CN116362651A - Multi-vehicle type cold chain vehicle goods taking path optimization method - Google Patents
Multi-vehicle type cold chain vehicle goods taking path optimization method Download PDFInfo
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Abstract
The invention relates to the field of optimization of a goods taking path of a cold chain vehicle, and discloses a path optimization method for taking goods of agricultural products of the first kilometer by adopting various vehicle types, which comprises the following steps: providing a commodity taking mode of a multi-vehicle type vehicle with a first kilometer agricultural product cold chain, and determining problem constraint conditions; acquiring position coordinates, demand, service time window and service time data of the agricultural product producing place customer points; constructing a multi-vehicle type cold chain vehicle goods taking path optimization model by taking the minimum sum of the fixed cost, the transportation cost and the refrigeration cost of the goods taking vehicle and the movable refrigerator as a target; and designing a variable neighborhood-genetic hybrid algorithm to solve a multi-vehicle-type cold chain vehicle pickup path optimization model to obtain a vehicle optimal path solution.
Description
Technical Field
The invention relates to the field of vehicle pickup path optimization, in particular to a multi-vehicle type cold chain vehicle pickup path optimization method.
Background
The agricultural product 'first kilometer' refers to a series of activities such as pre-cooling, grading, processing, packaging, storage and the like, which are performed for maintaining the quality of the agricultural product and prolonging the shelf life after the agricultural product is picked from the production place until the agricultural product is transported by a main logistics. The first kilometer is the starting point of agricultural product circulation, and the first kilometer is opened to reduce the commodity loss rate of agricultural product circulation, expand high-quality market supply, accelerate agricultural product circulation and improve agricultural comprehensive benefits.
However, in the current agricultural product circulation link, agricultural products are generally transported directly from a small vehicle to a pre-freezer at the production site, and due to capacity limitations, the vehicle needs to constantly travel between the farmer and the pre-freezer, which is a relatively high overall cost of operation.
Disclosure of Invention
The invention aims to: the invention aims to provide a multi-vehicle-type cold chain vehicle pickup path optimization method, which breaks through the existing research on the cold chain logistics 'first kilometer' vehicle pickup path, generally focuses on the research embarrassment of single-vehicle-type vehicles, and proposes a mode for considering multi-type vehicle combined pickup. And constructing a multi-vehicle-type vehicle goods taking path optimization model by taking the lowest total operation cost as an objective function, designing a variable neighborhood-genetic hybrid algorithm to solve the multi-vehicle-type cold chain vehicle goods taking path optimization model, obtaining a vehicle optimal path solution, and providing a new vehicle path problem research direction for the circulation of agricultural products of 'first kilometer'.
The technical scheme is as follows: the invention discloses a multi-vehicle type cold chain vehicle goods taking path optimization method, which comprises the following steps:
s1, setting constraint conditions: in the multi-vehicle type cold chain vehicle pickup path problem, two types of cold chain vehicles are arranged in total: pick-up vehicles and mobile freezers. The goods taking vehicle is a small cold chain transport vehicle, can pass through a narrow road and is used for serving clients; the movable refrigerator is a large cold chain transport vehicle, generally runs on a wide road, and can meet the goods taking vehicle to receive the goods collected by the goods taking vehicle, so that the goods taking vehicle has capacity to continue to serve the rest customer points. The following constraint limits are set: the client point is served by a pick-up truck; the mobile refrigerator does not directly serve customer points, and a truck is taken to transfer all cargoes on the truck to the mobile refrigerator when cargoes are transferred each time; the maximum capacity limit of the goods taking vehicle and the movable refrigerator cannot be exceeded;
s2, acquiring position coordinates, demand quantity, service time window and service time data of the agricultural product producing place customer points;
s3, constructing a multi-vehicle type cold chain vehicle goods taking path optimization model by taking the sum of the fixed cost, the transportation cost and the refrigeration cost of the goods taking vehicle and the movable refrigerator as a target;
s4, combining the advantages of the genetic algorithm and the variable neighborhood search algorithm, designing a variable neighborhood-genetic hybrid algorithm to solve a multi-vehicle type cold chain vehicle goods taking path optimization model based on the traditional genetic algorithm and the variable neighborhood algorithm, and obtaining a vehicle optimal path solution.
As a preferable scheme of the invention, the specific process of the step 1 is as follows:
the pick-up truck starts from the refrigerated warehouse and meets the customer point requirements within a given service time window. The pick-up truck has a capacity limit and after the capacity limit is reached, the truck is unable to continue to service the remaining customer points. The movable refrigerator starts from the refrigeration warehouse, a customer point of the goods taking vehicle, which needs to be served, is selected as a meeting point with the goods taking vehicle, goods taken by the goods taking vehicle are accepted in the service process of the goods taking vehicle, and the goods can be sent back to the refrigeration warehouse in advance, so that the goods taking vehicle has capacity to continue to serve the rest customer points.
