CN117973988A - Multi-target logistics path optimization method and system considering carbon emission - Google Patents
Multi-target logistics path optimization method and system considering carbon emission Download PDFInfo
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Abstract
The invention relates to the technical field of logistics distribution path optimization, in particular to a multi-objective logistics path optimization method and system considering carbon emission, comprising the following steps: acquiring distribution points and demand information of clients; constructing a multi-objective logistics path optimization model consisting of a transportation and distribution cost target, a customer satisfaction target and a carbon emission target; determining constraint conditions such as distance between clients, client demand, vehicle quantity, maximum cargo carrying capacity of a bicycle, time window and the like; and improving the ant colony algorithm from the state transition rule and the pheromone updating mechanism, solving the multi-objective logistics path optimization model, and determining the optimal distribution path. The method combines the traditional cold chain logistics with low carbon requirements, increases carbon tax to control carbon emission, realizes low carbon emission, introduces soft time window constraint conditions for customer satisfaction conversion, improves the customer satisfaction degree, reduces logistics distribution cost, and can realize economic benefit and sustainable development of society.
Description
Technical Field
The invention relates to the technical field of logistics distribution path optimization, in particular to a multi-objective logistics path optimization method and system considering carbon emission.
Background
In recent years, with the increasing pursuit of high-quality life and the acceleration of the pace of life, more people select to purchase fresh agricultural products online. The increasing demand is both an opportunity and a challenge for cold chain logistics. The distribution is the most important link in the cold chain logistics transportation process, and fresh agricultural products have the characteristics of easy corrosion, easy rot, short quality guarantee period and the like, so that the fresh agricultural products are more difficult to store in the transportation link. At present, the main problems in the cold chain logistics industry in China are expressed in the following aspects: the freight transportation cost is high, the freight can not be delivered on time, the real-time supervision of the freight is difficult, the information is distorted, and the like. In addition, the system is limited by conditions of lack of cold chain logistics basic equipment, large investment, high technical difficulty and the like, and the satisfaction degree of customers can be directly influenced, so that the economic benefit of enterprises can be influenced. On the other hand, with the increase of national consumption level, the demands for fresh agricultural products in different places are gradually increased, and the problems of high transportation cost, perishability of fresh agricultural products, timeliness of supply and demand and the like are increasingly serious. Therefore, the multi-objective logistics path optimization method considering carbon emission can ensure the accuracy of time, improve the customer satisfaction and the credibility of enterprises, and has certain practical significance.
Disclosure of Invention
The present invention has been made in view of the above-described problems.
Accordingly, the present invention solves the problems of: how to better realize the optimization of the cold chain logistics distribution path, and how to improve the user satisfaction degree and reduce the carbon emission and simultaneously reduce the logistics distribution cost while considering enterprise distribution cost, customer satisfaction degree and carbon emission cost.
In order to solve the technical problems, the invention provides the following technical scheme: a multi-objective logistics path optimization method considering carbon emission comprises the steps of obtaining demand information of a distribution center and each customer point; the demand information comprises position coordinates of demand points of all clients, demand quantity, a specified time window, an acceptable time window, service time, distribution time of a distribution center and vehicle configuration parameters of a cold chain vehicle; constructing a transportation and distribution cost target according to the vehicle transportation cost, the vehicle refrigeration cost and the cargo loss cost, constructing a customer satisfaction target according to a time window function, constructing a carbon emission target according to the carbon emission cost, and constructing a multi-target logistics path optimization model; determining constraint conditions such as center distance between each distribution point and a client, distance between the clients, client demand, vehicle quantity, maximum cargo carrying capacity of a bicycle, time window and the like; the ant colony algorithm is improved from the state transition rule and the pheromone updating strategy, and related factors related to transportation and distribution cost, customer satisfaction and carbon emission are integrated into the pheromone updating factor; and solving the multi-objective logistics path optimization model by utilizing an improved ant colony algorithm to determine an optimal distribution path.
