FlRepresenting the fitness of the ith individual; k'3And k'4Representing a variation parameter. The algorithm adopts Gaussian variation to realize variation operation;
s4: and (3) decoding: in the chromosome decoding process, comparing each gene in the chromosome by an algorithm, and acquiring the position values of the genes in the array from small to large, wherein when the value is larger than the number of distribution points, the value is changed into 0; and when 0 values are adjacent, carrying out duplicate removal operation to obtain a distribution path scheme.
3. The fresh agricultural product delivery path optimization method of claim 1, wherein:
the vehicle transportation cost C1Expressed as:
wherein N is the number of distribution points;represents a vehicle running cost per unit time; dijIs the distance from delivery point i to delivery point j;is the average travel speed of the delivery vehicle; zetaconThe influence rate of the vehicle speed under different weather conditions and different time periods is, sun, rain, snow and fog respectively represent sunny days, rainy days, snowy days and foggy days:
4. the fresh agricultural product delivery path optimization method of claim 1, wherein:
the raw fresh loss cost is expressed as follows:
wherein,representing the demand of the delivery point i;expressing the price of unit fresh agricultural product;the sensitivity of the quality of the fresh agricultural products to the time is shown,the larger the value is, the lower the sensitivity of the product quality to time is; t is tikIndicating the time at which the kth vehicle reaches delivery point i.
5. The fresh agricultural product delivery path optimization method of claim 1, wherein:
the time window penalty cost is expressed as:
wherein M is a positive number, αc，βc(αc，βc< 0) is a weight parameter;
andis the delivery time range rejected by the customer;
is the ideal delivery time range for the customer;
andis the range of delivery times acceptable to the customer.
6. The fresh agricultural product delivery path optimization method of claim 1, wherein:
the customer satisfaction cost for distribution point i is expressed as:
wherein the parametersIs a weight parameter.
7. A storage medium storing a computer program therein, characterized in that: the computer program, when read by a processor, performs the method of any of claims 1 to 6.
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