As a preferable scheme of the invention, before constructing a multi-vehicle type cold chain vehicle pickup path optimization model, the following assumption is made:
1) Each client point has a hard service time window, and the pick-up vehicle must arrive at the client point in the service time window and serve the client, otherwise, the client point cannot be served;
2) Each client point can only be served by the picking truck once, and the client point requirement can be met by one-time service;
3) The traffic condition during delivery is good, and the vehicle runs at a constant speed;
4) All get the freight train and be the same motorcycle type, all remove the freezer and be the same motorcycle type.
As a preferable scheme of the invention, the specific process of the step 3 is as follows:
defining a symbology:
let G= (N, L) beAgricultural product cold chain transport network, n=n C U {0} represents a set of network nodes, where N C = {1, 2..n } represents n sets of customer points, {0, n+1} is a cold storage warehouse, and the transfer of goods between the mobile freezer and pick-up truck occurs at a subset of customer points
Arc set l= { (i, j): i, j ε N, i+.j }, any one arc (i, j) ∈L corresponds to a travel distance d ij Travel time t ij . The maximum use number of the goods taking vehicle and the movable refrigerator in the main warehouse is m respectively tv 、m md 。K tv ＝{1,2,...,k tv The pick-up truck set from the main warehouse and pick-up truck k tv ∈K tv The device has the advantages of maximum load limit, fixed use cost, transportation cost and refrigeration cost; k (K) md ＝{1,2,...,k md The letter "indicates the set of mobile coolers, mobile cooler k, starting from the main warehouse md ∈K md The device also has the advantages of maximum load, fixed use cost, transportation cost and refrigeration cost; and move freezer k md ∈K md Is greater than the maximum load of the truck tv ∈K tv . Each client point i e N C Has a certain goods taking requirement u i And a service time window [ e ] i ,l i ]. The movable refrigerator and the pick-up vehicle are in a meeting point +.>
And carrying out cargo transfer and stopping until leaving after the cargo transfer is finished, wherein the cargo transfer time tau is determined and known.
N node set
N C Client point set
K md Mobile refrigerator set
K tv Collecting truck sets
i, j customer points, i, j e N C
k tv Pick-up vehicle, k tv ∈K tv
k md Movable refrigerator k md ∈K md
p customer points where the transfer of goods occurs,
d ij The distance between nodes i, j, i, j e N
s 0 Time for picking up goods of unit quantity of vehicle at customer point service
q i Demand point i epsilon N C Is required of (a)
s i The movable refrigerator is at the demand point i epsilon N C Service time s of (2) i ＝s 0 *q i
m tv Maximum number of use of pick-up vehicles in primary warehouse
m md Maximum usage number of mobile refrigerator in main warehouse
fd md Fixed cost of movable refrigerator
fd tv Fixed cost of truck
[e i ,l i ]Service time window of client point i, i e N C
v md Speed of mobile refrigerator
v tv Speed of truck
CD md Maximum load of movable refrigerator
CD tv Taking the maximum load of the truck
Time required for tau-moving refrigerator and goods taking truck to transfer goods each time
M is an infinite number
Decision variables are as follows:
x ij binary decision variable, x ij =1 means that the pick-up truck passes through an arc (i, j) i, j e N, otherwise 0
y ij Binary decision variable, y ij =1 means moving the refrigerator through an arc (i, j) i, j e N, otherwise 0
p i Binary variable p i When=1, the expression is at client point i∈n C A transfer of goods takes place, otherwise 0
The fixed cost comprises the daily maintenance, depreciation cost, driver and other manual cost of the vehicle and the refrigerator, is directly proportional to the number of the use in the delivery link, and is irrelevant to the transportation distance and the transportation time of the vehicle:
fixed cost of mobile refrigerator:
fixed cost of truck taking:
the transportation cost refers to energy consumption cost and maintenance cost generated during transportation, and is linearly and positively related to mileage of a vehicle:
mobile refrigerator transportation cost:
the transportation cost of the truck is:
the refrigeration cost is divided into two parts: firstly, in the running process of a vehicle, energy consumption is caused by heat conduction caused by temperature difference between the inside and the outside of a carriage in a door closing state, and refrigeration cost is generated; and secondly, when the goods taking vehicle service customer and the goods taking vehicle transfer goods with the mobile refrigerator, the external air and water vapor invade the vehicle after the vehicle door is opened, so that the refrigeration cost is generated.