As a preferred embodiment of the multi-objective flow path optimizing method considering carbon emissions according to the present invention, wherein: the method comprises the steps of establishing a multi-objective logistics path optimization model, namely establishing an objective function of transportation and distribution cost by taking the total cost consisting of vehicle transportation cost, vehicle use cost and cargo loss cost as an objective;
the transportation and distribution cost is as follows: c p＝C1+C2+C3+C4
The vehicle transportation cost is divided into fixed cost and vehicle running variable cost;
The fixed cost is expressed as:
Wherein f represents the fixed cost of the vehicle, q represents the upper limit of the number of cold chain vehicles, h represents the number of the vehicles, X h is a decision variable, represents the delivery point to the client point of the vehicles, and defaults to 1;
The running change cost is expressed as:
wherein c represents the vehicle unit transportation variation cost, q represents the number of cold chain vehicles, h represents the vehicle number, For decision variables, the h vehicle is represented from customer point i to customer point j, and d ij represents the vehicle travel distance from customer point i to customer point j;
Refrigeration cost is divided into transportation refrigeration and unloading refrigeration cost:
The transport refrigeration cost is expressed as:
Wherein w 1 represents unit refrigeration power in the transportation process, C f represents unit refrigeration energy consumption cost, Representing the load of the h vehicle from customer point i to customer point j,/>Representing the time of vehicle h to customer point j,/>Representing the time of the vehicle h to the customer point i;
The unloading refrigeration cost is expressed as:
Where w 2 denotes the unit refrigeration power during unloading, C f denotes the unit refrigeration energy consumption cost, t j denotes the unloading time of the vehicle at customer point j, Representing the shipment of the cargo at customer point j by vehicle h;
The refrigeration cost is expressed as:
According to the temperature difference of the cold chain vehicle in the transportation process and the collision of the goods loss, and the temperature difference of the vehicle in the unloading process, the goods loss rate is introduced, and the goods loss cost is divided into the goods loss in the transportation process and the goods loss in the unloading process, which can be expressed as:
Transportation loss:
where e is the price per unit of the good, Representing the delivery of the cargo at customer point j by vehicle h, b 1 representing the temperature difference during delivery and the damage caused by the collision of the damage, Q j representing the demand at customer point j,/>Time from vehicle h to customer point j is represented, t 0 represents time when the vehicle starts from the distribution center;
Unloading goods loss:
where e is the price per unit of the good, Representing the shipment of the cargo at customer point j by vehicle h, b 2 representing the rate of cargo loss during unloading of the vehicle, R j representing the demand at customer point j, t j representing the unloading time of vehicle h at customer point j;
The vehicle cargo loss cost is as follows:
wherein, C 4 is the vehicle cargo loss cost.
As a preferred embodiment of the multi-objective flow path optimizing method considering carbon emissions according to the present invention, wherein: the construction of the customer satisfaction objective by the time window function includes defining customer satisfaction through a mixed time window penalty cost, different penalty costs are generated when the vehicle is not delivered according to the optimal ideal time window, alpha 1 is the cost paid by the cold chain vehicle for waiting earlier than the ideal time window, alpha 2 is the penalty paid to the customer later than the ideal time window, and the influence of the vehicle on goods is considered to be more early than early, thus setting alpha 1＜α2, and the mixed time window penalty cost can be expressed as:
Wherein, For time penalty costs, [ E i,Li ] is the optimal service time interval desired by the customer, [ E i′,Li' ] is the time window acceptable to the user, and different penalty costs are incurred when the vehicle is not delivered in the optimal ideal time window.
As a preferred embodiment of the multi-objective flow path optimizing method considering carbon emissions according to the present invention, wherein: the carbon emission targets are expressed as:
Wherein C r is carbon tax price, E is carbon emission coefficient, d ij is vehicle driving distance from client point i to client point j, a 1 is unit distance oil consumption in vehicle driving process, a 2 is unit goods and unit time oil consumption of vehicle refrigeration equipment, Representing the load of the h vehicle from customer point i to customer point j,/>The time from customer point i to customer point j for vehicle h is indicated.
As a preferred embodiment of the multi-objective flow path optimizing method considering carbon emissions according to the present invention, wherein: the step of establishing the multi-objective logistics path optimization model comprises the following steps of establishing the multi-objective logistics path optimization model function according to the transportation distribution cost target, the customer satisfaction target and the carbon emission target:
minC(x)＝ω1Cp+ω1Ct+ω2Cc
Wherein C (x) is a total objective function, ω 1 is a cost-related parameter, ω 2 is a distance-related parameter;
The constraint conditions are as follows:
each client point is accessed once;
Each customer can only be serviced by one vehicle;
Indicating that the vehicle needs to return to the distribution center from the distribution center;
the vehicle load cannot exceed the maximum load;
the sum of the maximum loads of all vehicles in one distribution is greater than the sum of the demands of all customers;
in the primary cold chain distribution process, the total distribution distance of each cold chain vehicle does not exceed the specified maximum driving distance;
Ensuring the continuity of the time window;
Is a decision variable.
As a preferred embodiment of the multi-objective flow path optimizing method considering carbon emissions according to the present invention, wherein: the improvement of the ant colony algorithm comprises updating the pheromone value by replacing the distance in the traditional ant colony algorithm with the distribution and transportation cost, the customer satisfaction and the carbon emission, and the improved pheromone updating mechanism is expressed as:
Wherein τ ij (t) is a pheromone value from point i to point j, Δτ ij is a pheromone updating amount, ρ is a pheromone evaporation coefficient, 1- ρ is a pheromone reserve coefficient, Q is a pheromone concentration, C p,Ct,Cc is a delivery transportation cost target, a customer satisfaction target and a carbon emission target respectively, and three objective function values are subjected to homogenization treatment before calculating the pheromone updating amount in order to prevent the influence of different dimensions among the delivery transportation cost, the customer satisfaction and the carbon emission;
By utilizing the improved ant state transition rule, a time window deviation degree factor and a time window width factor are introduced, and based on a self-adaptive state transition mechanism, nodes with narrower client time windows are preferentially considered, so that the efficiency of a distribution task is improved, and the specific steps are as follows:
at time t, ant k is selectively transferred from node i to node j according to the following formula:
Wherein unpassed k represents the next node set that can be selected by ants but is not accessed, unpassed k = {0,1,2,., n-1}, α is a pheromone degree importance factor, β is a heuristic function degree of importance factor, r is a random number, r 0 is a control transfer factor, r 0 e [0,1];
dv j is a time window deviation factor, width is a time window width factor, and dv j and width are calculated as follows:
width＝Lj-Ej
Where [ E i,Li ] is the optimal service time interval desired by customer point j, and t j is the time when the vehicle arrives at customer point j.