The refrigeration cost when the vehicle door is closed in the transportation and running process of the truck is taken:
after the truck vehicle reaches the demand point, the door is opened to carry out cargo handling service, and the refrigeration cost is reduced when the demand point and the mobile refrigerator are in cargo transfer:
refrigeration cost when the door is closed in the running process of the mobile refrigerator:
refrigeration cost when moving freezer and getting goods vehicle and taking place the goods transfer at the demand point:
thus, the total cost is calculated as follows:
the method comprises the steps of establishing a multi-vehicle type cold chain vehicle pickup path optimization model by taking the minimum total cost as a target, wherein the specific model is as follows:
S.t
p i ∈{0，1}i∈N C (24)
the model aims at minimizing the sum of fixed cost, transportation cost and refrigeration cost of the goods taking vehicle and the mobile refrigerator. Constraint (1) ensures that each point of demand is serviced by the pick-up truck. Constraint (2) ensures that the mobile refrigerator can reach the point of demand for goods transfer. Constraints (3) and (4) ensure that the pick-up vehicle and the mobile refrigerator will also leave the point of demand after reaching the point of demand. Constraints (5) and (6) ensure that the number of pick-up vehicles and mobile freezer vehicles exiting the refrigerated warehouse is less than the maximum number of available vehicles. Constraint (7) ensures that pick-up vehicles arrive within the point-of-demand service time window. Constraints (8) and (9) ensure that the two vehicles are synchronized for cargo transfer by making the pick-up vehicle and the mobile cooler leave the meeting point at equal times. The constraints (10) determine the load range of the mobile vehicle. Constraints (11) - (12) define binary decision variables.
As a preferred scheme of the present invention, the specific process of the step 4 is as follows:
and acquiring position coordinates, demand, service time window and service time data of the client point.
As a preferred scheme of the present invention, the specific process of the step 4 is as follows:
step 41, performing integer coding, and adopting a double-layer coding mode. In the first layer of codes, the number 0 represents a distribution center, 1, … and n represents customer points, wherein the maximum number of goods taking vehicles which can be used by the distribution center is m (m < n), so that the number of path information pieces which can be stored in the first layer of codes of the chromosome is less than or equal to m, and the code length=the number of customer points+the maximum number of vehicles used is-1 (n+m-1). The length of the second layer chromosome code is the same as that of the first layer chromosome, the maximum number of mobile refrigerators which can be used by the distribution center is k, the numbers are 1, … and k, the serial numbers of the mobile refrigerators are randomly distributed to clients in the first layer chromosome code, and the number 0 indicates that no mobile refrigerator reaches the client point. And randomly generating an initial population to obtain an initial solution.
Step 42, constructing a fitness function, and calculating fitness of individuals in the initial population; the fitness function is constructed by considering time constraint and capacity constraint in the mathematical model, so that the fitness in the algorithm is matched with the fitness in the mathematical model.
The invention adopts punishment function to process constraint conditions, and the basic idea is that: when calculating fitness function values of individuals of infeasible solutions in a chromosome population, the individual penalty function is given to reduce its fitness function value and the probability of inheritance to the next generation, thereby automatically eliminating the individual.
Fitness function of individual x:
wherein f (x) is an fitness function value; TC (x) is the objective function value; p (i) is a penalty function,
p is a penalty coefficient, which is a very large positive value.
Step 43, selecting individuals according to a roulette selection method, wherein the specific steps are as follows:
the fitness value of the ith individual is f (x) i ) The probability that the ith individual is selected is given by:
step 44, acting the crossover operator and the mutation operator on the selected individuals according to a certain occurrence probability, and combining the filial generation after crossover mutation with the first generation population;
step 45, carrying out neighborhood changing operation on the individuals of the combined population according to a certain occurrence probability, and selecting the individuals with better preference to obtain a second generation population;
and step 46, determining that the iteration times meet the requirements, and outputting an optimal path solution.
As a preferred embodiment of the present invention, the specific process of the step 44 is as follows:
single point crossover operator: a crossover point is randomly placed in the encoded individual, and then the partial chromosomes of the two paired individuals are interchanged at this point.
Basic position variationOperators: with variation probability P in chromosome coding string m Randomly assigning one or more gene values to perform mutation operation;
uniform mutation operator: and replacing one or a plurality of gene values in the chromosome coding string by random numbers which are uniformly distributed within a certain range and have mutation probability.
As a preferred embodiment of the present invention, the specific process of step 45 is as follows:
reversing the structure operation: randomly selecting two position points, and arranging elements between the two positions in an inverted order;
switching fabric operation: randomly selecting two position points in the current solution, and exchanging elements on the two positions;
the operation of the insertion structure: two location points are randomly selected and then the element at the first location is inserted behind the second element.
Compared with the prior art, the technical scheme provided by the invention has the following technical effects:
1. according to the invention, a multi-vehicle type vehicle goods taking mode is adopted in the first kilometer of agricultural products, and the goods taking path of the multi-vehicle type vehicle is optimized, so that the operation cost of cold chain logistics can be effectively reduced, the circulation efficiency of the agricultural products is improved, and a deep foundation is laid for establishing a high-efficiency and perfect cold chain logistics system.
2. The method of the invention considers the fixed cost and the transportation cost of the goods taking vehicle and the movable refrigerator, and also considers the refrigeration cost of the transportation, loading and unloading and carrying links, thereby being more close to the actual situation of cold chain logistics operation.
3. The invention provides that a small-sized goods taking vehicle and a large-sized mobile refrigerator are simultaneously used in the first kilometer of agricultural products, and the small-sized goods taking vehicle is used for driving on narrow rural roads and serves farmer points. The large-sized movable refrigerator has a larger load, can be used as a movable warehouse, and can be used for receiving and collecting agricultural products accumulated and collected by a truck on a wide road and conveying the agricultural products to a pre-refrigeration house, so that the truck can be conveniently taken out, and the truck has capacity to continue to serve farmers.
Drawings
FIG. 1 is a flow chart of a method for optimizing a pickup path of a multi-model vehicle for the first kilometer of agricultural products according to the present invention;
fig. 2 is a schematic view of the pick-up path of the pick-up vehicle and mobile cooler of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings.
As shown in fig. 1, the invention relates to a multi-vehicle type cold chain vehicle pickup path optimization method, which comprises the following steps:
(1) The invention provides an optimization problem of a goods taking path of a multi-vehicle type cold chain vehicle for the first kilometer of agricultural products, and aims to reduce the operation cost of the cold chain logistics on the premise of meeting the demand of customers of the agricultural product producing place, wherein the total cost comprises the fixed cost, the transportation cost and the refrigeration cost of the goods taking vehicle and a mobile refrigerator.
In this problem, the pick-up vehicle starts from a refrigerated warehouse and meets customer point requirements within a given service time window. The pick-up truck has a capacity limit and after the capacity limit is reached, the truck is unable to continue to service the remaining customer points. The movable refrigerator starts from the refrigeration warehouse, a customer point of the goods taking vehicle, which needs to be served, is selected as a meeting point with the goods taking vehicle, goods taken by the goods taking vehicle are accepted in the service process of the goods taking vehicle, and the goods can be sent back to the refrigeration warehouse in advance, so that the goods taking vehicle has capacity to continue to serve the rest customer points.
As shown in fig. 2, a schematic diagram of a problem description for 18 client points is shown. (1) The number taking truck serves two demand points (including demand point 1) from the refrigeration warehouse, goods exchange occurs between the demand point 1 and a number 1 mobile refrigerator which starts from the refrigeration warehouse, all goods on the truck are delivered, and then the number 1 taking truck returns to the refrigeration warehouse after three demand points remain on a service route of the number 1 taking truck. (2) The number taking truck starts from the refrigeration warehouse and serves three demand points (comprising demand point 2), and goods exchange occurs between the demand point 2 and the number 1 mobile refrigerator starting from the demand point 1, so that all goods on the truck are delivered. And (2) returning the goods after three remaining demand points on the service route of the truck to the refrigeration warehouse, wherein the mobile refrigerator 1 returns to the refrigeration warehouse with the goods after the goods are transferred at the demand point 2. And the same is true. The No. 2 mobile refrigerator sequentially receives the goods of the (3) goods taking vehicle at the demand point 3 and the demand point 4, then returns to the refrigeration warehouse, and then the goods collected after the demand point 4 are sent back to the refrigeration warehouse by the (3) goods taking vehicle.
The problem has the following constraint limitations: the client point is served by a pick-up truck; the mobile refrigerator does not directly serve customer points, and a truck is taken to transfer all cargoes on the truck to the mobile refrigerator when cargoes are transferred each time; the maximum capacity limit of the pick-up vehicle and the mobile freezer cannot be exceeded.
And make the following assumptions about the problem:
1) Each client point has a hard service time window, and the pick-up vehicle must arrive at the client point in the service time window and serve the client, otherwise, the client point cannot be served;
2) Each client point can only be served by the picking truck once, and the client point requirement can be met by one-time service;
3) The traffic condition during delivery is good, and the vehicle runs at a constant speed;
4) All get the freight train and be the same motorcycle type, all remove the freezer and be the same motorcycle type.
(2) Acquiring position coordinates, demand, service time window and service time data of the agricultural product producing place customer points;
(3) Constructing a multi-vehicle type cold chain vehicle goods taking path optimization model by taking the minimum sum of the fixed cost, the transportation cost and the refrigeration cost of the goods taking vehicle and the movable refrigerator as a target;
the method comprises the following specific steps:
defining a symbology:
let g= (N, L) be a cold chain transport network of agricultural products, n=n C U {0} represents a set of network nodes, where N C = {1, 2..n } represents n sets of customer points, {0, n+1} is a cold storage warehouse, and the transfer of goods between the mobile freezer and pick-up truck occurs at a subset of customer points
Arc set l= { (i, j): i, j εN, i+.j, any one arc (i, j) epsilon L corresponds to a driving distance d ij Travel time t ij . The maximum use number of the goods taking vehicle and the movable refrigerator in the main warehouse is m respectively tv 、m md 。K tv ＝{1,2,...,k tv -representing a collection of pick-up vehicles, vehicle k, starting from a main warehouse tv ∈K tv With a maximum load limit; k (K) md ＝{1,2,...,k md -representing a set of mobile coolers starting from a main warehouse, vehicle k md ∈K md The device also has the advantages of maximum load, fixed use cost, transportation cost and refrigeration cost; and move freezer k md ∈K md Is greater than the maximum load of the truck tv ∈K tv . Each client point i e N C Has a certain goods taking requirement u i And a service time window [ e ] i ,l i ]. The movable refrigerator and the pick-up vehicle are in a meeting point +.>
And carrying out cargo transfer and stopping until leaving after the cargo transfer is finished, wherein the cargo transfer time tau is determined and known.