As a preferred embodiment of the multi-objective flow path optimizing method considering carbon emissions according to the present invention, wherein: the determining of the optimal distribution path comprises setting an iteration termination condition and the ant colony number k, and setting the maximum iteration number as the iteration termination condition;
Initializing an pheromone matrix, establishing an ant colony tabu list, and storing all nodes in the tabu list;
The transition probability of the ant colony individuals is obtained through a state transition probability mechanism;
Judging whether the node selected in the third step meets constraint conditions such as customer demand, time window, vehicle load and the like, if so, tabuing the node in a tabu list, and selecting the node as the next node; if not, repeating the third step to continuously select nodes meeting the constraint conditions;
Judging whether a complete path is completed or not, checking whether the client nodes are traversed under all constraint conditions, if yes, jumping to a third step until all the client nodes meeting the conditions are traversed. If not, the complete path is considered to be completed once;
local pheromone updating, after completing a complete path, the local pheromone updating is carried out through an improved pheromone updating mechanism;
Fast non-dominant sorting, namely performing non-dominant sorting on path planning results of all ants to obtain different layering results, taking out the layer result which is the optimal solution set in the first layer, namely the pareto optimal solution set in the iteration;
taking the optimal layer result of non-dominant order as an optimal solution set to update pheromone;
updating the iteration times, wherein the iteration times are added with 1;
Judging whether the iteration number termination condition is met, if so, storing an operation result; if not, returning to the second step to continue updating iteration.
Another object of the present invention is to provide a system for optimizing a multi-objective logistic path considering carbon emissions, which can determine constraint conditions such as a center distance between each distribution point and a customer, a distance between customers, a customer demand, a number of vehicles, a maximum cargo capacity of a bicycle, a time window, etc. by constructing an objective function, solve an optimization model, determine a distribution path, reduce logistic cost, reduce carbon emission cost, improve customer satisfaction, and improve logistic distribution efficiency.
In order to solve the technical problems, the invention provides the following technical scheme: the new energy station multistage cooperative control system based on the intelligent self-adaptive algorithm comprises a demand information acquisition module, a cost target construction module, a carbon emission target construction module, a satisfaction target construction module, a multi-target optimization model construction module, an ant colony algorithm improvement module, a path solving module, a constraint condition management module, a pheromone updating and iteration module and a result output module;
the demand information acquisition module is used for acquiring demand information of the distribution center and each client point;
The cost target construction module is used for constructing a transportation distribution cost target function;
the carbon emission target construction module is used for constructing a carbon emission cost target function;
The satisfaction target construction module is used for constructing a customer satisfaction target through a time window function;
The multi-objective optimization model building module is used for building a multi-objective logistics path optimization model by combining a cost target, a carbon emission target and a satisfaction target, and determining corresponding constraint conditions;
The ant colony algorithm improvement module is used for improving the state transition rule and the pheromone updating strategy of the ant colony algorithm;
The path solving module is used for solving the multi-objective logistics path optimizing model by using the improved ant colony algorithm to determine an optimal distribution path;
the constraint condition management module is used for managing and ensuring that all distribution paths meet preset constraint conditions;
The pheromone updating and iterating module is used for updating the pheromone matrix after each iteration, carrying out non-dominant sorting, storing the pareto optimal solution set, and updating the iteration times until the iteration termination condition is met;
And the result output module is used for storing and outputting a final optimization result.
A computer device comprising a memory storing a computer program and a processor which when executed implements the steps of a multi-objective logistic path optimization method taking into account carbon emissions as described above.
A computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of a multi-objective logistic path optimization method taking into account carbon emissions as described above.
The invention has the beneficial effects that: according to the selection characteristics of ants in the ant colony algorithm, new breakthroughs are made on the aspects of state transition probability and pheromone updating of the algorithm, transportation and distribution cost, customer satisfaction and carbon emission are integrated into the core elements of the algorithm, a state transition mechanism and an pheromone updating mechanism are provided, and the selection probability and the pheromone updating are optimized to fit the provided multi-objective logistics path optimization model. Not only provides excellent distribution scheme for logistics distribution of enterprises, improves economic benefit of the enterprises, but also reduces carbon emission and realizes higher environmental benefit.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a multi-objective flow path optimization method considering carbon emissions according to a first embodiment of the present invention.
Fig. 2 is an algorithm logic diagram of a multi-objective flow path optimization method considering carbon emissions according to a first embodiment of the present invention.
Fig. 3 is a block diagram of a multi-objective flow path optimizing system considering carbon emissions according to a second embodiment of the present invention.