N node set
N C Client point set
K md Mobile refrigerator set
K tv Collecting truck sets
i, j customer points, i, j e N C
k tv Pick-up vehicle, k tv ∈K tv
k md Movable refrigerator k md ∈K md
p customer points where the transfer of goods occurs,
d ij The distance between nodes i, j, i, j e N
s 0 Time for picking up goods of unit quantity of vehicle at customer point service
q i Demand point i epsilon N C Is required of (a)
s i The movable refrigerator is at the demand point i epsilon N C Service time s of (2) i ＝s 0 *q i
m tv Maximum number of use of pick-up vehicles in primary warehouse
m md Maximum usage number of mobile refrigerator in main warehouse
fd md Fixed cost of movable refrigerator
fd tv Fixed cost of truck
[e i ,l i ]Service time window of client point i, i e N C
v md Speed of mobile refrigerator
v tv Speed of truck
CD md Maximum load of movable refrigerator
CD tv Taking the maximum load of the truck
Time required for tau-moving refrigerator and goods taking truck to transfer goods each time
M is an infinite number
Decision variables are as follows:
x ij binary decision variable, x ij =1 means that the pick-up truck passes through an arc (i, j) i, j e N, otherwise 0
y ij Binary decision variable, y ij =1 means moving the refrigerator through an arc (i, j) i, j e N, otherwise 0
p i Binary variable p i When=1, the expression is at client point i∈n C A transfer of goods takes place, otherwise 0
The fixed cost comprises the daily maintenance, depreciation cost, driver and other manual cost of the vehicle and the refrigerator, is directly proportional to the number of the use in the delivery link, and is irrelevant to the transportation distance and the transportation time of the vehicle:
fixed cost of mobile refrigerator:
fixed cost of truck taking:
the transportation cost refers to energy consumption cost and maintenance cost generated during transportation, and is linearly and positively related to mileage of a vehicle:
mobile refrigerator transportation cost:
the transportation cost of the truck is:
the refrigeration cost is divided into two parts: firstly, in the running process of a vehicle, energy consumption is caused by heat conduction caused by temperature difference between the inside and the outside of a carriage in a door closing state, and refrigeration cost is generated; and secondly, when the goods taking vehicle service customer and the goods taking vehicle transfer goods with the mobile refrigerator, the external air and water vapor invade the vehicle after the vehicle door is opened, so that the refrigeration cost is generated.
The refrigeration cost when the vehicle door is closed in the transportation and running process of the truck is taken:
after the truck vehicle reaches the demand point, the door is opened to carry out cargo handling service, and the refrigeration cost is reduced when the demand point and the mobile refrigerator are in cargo transfer:
refrigeration cost when the door is closed in the running process of the mobile refrigerator:
refrigeration cost when moving freezer and getting goods vehicle and taking place the goods transfer at the demand point:
thus, the total cost is calculated as follows:
the method comprises the steps of establishing a multi-vehicle type cold chain vehicle pickup path optimization model by taking the minimum total cost as a target, wherein the specific model is as follows:
S.t
p i ∈{0，1}i∈N C (12)
the model aims at minimizing the sum of fixed cost, transportation cost and refrigeration cost of the goods taking vehicle and the mobile refrigerator. Constraint (1) ensures that each point of demand is serviced by the pick-up truck. Constraint (2) ensures that the mobile refrigerator can reach the point of demand for goods transfer. Constraints (3) and (4) ensure that the pick-up vehicle and the mobile refrigerator will also leave the point of demand after reaching the point of demand. Constraints (5) and (6) ensure that the number of pick-up vehicles and mobile freezer vehicles exiting the refrigerated warehouse is less than the maximum number of available vehicles. Constraint (7) ensures that pick-up vehicles arrive within the point-of-demand service time window. Constraints (8) and (9) ensure that the two vehicles are synchronized for cargo transfer by making the pick-up vehicle and the mobile cooler leave the meeting point at equal times. The constraints (10) determine the load range of the mobile vehicle. Constraints (11) - (12) define binary decision variables.
(4) Solving a multi-vehicle type cold chain vehicle pickup path optimization model through a variable neighborhood-genetic hybrid algorithm to obtain a vehicle optimal path solution. The method comprises the following specific steps:
step 41, performing integer coding, and adopting a double-layer coding mode. In the first layer of codes, the number 0 represents a distribution center, 1, … and n represents customer points, wherein the maximum number of goods taking vehicles which can be used by the distribution center is m (m < n), so that the number of path information pieces which can be stored in the first layer of codes of the chromosome is less than or equal to m, and the code length=the number of customer points+the maximum number of vehicles used is-1 (n+m-1). The length of the second layer chromosome code is the same as that of the first layer chromosome, the maximum number of mobile refrigerators which can be used by the distribution center is k, the numbers are 1, … and k, the serial numbers of the mobile refrigerators are randomly distributed to clients in the first layer chromosome code, and the number 0 indicates that no mobile refrigerator reaches the client point. And randomly generating an initial population to obtain an initial solution.
Step 42, constructing a fitness function, and calculating fitness of individuals in the initial population;
step 43, selecting individuals according to a roulette selection method to obtain offspring populations;
step 44, acting the crossover operator and the mutation operator on the selected individuals according to a certain occurrence probability, and combining the filial generation after crossover mutation with the first generation population;
step 45, carrying out neighborhood changing operation on the individuals of the combined population according to a certain occurrence probability, and selecting the individuals with better preference to obtain a second generation population;
and step 46, determining that the iteration times meet the requirements, and outputting an optimal path solution.
In the embodiment of the invention, a multi-vehicle type cold chain vehicle path optimization model is solved through a variable neighborhood-genetic hybrid algorithm to obtain an optimal path solution, which is specifically as follows:
taking the vehicle path problem of 20 customers as an example, the present invention is analyzed in detail. First, the following information is collected: the position coordinates of the warehouse, the position coordinates of clients, the demand quantity, the service time window, the speed, the maximum load, the cost and other information of two types of vehicles.
Designing a representation structure of a variable neighborhood-genetic algorithm, wherein a first layer of chromosome codes comprise clients needing service and used goods taking vehicles, a second layer of chromosome codes comprise used mobile refrigerators, and an optimal path solution is obtained through selection, intersection, variation and variable neighborhood operation, and the maximum iteration number of the algorithm is 1000. The results indicate that there are three pick-up vehicles, 1 mobile freezer serving 20 customer points, wherein the mobile freezer meets pick-up vehicle 2 at customer point 12 and receives all of the goods collected by the pick-up vehicle, allowing the pick-up vehicle capacity to continue to serve the customer point.
TABLE 1 Multi-model Cold chain vehicle optimal Path solution for 20 customer points
Note that: 1, 2..the term "19, 20 represents a customer point; 0 represents a warehouse.
While the foregoing has been described in relation to illustrative embodiments thereof, so as to facilitate the understanding of the present invention by those skilled in the art, it should be understood that the present invention is not limited to the scope of the embodiments, but is to be construed as limited to the spirit and scope of the invention as defined and defined by the appended claims, as long as various changes are apparent to those skilled in the art, all within the scope of which the invention is defined by the appended claims.
Claims (8)
1. The optimizing method for the goods taking path of the multi-vehicle type cold chain vehicle is characterized by comprising the following steps of:
s1, presetting constraint conditions in the problem of a goods taking path of a multi-vehicle type cold chain vehicle, and presetting two types of cold chain vehicles: a pick-up vehicle and a mobile refrigerator; the goods taking vehicle is a small cold chain transport vehicle and directly serves a customer point of an agricultural product producing place; the movable refrigerator is a large-sized cold chain transport vehicle and is used for receiving cargoes collected by the goods taking vehicle and transporting the cargoes to the refrigerator;
s2, acquiring the position coordinates, the demand, the service time window and the service time of the agricultural product producing place client point;
s3, constructing a multi-vehicle type cold chain vehicle goods taking path optimization model by taking the minimum sum of the fixed cost, the transportation cost and the refrigeration cost of a truck and a movable refrigerator as a target based on the data of the step S2;
s4, designing a variable neighborhood-genetic hybrid algorithm to solve a multi-vehicle type cold chain vehicle goods taking path optimization model based on a genetic algorithm and a variable neighborhood search algorithm, and obtaining a vehicle optimal path solution.
2. The optimization method of the picking path of the multi-vehicle type cold chain vehicle according to claim 1, wherein the step 1 is specifically: the truck taking vehicle starts from the refrigeration warehouse and meets the requirements of customer points in a given service time window; the goods taking vehicle has capacity limitation, and after the capacity limitation is reached, the goods taking vehicle and the mobile refrigerator are subjected to goods delivery; the movable refrigerator starts from the refrigeration warehouse, a customer point of the goods taking vehicle, which needs to be served, is selected as a meeting point with the goods taking vehicle, goods taken by the goods taking vehicle are received in the process of the service of the goods taking vehicle, and the goods are returned to the refrigeration warehouse.