Fig. 4 is a distribution route diagram before optimization of a multi-objective flow path optimization method considering carbon emissions according to a third embodiment of the present invention.
Fig. 5 is an optimized distribution route diagram of a multi-objective flow path optimizing method considering carbon emissions according to a third embodiment of the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Example 1
Referring to fig. 1 and 2, a first embodiment of the present invention provides a multi-objective flow path optimization method considering carbon emissions, including:
S1: the method comprises the steps of obtaining demand information of a distribution center and each client point, and firstly, according to the demand information of the distribution center and each client point, the demand information comprises position coordinates of each client demand point, demand quantity, a specified time window, an acceptable time window and service time, the distribution time of the distribution center and the configuration parameters of a cold chain vehicle; and constructing a transportation and distribution cost target according to the vehicle transportation cost, the vehicle refrigeration cost and the cargo loss cost, constructing a customer satisfaction target according to a time window function, constructing a carbon emission target according to the carbon emission cost, and constructing a multi-target logistics path optimization model.
1) According to the transportation and distribution cost target, the customer satisfaction target and the carbon emission target, a multi-target logistics path optimization model function is established as follows:
minC(x)＝ω1Cp+ω1Ct+ω2Cc
Wherein, C (x) is a total objective function, omega 1 is a cost related parameter, omega 2 is a distance related parameter, C p is a transportation and distribution cost target, C t is a customer satisfaction target, and C c is a carbon emission target.
2) The transportation and distribution cost targets comprise vehicle transportation cost, vehicle refrigeration cost and cargo loss cost:
Cp＝C1+C2+C3+C4
Vehicle fixed cost:
Vehicle running cost:
Vehicle cooling cost:
Vehicle loss cost:
Wherein f represents a fixed cost of the vehicle, q represents an upper limit of the number of cold chain vehicles, h represents a vehicle number, X h is a decision variable, represents a delivery point to a customer point of the vehicle, defaults to 1, c represents a unit transportation variation cost of the vehicle, For decision variables, the h vehicle is represented from customer point i to customer point j, d ij represents the vehicle travel distance from customer point i to customer point j, w 1 represents the unit refrigeration power during transportation, w 2 represents the unit refrigeration power during unloading, C f represents the unit refrigeration energy consumption cost,/>Representing the load of the h vehicle from customer point i to customer point j,/>Representing the time of vehicle h to customer point j,/>Representing the time from the vehicle h to the customer point i, t j representing the unloading time of the vehicle at the customer point j, e being the price per unit of the cargo, b 1 representing the temperature difference during transportation and the damage caused by the damage collision, b 2 representing the damage rate during unloading of the vehicle, Q j representing the demand at the customer point j, and R j representing the demand at the customer point j.
3) The customer satisfaction objective is defined by a hybrid time window penalty cost, which can be expressed as:
Different penalty costs are incurred when the vehicle is not delivered in the optimal ideal time window, a 1 is the cost of waiting for the cold chain vehicle before the ideal time window, a 2 is the penalty paid to the customer later than the ideal time window, and a 1＜α2 is set considering that the effect of the vehicle on the goods is more late than early, For time penalty costs, [ E i,Li ] is the optimal service time interval desired by the customer, [ E' i,L′i ] is the time window acceptable to the user, and different penalty costs are incurred when the vehicle is not delivered in the optimal ideal time window.
4) The carbon emission target may be expressed as:
Wherein C r is carbon tax price, E is carbon emission coefficient, d ij is vehicle driving distance from client point i to client point j, alpha 1 is unit distance oil consumption in vehicle driving process, alpha 2 is unit goods and unit time oil consumption of vehicle refrigeration equipment, Representing the load of the h vehicle from customer point i to customer point j,/>The time from customer point i to customer point j for vehicle h is indicated.
S2: determining center distances between distribution points and clients, distances between the clients, client demand, vehicle quantity, maximum cargo carrying capacity of a bicycle and time window constraint conditions;
The constraint conditions are as follows:
each client point is accessed once;
Each customer can only be serviced by one vehicle;
Indicating that the vehicle needs to return to the distribution center from the distribution center;
the vehicle load cannot exceed the maximum load;
the sum of the maximum loads of all vehicles in one distribution is greater than the sum of the demands of all customers;
in the primary cold chain distribution process, the total distribution distance of each cold chain vehicle does not exceed the specified maximum driving distance;
Ensuring the continuity of the time window;
Is a decision variable.
S3: the ant colony algorithm is improved from the state transition rules and the pheromone updating strategies, and related factors related to transportation and distribution cost, customer satisfaction and carbon emission are integrated into the pheromone updating factors.
1) According to the improved ant colony algorithm from the state transition rule, introducing a time window deviation degree factor and a time window width factor, providing a self-adaptive state transition mechanism, and specifically comprising the following steps:
at time t, ant k is selectively transferred from node i to node j according to the following formula:
Wherein unpassed k represents the next node set that can be selected by the ant but has not been accessed, unpassed k = {0,1,2,..n-1 }, r is a random number, r 0 is a control transfer factor, r 0 e [0,1].
Dv j is a time window deviation factor, width is a time window width factor, and dv j and width are calculated as follows:
width＝Lj-Ej
Where [ E i,Li ] is the optimal service time interval desired by customer point j, and t j is the time when the vehicle arrives at customer point j.
2) According to the improvement of the ant colony algorithm from an improved pheromone updating mechanism, the pheromone value is updated by replacing the distance in the traditional ant colony algorithm with the distribution and transportation cost, the customer satisfaction and the carbon emission, the improved pheromone updating mechanism is as follows:
wherein τ ij (t) is a pheromone value from i point to j point, Δτ ij is a pheromone updating amount, ρ is a pheromone evaporation coefficient, 1- ρ is a pheromone reserve coefficient, C p,Ct,Cc is a delivery transportation cost target, a customer satisfaction target and a carbon emission target respectively, and in order to prevent the influence of different dimensions among the delivery transportation cost, the customer satisfaction and the carbon emission, three objective function values are subjected to homogenization treatment before calculating the pheromone updating amount.
S4: and solving the multi-objective logistics path optimization model by utilizing an improved ant colony algorithm to determine an optimal distribution path.
As shown in fig. 2, the cold chain logistics distribution path optimization algorithm based on the improved ant colony algorithm comprises the following specific steps:
1) Setting an iteration termination condition and the number k of ant colony, and setting the maximum iteration number as the iteration termination condition in the algorithm;
2) Initializing pheromone, and setting τ ij =0 (the pheromone from the point i to the point j is set to 0); secondly, an ant colony tabu list is established, all nodes are stored in the tabu list, an initial client node is randomly selected, and all client points in the tabu list are tabued, so that when goods are not distributed, the route selection mistakenly-selected node is prevented from consuming calculation time, an arrival time and departure time list is set, and time constraint judgment and transportation time calculation in the route selection process are facilitated;
3) The transition probability of the ant colony individuals is obtained through a state transition probability mechanism, and a self-adaptive state transition mechanism is designed;
when r > r 0, the transition state probability formula is as follows:
Wherein unpassed k represents the next node set that can be selected by the ant but is not accessed, unpassed k = {0,1,2,..n-1 }, r is a random number, r 0 is a control transfer factor, r 0∈[0,1],dvj is a time window deviation degree factor, and width is a time window width factor.
When r is less than or equal to r 0, the transition state probability formula is as follows:
4) Judging whether the node selected in the third step meets constraint conditions such as customer demand, time window, vehicle load and the like, and if so, tabooing the node in a taboo table. Selecting the point as the next node; if not, repeating the third step to continuously select nodes meeting the constraint conditions;
5) And judging whether the complete path is completed once. And checking whether the client nodes are traversed under all constraint conditions, if so, jumping to a third step until all the client nodes meeting the constraint conditions are traversed. If not, the complete path is considered to be completed once;
6) Local pheromone update. After completing a complete path, carrying out local pheromone updating through an improved pheromone updating mechanism;
The improved pheromone update mechanism is as follows:
wherein τ ij (t) is a pheromone value from i point to j point, Δτ ij is a pheromone updating amount, ρ is a pheromone evaporation coefficient, 1- ρ is a pheromone reserve coefficient, C p,Ct,Cc is a delivery transportation cost target, a customer satisfaction target and a carbon emission target respectively, and in order to prevent the influence of different dimensions among the delivery transportation cost, the customer satisfaction and the carbon emission, three objective function values are subjected to homogenization treatment before calculating the pheromone updating amount.
7) Fast non-dominant ordering. Non-dominant ordering is carried out on path planning results of all ants to obtain different layering results, the result of the first layer is an optimal solution set, and the result of the first layer is taken out to be the pareto optimal solution set of the iteration;
8) Taking the optimal layer result of non-dominant ordering in the seventh step as an optimal solution set to update the pheromone;
The improved pheromone update mechanism is as follows:
9) Updating the iteration times, wherein the iteration times are added with 1;
10 If the iteration number termination condition is met, storing an operation result; if not, returning to the second step to continue updating iteration.
Example 2
Referring to fig. 3, in a second embodiment of the present invention, which is different from the previous embodiment, a new energy station multi-level cooperative control system based on an intelligent adaptive algorithm is provided, including: the system comprises a demand information acquisition module, a cost target construction module, a carbon emission target construction module, a satisfaction target construction module, a multi-target optimization model construction module, an ant colony algorithm improvement module, a path solving module, a constraint condition management module, a pheromone updating and iteration module and a result output module;
the demand information acquisition module is used for acquiring demand information of the distribution center and each client point;
The cost target construction module is used for constructing a transportation distribution cost target function;
the carbon emission target construction module is used for constructing a carbon emission cost target function;
The satisfaction target construction module is used for constructing a customer satisfaction target through a time window function;
The multi-objective optimization model building module is used for building a multi-objective logistics path optimization model by combining a cost target, a carbon emission target and a satisfaction target, and determining corresponding constraint conditions;
The ant colony algorithm improvement module is used for improving the state transition rule and the pheromone updating strategy of the ant colony algorithm;
The path solving module is used for solving the multi-objective logistics path optimizing model by using the improved ant colony algorithm to determine an optimal distribution path;
the constraint condition management module is used for managing and ensuring that all distribution paths meet preset constraint conditions;
The pheromone updating and iterating module is used for updating the pheromone matrix after each iteration, carrying out non-dominant sorting, storing the pareto optimal solution set, and updating the iteration times until the iteration termination condition is met;
And the result output module is used for storing and outputting a final optimization result.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-only memory (ROM), a random access memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Example 3
Referring to fig. 4-5, a third embodiment of the present invention is shown, which differs from the first two embodiments in that: the technical effects adopted in the invention are verified and explained to verify the true effects of the method.