3. The method for optimizing a pickup path of a multi-vehicle type cold chain vehicle according to claim 1, wherein in step S3, the constructing a model for optimizing a pickup path of a multi-vehicle type cold chain vehicle specifically includes:
defining a symbology:
let g= (N, L) be a cold chain transport network of agricultural products, n=n C U {0} represents a set of network nodes, where N C = {1, 2..n } represents n sets of customer points, {0, n+1} is a cold storage warehouse, and the transfer of goods between the mobile freezer and pick-up truck occurs at a subset of customer points
Arc set l= { (i, j): i, j ε N, i+.j }, arbitraryAn arc (i, j) epsilon L corresponds to a travel distance d ij Travel time t ij The method comprises the steps of carrying out a first treatment on the surface of the The maximum use number of the goods taking vehicle and the movable refrigerator in the main warehouse is m respectively tv 、m md ；K tv ＝{1,2,...,k tv The pick-up truck set from the main warehouse and pick-up truck k tv ∈K tv The device has the advantages of maximum load limit, fixed use cost, transportation cost and refrigeration cost; k (K) md ＝{1,2,...,k md The letter "indicates the set of mobile coolers, mobile cooler k, starting from the main warehouse md ∈K md The device also has the advantages of maximum load, fixed use cost, transportation cost and refrigeration cost; and move freezer k md ∈K md Is greater than the maximum load of the truck tv ∈K tv The method comprises the steps of carrying out a first treatment on the surface of the Each client point i e N C Has a certain goods taking requirement u i And a service time window [ e ] i ,l i ]The method comprises the steps of carrying out a first treatment on the surface of the The movable refrigerator and the pick-up vehicle are in a meeting point +.>
Carrying out cargo transfer, stopping until the cargo transfer is finished, leaving, and determining and knowing the cargo transfer time tau;
n node set
N C Client point set
K md Mobile refrigerator set
K tv Collecting truck sets
i, j customer points, i, j e N C
k tv Pick-up vehicle, k tv ∈K tv
k md Movable refrigerator k md ∈K md
p occurrenceThe customer point of the transfer of the goods,
d ij The distance between nodes i, j, i, j e N
s 0 Time for picking up goods of unit quantity of vehicle at customer point service
q i Demand point i epsilon N C Is required of (a)
s i The movable refrigerator is at the demand point i epsilon N C Service time s of (2) i ＝s 0 *q i
m tv Maximum number of use of pick-up vehicles in primary warehouse
m md Maximum usage number of mobile refrigerator in main warehouse
fd md Fixed cost of movable refrigerator
fd tv Fixed cost of truck
[e i ,l i ]Service time window of client point i, i e N C
v md Speed of mobile refrigerator
v tv Speed of truck
CD md Maximum load of movable refrigerator
CD tv Taking the maximum load of the truck
Time required for tau-moving refrigerator and goods taking truck to transfer goods each time
M is an infinite number
Decision variables are as follows:
x ij binary decision variable, x ij =1 means that the pick-up truck passes through an arc (i, j) i, j e N, otherwise 0
y ij Binary decision variable, y ij =1 means moving the refrigerator through an arc (i, j) i, j e N, otherwise 0
p i Binary variable p i When=1, the expression is at client point i∈n C A transfer of goods takes place, otherwise 0
The fixed cost comprises the daily maintenance, maintenance and depreciation cost of the vehicle and the refrigerator and the labor cost of a driver, is proportional to the number of the use in the delivery link, and is irrelevant to the transportation distance and the transportation time of the vehicle:
fixed cost of mobile refrigerator:
fixed cost of truck taking:
the transportation cost refers to energy consumption cost and maintenance cost generated during transportation, and is linearly and positively related to mileage of a vehicle:
mobile refrigerator transportation cost:
the transportation cost of the truck is:
the refrigeration cost includes two parts: firstly, in the running process of a vehicle, energy consumption is caused by heat conduction caused by temperature difference between the inside and the outside of a carriage in a door closing state, and refrigeration cost is generated; secondly, when a service customer of the goods taking vehicle and the movable refrigerator are used for transferring goods, the external air and water vapor invade the vehicle after the vehicle door is opened to generate refrigeration cost;
the refrigeration cost when the vehicle door is closed in the transportation and running process of the truck is taken:
after the truck vehicle reaches the demand point, the door is opened to carry out cargo handling service, and the refrigeration cost is reduced when the demand point and the mobile refrigerator are in cargo transfer:
refrigeration cost when the door is closed in the running process of the mobile refrigerator:
movable refrigeratorRefrigeration cost of goods taking vehicle when goods transfer occurs at demand point:
thus, the total cost is calculated as follows:
the method comprises the steps of establishing a multi-vehicle type cold chain vehicle pickup path optimization model by taking the minimum total cost as a target, wherein the specific model is as follows:
S.t
x ij ,y ij ∈{0，1}i,j∈N,i≠j (11)
p i ∈{0，1}i∈N C (12)
the model aims at minimizing the sum of the fixed cost, the transportation cost and the refrigeration cost of the goods taking vehicle and the movable refrigerator; constraint (1) ensures that each demand point is serviced by a pick-up truck; constraint (2) ensures that the movable refrigerator reaches a demand point to transfer goods; constraints (3) and (4) ensure that the pick-up vehicle and the mobile refrigerator will also leave from the point of demand after reaching the point of demand; constraints (5) and (6) ensure that the number of pick-up vehicles and mobile freezer vehicles from the refrigerated warehouse is less than the maximum number of available vehicles; constraint (7) ensures that the pick-up truck arrives within the point-of-demand service time window; constraint (8) and (9) ensure that the two vehicles have cargo transferring synchronism by making the time for the pick-up vehicle and the mobile refrigerator to leave the meeting point equal; constraint (10) determines a load range of the mobile vehicle; constraints (11) - (12) define binary decision variables.