Taking cold chain logistics distribution of an enterprise as an example, the problem of existing persistence is solved, and the whole link of a 23 customer point distribution task of the enterprise is analyzed. The transportation products are in ice temperature category, and the temperature requirement for the refrigerated truck is-2 ℃. Assuming that the cold chain delivery vehicles of the delivery center of a certain enterprise are all of the same type, and that the vehicles are sufficient, the vehicle configuration specific parameters are shown in table 1. The client specific information is shown in table 2, and other information includes location information, the amount of demand, the time window requirement of the client, the service time of the client, and the like. The optimal model parameters in this example are shown in table 3.
TABLE 1 vehicle configuration specific parameters
The urban stores around the enterprise perform distribution service, take a distribution center as a starting point, and the distribution time is 5:00-17:00 in order to avoid the peak time period of a road section, and influence the travelling speed of the refrigerated vehicle when the refrigerated vehicle performs tasks based on the condition that traffic jam is not considered. Assuming that the external environment temperature is stabilized at 20 ℃, the internal temperature of the refrigerated compartment is at-1 ℃, the running speed of the vehicle is kept at 60km/h during the delivery task, the delivery cost of each refrigerated vehicle is 3 yuan/km, and the fixed cost of each refrigerated vehicle for delivery is 250 yuan.
Table 2 demand conditions for each store
TABLE 3 example model parameters
The parameters alpha and beta are important parameters of a path construction principle in an ant colony algorithm, and the value ranges in the existing research are usually in [1,3] and [2,5 ]; the parameter ρ is an important parameter of the pheromone updating mechanism, and the value range is [0.1,0.3].
Before simulation calculation, in order to reduce the error of a simulation result solution and ensure that the solution results of two algorithms are comparable, the same value is set for the common parameters of a basic ant colony algorithm and an improved algorithm: the maximum iteration number of the improved ant colony algorithm is 200, the number of ants is 50, the parameters of the control transfer rule factor are r 0 =0.5, the parameter combination is alpha=3, beta=5, rho=0.6, and the initial pheromone concentration Q is 100.
The basic ant colony algorithm and the improved ant colony algorithm are compared by summarizing simulation results of the two algorithms in a table in order to verify that the improved algorithm has higher reliability and faster convergence, and specific conditions are shown in fig. 4 and 5;
Based on the results of the modified ant colony algorithm and the unmodified algorithm, the optimal distribution scheme is shown in table 4 below:
Table 4 information table of algorithm optimal distribution scheme before and after improvement
The loading conditions for each vehicle for the total cost of delivery for both algorithmically solved delivery, such as whether the time window requirements have been met by the customer, are listed and analyzed for comparison by tables 5 and 6, respectively.
Table 5 optimization results of low carbon cold chain delivery based on improved ant colony algorithm
TABLE 6 optimization results of low carbon Cold chain delivery based on substantially ant colony algorithm
The improved ant algorithm and the basic ant colony algorithm solve the logistics distribution problem of an enterprise, wherein the shortest paths are 578.87km and 639.26km respectively, the minimum distribution total cost is 6753.37 yuan and 7394.75 yuan, and the minimum distribution total cost is reduced by 9.4 percent and 8.7 percent. According to the result analysis, the improved ant colony algorithm can obtain a better distribution scheme for solving the low-carbon cold chain VRP problem.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (10)
1. A multi-objective logistics path optimization method considering carbon emission is characterized in that: comprising the steps of (a) a step of,
Acquiring demand information of a distribution center and each client point;
the demand information comprises position coordinates of demand points of all clients, demand quantity, a specified time window, an acceptable time window, service time, distribution time of a distribution center and vehicle configuration parameters of a cold chain vehicle;
Constructing a transportation and distribution cost target according to the vehicle transportation cost, the vehicle refrigeration cost and the cargo loss cost, constructing a customer satisfaction target according to a time window function, constructing a carbon emission target according to the carbon emission cost, and constructing a multi-target logistics path optimization model;
Determining constraint conditions such as center distance between each distribution point and a client, distance between the clients, client demand, vehicle quantity, maximum cargo carrying capacity of a bicycle, time window and the like;
the ant colony algorithm is improved from the state transition rule and the pheromone updating strategy, and related factors related to transportation and distribution cost, customer satisfaction and carbon emission are integrated into the pheromone updating factor;
and solving the multi-objective logistics path optimization model by utilizing an improved ant colony algorithm to determine an optimal distribution path.
2. A multi-objective flow path optimization method taking carbon emissions into account as defined in claim 1, wherein: the method comprises the steps of establishing a multi-objective logistics path optimization model, namely establishing an objective function of transportation and distribution cost by taking the total cost consisting of vehicle transportation cost, vehicle use cost and cargo loss cost as an objective;
the transportation and distribution cost is as follows: c p＝C1+C2+C3+C4
The vehicle transportation cost is divided into fixed cost and vehicle running variable cost;
The fixed cost is expressed as:
Wherein f represents the fixed cost of the vehicle, q represents the upper limit of the number of cold chain vehicles, h represents the number of the vehicles, X h is a decision variable, represents the delivery point to the client point of the vehicles, and defaults to 1;
The running change cost is expressed as:
wherein c represents the vehicle unit transportation variation cost, q represents the number of cold chain vehicles, h represents the vehicle number, For decision variables, the h vehicle is represented from customer point i to customer point j, and d ij represents the vehicle travel distance from customer point i to customer point j;
Refrigeration cost is divided into transportation refrigeration and unloading refrigeration cost:
The transport refrigeration cost is expressed as:
Wherein w 1 represents unit refrigeration power in the transportation process, C f represents unit refrigeration energy consumption cost, Representing the load of the h vehicle from customer point i to customer point j,/>Representing the time of vehicle h to customer point j,/>Representing the time of the vehicle h to the customer point i;
The unloading refrigeration cost is expressed as:
Where w 2 denotes the unit refrigeration power during unloading, C f denotes the unit refrigeration energy consumption cost, t j denotes the unloading time of the vehicle at customer point j, Representing the shipment of the cargo at customer point j by vehicle h;
The refrigeration cost is expressed as:
According to the temperature difference of the cold chain vehicle in the transportation process and the collision of the goods loss, and the temperature difference of the vehicle in the unloading process, the goods loss rate is introduced, and the goods loss cost is divided into the goods loss in the transportation process and the goods loss in the unloading process, which can be expressed as:
Transportation loss:
where e is the price per unit of the good, Representing the delivery of the cargo at customer point j by vehicle h, b 1 representing the temperature difference during delivery and the damage caused by the collision of the damage, Q j representing the demand at customer point j,/>Time from vehicle h to customer point j is represented, t 0 represents time when the vehicle starts from the distribution center;
Unloading goods loss:
where e is the price per unit of the good, Representing the shipment of the cargo at customer point j by vehicle h, b 2 representing the rate of cargo loss during unloading of the vehicle, R j representing the demand at customer point j, t j representing the unloading time of vehicle h at customer point j;
The vehicle cargo loss cost is as follows:
wherein, C 4 is the vehicle cargo loss cost.
3. A multi-objective flow path optimization method taking carbon emissions into account as defined in claim 2, wherein: the construction of the customer satisfaction objective with the time window function includes defining customer satisfaction through a hybrid time window penalty cost, different penalty costs are generated when the vehicle is not delivered according to the optimal ideal time window, alpha 1 is the cost paid by the cold chain vehicle for waiting earlier than the ideal time window, alpha 2 is the penalty paid to the customer later than the ideal time window, and the influence of the vehicle on goods is considered to be more late than early, thus setting alpha 1＜α2, and the hybrid time window penalty cost is expressed as:
Wherein, For time penalty costs, [ E i,Li ] is the optimal service time interval desired by the customer, [ E' i,L′i ] is the time window acceptable to the user, and different penalty costs are incurred when the vehicle is not delivered in the optimal ideal time window.
4. A multi-objective flow path optimization method taking into account carbon emissions as defined in claim 3, wherein: the carbon emission targets are expressed as:
Wherein C r is carbon tax price, E is carbon emission coefficient, d ij is vehicle driving distance from client point i to client point j, a 1 is unit distance oil consumption in vehicle driving process, a 2 is unit goods and unit time oil consumption of vehicle refrigeration equipment, Representing the load of the h vehicle from customer point i to customer point j,/>The time from customer point i to customer point j for vehicle h is indicated.
5. A multi-objective flow path optimization method taking carbon emissions into account as defined in claim 4, wherein: the step of establishing the multi-objective logistics path optimization model comprises the following steps of establishing the multi-objective logistics path optimization model function according to the transportation distribution cost target, the customer satisfaction target and the carbon emission target:
minC(x)＝ω1Cp+ω1Ct+ω2Cc
Wherein C (x) is a total objective function, ω 1 is a cost-related parameter, ω 2 is a distance-related parameter;
The constraint conditions are as follows:
each client point is accessed once;
Each customer can only be serviced by one vehicle;
Indicating that the vehicle needs to return to the distribution center from the distribution center;
the vehicle load cannot exceed the maximum load;
the sum of the maximum loads of all vehicles in one distribution is greater than the sum of the demands of all customers;
in the primary cold chain distribution process, the total distribution distance of each cold chain vehicle does not exceed the specified maximum driving distance;
Ensuring the continuity of the time window;
Is a decision variable.