4. The method for optimizing the picking path of the multi-vehicle type cold chain vehicle according to claim 1, wherein the step S4 is specifically:
step 41, integer coding is carried out, and a double-layer coding mode is adopted; in the first layer of codes, the number 0 represents a distribution center, 1, … and n represent customer points, wherein the maximum number of goods taking vehicles used by the distribution center is m, wherein m < n, the number of path information pieces stored in the first layer of codes of the chromosome is less than or equal to m, and the code length = customer point number + vehicle maximum use number-1 (n+m-1); the length of the second layer chromosome is the same as that of the first layer chromosome, the maximum number of the mobile refrigerators used by the distribution center is k, the numbers are 1, … and k, the serial numbers of the mobile refrigerators are randomly distributed to clients in the first layer chromosome, and the number 0 represents that no mobile refrigerator reaches the client point; randomly generating an initial population to obtain an initial solution;
step 42, constructing a fitness function, and calculating fitness of individuals in the initial population;
step 43, selecting individuals according to roulette selection;
step 44, acting the crossover operator and the mutation operator on the selected individuals according to the occurrence probability, and combining the filial generation after the crossover mutation with the first generation population;
step 45, carrying out neighborhood changing operation on the individuals of the combined population according to the occurrence probability, and selecting the individuals with better preference to obtain a second generation population;
and step 46, determining that the iteration times meet the requirements, and outputting an optimal path solution.
5. The method for optimizing a pickup path of a multi-vehicle type cold chain vehicle according to claim 4, wherein the specific process of step 42 is as follows:
constructing a fitness function to consider time constraint and capacity constraint in the mathematical model, so that the fitness in the algorithm is consistent with the fitness in the mathematical model; adopting a punishment function to process constraint conditions, and when the fitness function value of an infeasible solution individual in the chromosome population is calculated, giving the individual punishment function to reduce the fitness function value and the probability of inheritance to the next generation, so that the individual is automatically eliminated;
fitness function of individual x:
wherein f (x) is an fitness function value; TC (x) is the objective function value; p (i) is a penalty function,
p is a penalty coefficient, which is a very large positive value.
6. The method for optimizing a pickup path of a multi-vehicle type cold chain vehicle according to claim 4, wherein the specific process of step 43 is as follows:
selecting individuals according to a roulette selection method, wherein the specific steps are as follows:
the fitness value of the ith individual is f (x) i ) The probability that the ith individual is selected is given by:
7. the method for optimizing a pickup path of a multi-vehicle type cold chain vehicle according to claim 4, wherein the specific process of step 44 is as follows:
single point crossover operator: randomly setting a cross point in the coded individuals, and then exchanging partial chromosomes of two paired individuals at the cross point;
basic mutation operator: with variation probability P in chromosome coding string m Randomly assigning one or more gene values to perform mutation operation;
uniform mutation operator: and replacing one or a plurality of gene values in the chromosome coding string by random numbers which are uniformly distributed within a certain range and have mutation probability.
8. The method for optimizing a pickup path of a multi-vehicle type cold chain vehicle according to claim 4, wherein the specific process of step 45 is as follows:
reversing the structure operation: randomly selecting two position points, and arranging elements between the two positions in an inverted order;
switching fabric operation: randomly selecting two position points in the current solution, and exchanging elements on the two positions;
the operation of the insertion structure: two location points are randomly selected and then the element at the first location is inserted after the second element.
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| CN116976231A (en) * | 2023-09-25 | 2023-10-31 | 闽都创新实验室 | Genetic algorithm-based planar ground heap AGV (automated guided vehicle) management operation optimization method |
| CN117236546A (en) * | 2023-11-15 | 2023-12-15 | 四川芯元一食品科技有限公司 | Logistics distribution vehicle path optimization method |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116976231A (en) * | 2023-09-25 | 2023-10-31 | 闽都创新实验室 | Genetic algorithm-based planar ground heap AGV (automated guided vehicle) management operation optimization method |
| CN116976231B (en) * | 2023-09-25 | 2024-02-06 | 闽都创新实验室 | Genetic algorithm-based planar ground heap AGV (automated guided vehicle) management operation optimization method |
| CN117236546A (en) * | 2023-11-15 | 2023-12-15 | 四川芯元一食品科技有限公司 | Logistics distribution vehicle path optimization method |
| CN117236546B (en) * | 2023-11-15 | 2024-02-20 | 四川芯元一食品科技有限公司 | Logistics distribution vehicle path optimization method |
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