6. A multi-objective flow path optimization method taking carbon emissions into account as defined in claim 5, wherein: the improvement of the ant colony algorithm comprises updating the pheromone value by replacing the distance in the traditional ant colony algorithm with the distribution and transportation cost, the customer satisfaction and the carbon emission, and the improved pheromone updating mechanism is expressed as:
Wherein τ ij (t) is a pheromone value from point i to point j, Δτ ij is a pheromone updating amount, ρ is a pheromone evaporation coefficient, 1- ρ is a pheromone reserve coefficient, Q is a pheromone concentration, C p,Ct,Cc is a delivery transportation cost target, a customer satisfaction target and a carbon emission target respectively, and three objective function values are subjected to homogenization treatment before calculating the pheromone updating amount in order to prevent the influence of different dimensions among the delivery transportation cost, the customer satisfaction and the carbon emission;
By utilizing the improved ant state transition rule, a time window deviation degree factor and a time window width factor are introduced, and based on a self-adaptive state transition mechanism, nodes with narrower client time windows are preferentially considered, so that the efficiency of a distribution task is improved, and the specific steps are as follows:
at time t, ant k is selectively transferred from node i to node j according to the following formula:
Wherein unpassed k represents the next selectable but not accessed node set of ants, unpassed k = {0,1,2,., n-1}, α is a pheromone degree importance factor, β is a heuristic function degree of importance factor, r is a random number, r 0 is a control transfer factor, r 0 e [0,1];
dv j is a time window deviation factor, width is a time window width factor, and dv j and width are calculated as follows:
width＝Lj-Ej
Where [ E i,Li ] is the optimal service time interval desired by customer point j, and t j is the time when the vehicle arrives at customer point j.
7. A multi-objective flow path optimization method taking carbon emissions into account as defined in claim 6, wherein: the determining of the optimal distribution path comprises setting an iteration termination condition and the ant colony number k, and setting the maximum iteration number as the iteration termination condition;
Initializing an pheromone matrix, establishing an ant colony tabu list, and storing all nodes in the tabu list;
The transition probability of the ant colony individuals is obtained through a state transition probability mechanism;
Judging whether the node selected in the third step meets constraint conditions such as customer demand, time window, vehicle load and the like, if so, tabuing the node in a tabu list, and selecting the node as the next node; if not, repeating the third step to continuously select nodes meeting the constraint conditions;
Judging whether a complete path is completed or not, checking whether the client nodes are traversed under all constraint conditions, if yes, jumping to a third step until all the client nodes meeting the conditions are traversed, and if not, considering that the complete path is completed;
local pheromone updating, after completing a complete path, the local pheromone updating is carried out through an improved pheromone updating mechanism;
Fast non-dominant sorting, namely performing non-dominant sorting on path planning results of all ants to obtain different layering results, taking out the layer result which is the optimal solution set in the first layer, namely the pareto optimal solution set in the iteration;
taking the optimal layer result of non-dominant order as an optimal solution set to update pheromone;
updating the iteration times, wherein the iteration times are added with 1;
Whether the iteration number termination condition is met or not is judged, and if so, an operation result is stored; if not, returning to the second step to continue updating iteration.
8. A system employing a multi-objective flow path optimization method taking into account carbon emissions according to any of claims 1-7, characterized by: the system comprises a demand information acquisition module, a cost target construction module, a carbon emission target construction module, a satisfaction target construction module, a multi-target optimization model construction module, an ant colony algorithm improvement module, a path solving module, a constraint condition management module, a pheromone updating and iteration module and a result output module;
the demand information acquisition module is used for acquiring demand information of the distribution center and each client point;
The cost target construction module is used for constructing a transportation distribution cost target function;
the carbon emission target construction module is used for constructing a carbon emission cost target function;
The satisfaction target construction module is used for constructing a customer satisfaction target through a time window function;
The multi-objective optimization model building module is used for building a multi-objective logistics path optimization model by combining a cost target, a carbon emission target and a satisfaction target, and determining corresponding constraint conditions;
The ant colony algorithm improvement module is used for improving the state transition rule and the pheromone updating strategy of the ant colony algorithm;
The path solving module is used for solving the multi-objective logistics path optimizing model by using the improved ant colony algorithm to determine an optimal distribution path;
the constraint condition management module is used for managing and ensuring that all distribution paths meet preset constraint conditions;
The pheromone updating and iterating module is used for updating the pheromone matrix after each iteration, carrying out non-dominant sorting, storing the pareto optimal solution set, and updating the iteration times until the iteration termination condition is met;
And the result output module is used for storing and outputting a final optimization result.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that: the processor when executing the computer program implements the steps of a multi-objective flow path optimization method taking into account carbon emissions of any one of claims 1 to 7.
10. A computer-readable storage medium having stored thereon a computer program, characterized by: the computer program when executed by a processor implements the steps of a multi-objective flow path optimization method taking into account carbon emissions as claimed in any one of claims 1 to 7.
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